Abstract Background To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images. Results A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models. Conclusions The COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.

Abstract Background Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblastic leukemia (ALL) in microscopic images. Results A Resnet101-9 ensemble model was developed for classifying ALL in microscopic images. The proposed Resnet101-9 ensemble model combined the use of the nine trained Resnet-101 models with a majority voting strategy. Each trained Resnet-101 model integrated the well-known pre-trained Resnet-101 model and its algorithm hyperparameters by using transfer learning method to classify ALL in microscopic images. The best combination of algorithm hyperparameters for the pre-trained Resnet-101 model was determined by Taguchi experimental method. The microscopic images used for training of the pre-trained Resnet-101 model and for performance tests of the trained Resnet-101 model were obtained from the C-NMC dataset. In experimental tests of performance, the Resnet101-9 ensemble model achieved an accuracy of 85.11% and an F 1 -score of 88.94 in classifying ALL in microscopic images. The accuracy of the Resnet101-9 ensemble model was superior to that of the nine trained Resnet-101 individual models. All other performance measures (i.e., precision, recall, and specificity) for the Resnet101-9 ensemble model exceeded those for the nine trained Resnet-101 individual models. Conclusion Compared to the nine trained Resnet-101 individual models, the Resnet101-9 ensemble model had superior accuracy in classifying ALL in microscopic images obtained from the C-NMC dataset.

BACKGROUND: To diagnose key pathologies of age-related macular degeneration (AMD) and diabetic macular edema (DME) quickly and accurately, researchers attempted to develop effective artificial intelligence methods by using medical images. RESULTS: A convolutional neural network (CNN) with transfer learning capability is proposed and appropriate hyperparameters are selected for classifying optical coherence tomography (OCT) images of AMD and DME. To perform transfer learning, a pre-trained CNN model is used as the starting point for a new CNN model for solving related problems. The hyperparameters (parameters that have set values before the learning process begins) in this study were algorithm hyperparameters that affect learning speed and quality. During training, different CNN-based models require different algorithm hyperparameters (e.g., optimizer, learning rate, and mini-batch size). Experiments showed that, after transfer learning, the CNN models (8-layer Alexnet, 22-layer Googlenet, 16-layer VGG, 19-layer VGG, 18-layer Resnet, 50-layer Resnet, and a 101-layer Resnet) successfully classified OCT images of AMD and DME. CONCLUSIONS: The experimental results further showed that, after transfer learning, the VGG19, Resnet101, and Resnet50 models with appropriate algorithm hyperparameters had excellent capability and performance in classifying OCT images of AMD and DME.

BACKGROUND: Researchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem. RESULTS: The three types of blood cells are platelets, red blood cells, and white blood cells. This study used the Resnet50 network as a backbone network of the single shot detector (SSD) for automatically identifying and counting different blood cells and, meanwhile, proposed a systematic method to find a better combination of algorithm hyperparameters of the Resnet50 network for promoting accuracy for identifying and counting blood cells. The Resnet50 backbone network of the SSD with its optimized algorithm hyperparameters, which is called the Resnet50-SSD model, was developed to enhance the feature extraction ability for identifying and counting blood cells. Furthermore, the algorithm hyperparameters of Resnet50 backbone networks of the SSD were optimized by the Taguchi experimental method for promoting detection accuracy of the Resnet50-SSD model. The experimental result shows that the detection accuracy of the Resnet50-SSD model with 512 × 512 × 3 input images was better than that of the Resnet50-SSD model with 300 × 300 × 3 input images on the test set of blood cells images. Additionally, the detection accuracy of the Resnet50-SSD model using the combination of algorithm hyperparameters got by the Taguchi method was better than that of the Resnet50-SSD model using the combination of algorithm hyperparameters given by the Matlab example. CONCLUSION: In blood cell images acquired from the BCCD dataset, the proposed Resnet50-SSD model had higher accuracy in identifying and counting blood cells, especially white blood cells and red blood cells.

BACKGROUND: Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to be "suitable" only when both "efficacy" and "safety" are considered. This study presents a model, namely the "ensemble strategy model," to predict the suitability of vancomycin regimens. The experimental data consisted of 2141 "suitable" and "unsuitable" patients tagged with a vancomycin regimen, including six diagnostic input attributes (sex, age, weight, serum creatinine, dosing interval, and total daily dose), and the dataset was normalized into a training dataset, a validation dataset, and a test dataset. AdaBoost.M1, Bagging, fastAdaboost, Neyman-Pearson, and Stacking were used for model training. The "ensemble strategy concept" was then used to arrive at the final decision by voting to build a model for predicting the suitability of vancomycin treatment regimens. RESULTS: The results of the tenfold cross-validation showed that the average accuracy of the proposed "ensemble strategy model" was 86.51% with a standard deviation of 0.006, and it was robust. In addition, the experimental results of the test dataset revealed that the accuracy, sensitivity, and specificity of the proposed method were 87.54%, 89.25%, and 85.19%, respectively. The accuracy of the five algorithms ranged from 81 to 86%, the sensitivity from 81 to 92%, and the specificity from 77 to 88%. Thus, the experimental results suggest that the model proposed in this study has high accuracy, high sensitivity, and high specificity. CONCLUSIONS: The "ensemble strategy model" can be used as a reference for the determination of vancomycin doses in clinical treatment.

BACKGROUND: The prevalence of chronic disease is growing in aging societies, and artificial-intelligence-assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design-a systematic, scientific experimental design-to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness. RESULTS: An open dataset of macular degeneration images ( https://data.mendeley.com/datasets/rscbjbr9sj/3 ) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance. CONCLUSION: The high stability of the ResNet model established using uniform design is attributable to the study's strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.

BACKGROUND: Heart sound measurement is crucial for analyzing and diagnosing patients with heart diseases. This study employed phonocardiogram signals as the input signal for heart disease analysis due to the accessibility of the respective method. This study referenced preprocessing techniques proposed by other researchers for the conversion of phonocardiogram signals into characteristic images composed using frequency subband. Image recognition was then conducted through the use of convolutional neural networks (CNNs), in order to classify the predicted of phonocardiogram signals as normal or abnormal. However, CNN requires the tuning of multiple hyperparameters, which entails an optimization problem for the hyperparameters in the model. To maximize CNN robustness, the uniform experiment design method and a science-based methodical experiment design were used to optimize CNN hyperparameters in this study. RESULTS: An artificial intelligence prediction model was constructed using CNN, and the uniform experiment design method was proposed to acquire hyperparameters for optimal CNN robustness. The results indicate Filters ([Formula: see text]), Stride ([Formula: see text]), Activation functions ([Formula: see text]), and Dropout ([Formula: see text]) to be significant factors considerably influencing the ability of CNN to distinguish among heart sound states. Finally, the confirmation experiment was conducted, and the hyperparameter combination for optimal model robustness was Filters ([Formula: see text]) = 32, Kernel Size ([Formula: see text] = 3 × 3, Stride ([Formula: see text]) = (1,1), Padding ([Formula: see text] as same, Optimizer ([Formula: see text] as the stochastic gradient descent, Activation functions ([Formula: see text]) as relu, and Dropout ([Formula: see text]) = 0.544. With this combination of parameters, the model had an average prediction accuracy rate of 0.787 and standard deviation of 0. CONCLUSION: In this study, phonocardiogram signals were used for the early prediction of heart diseases. The science-based and methodical uniform experiment design was used for the optimization of CNN hyperparameters to construct a CNN with optimal robustness. The results revealed that the constructed model exhibited robustness and an acceptable accuracy rate. Other literature has failed to address hyperparameter optimization problems in CNN; a method is subsequently proposed for robust CNN optimization, thereby solving this problem.

BACKGROUND: Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. RESULTS: We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000-2016 from three cities with a population of 4.7 million. The established computer model of "energy accumulation-delayed diffusion-epidemics" is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. CONCLUSIONS: Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.

BACKGROUND: Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. RESULTS: This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. CONCLUSION: In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model's predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.

The aims of this study were to use the technology acceptance model to examine how the cultural characteristics of social media users in Taiwan affect their use of social media for acquiring and sharing health-related information and to examine how their use of online social media benefits their social relationships and health self-efficacy. The research model in this quantitative cross-sectional study was tested with data collected from 321 active Facebook users in Taiwan. All three cultural characteristics/dimensions considered in the research model (masculinity, collectivism, and uncertainty avoidance) significantly affected the perceived usefulness and the perceived ease of using the online social media platform. However, masculinity had a significant positive effect on perceived usefulness but not on perceived ease of use. These results imply that technology tools for people in high masculinity cultures should be designed to maximize the effectiveness of the technology for achieving goals rather than to maximize the ease of using the technology. On the other hand, the use of online social media for acquiring and sharing health-related information significantly affected the social relationships of users but not their health self-efficacy. The results of this study imply that participants in online communities share health-related information not only to enhance their health but also to form strong social connections. This study proposes a new construct of technology acceptance, acquisition, and sharing of health-related information and investigates its effects on social relationships and health self-efficacy.

Based on the clinic data, 69 % - 80 % stroke patients in Taiwan have the muscle weakness symptom of upper limb. Stroke patients are able to restore their independent living if they keep formal rehabilitation without interruption. However, due to the tedious and painful process, stroke patients may pause or suspend their rehabilitation therapy. Moreover, physiatrists in Taiwan hospitals must take care of more than one patient at the same time. This paper is motivated to propose a motor assessment system for stroke upper-limb rehabilitation. Our implementation is integrated with the Complete Minnesota Dexterity Test to quantify patients' recovery status automatically. Therefore, our implementation is able to reduce the workload of physiatrists effectively. In addition to assessment scale score, the proposed system is able to record more details, e.g., the tremor status and the grip strength, for doctors to evaluate the following rehabilitation therapy. Finally, a body-sensed game is provided to increase interest to the rehabilitation process.

By integrating the stabilizability condition, the orthogonal-functions approach (OFA), and the hybrid Taguchi-genetic algorithm (HTGA), an integrative method is presented in this paper to design the stable and quadratic-optimal static output feedback parallel-distributed-compensation (PDC) controller such that (i) the Takagi-Sugeno (TS) fuzzy-model-based control system can be stabilized, and (ii) a quadratic integral performance index for the TS-fuzzy-model-based control system can be minimized. In this paper, the stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). A design example of stable and quadratic-optimal static output feedback PDC controller for a nonlinear inverted pendulum system controlled by a separately excited direct-current (DC) motor is given to demonstrate the applicability of the proposed new integrative approach.

This paper proposes a procedure for process parameters design by combining both modeling and optimization methods. The proposed procedure integrates the Taguchi method, the artificial neural network (ANN), and the genetic algorithm (GA). First, the Taguchi method is applied to minimize experimental numbers and to collect experimental data representing the quality performances of a system. Next, the ANN is used to build a system model based on the data from the Taguchi experimental method. Then, the GA is employed to search for the optimal process parameters. A process parameters design for a titanium dioxide (TiO2) thin film in the vacuum sputtering process is studied in this paper. The quality objective is to form a smaller water contact angle on the TiO2thin-film surface. The water contact angle is 4° obtained from the system model of the proposed procedure. The process parameters obtained from the proposed procedure were used to conduct the experiment in the vacuum sputtering process for the TiO2thin film. The water contact angle given from the practical experiment is 3.93°. The difference percent is 1.75% between 4° and 3.93°. The result obtained from the system model of the proposed procedure is promising. Hence, we can conclude that the proposed procedure is a very good approach in solving the problem of the process parameters design.

The robust completely controllability problem for the Takagi-Sugeno (TS) fuzzy descriptor systems is studied in this paper. The proposed sufficient condition can provide the explicit relationship of the bounds on parameter uncertainties for preserving the assumed properties.

The robust controllability problem for the Takagi-Sugeno (T-S) fuzzy-model-based control systems is studied in this paper. Under the assumption that the nominal T-S fuzzy-model-based control systems are locally controllable (i.e., each fuzzy rule of the nominal T-S fuzzy-model-based control systems has a full row rank for its controllability matrix), a sufficient condition is proposed to preserve the assumed property when the parameter uncertainties are added into the nominal T-S fuzzy-model-based control systems. The proposed sufficient condition can provide the explicit relationship of the bounds on parameter uncertainties to preserve the assumed property. Besides, a robustly global controllability condition and the related robustly global stabilizability condition of the uncertain T-S fuzzy-model-based control systems are also presented in this paper. A nonlinear mass-spring-damper mechanical system with parameter uncertainties is given as an example to illustrate the application of the proposed sufficient conditions.

This paper considers the design problem of the robust quadratic-optimal parallel-distributed-compensation (PDC) controllers for Takagi–Sugeno (TS) fuzzy-model-based control systems with both elemental parametric uncertainties and norm-bounded approximation error. By complementarily fusing the robust stabilizability condition, the orthogonal functions approach (OFA), and the hybrid Taguchi genetic algorithm (HTGA), an integrative method is presented in this paper to design the robust quadratic-optimal PDC controllers such that 1) the uncertain TS-fuzzy-model-based control systems can be robustly stabilized, and 2) a quadratic integral performance index for the nominal TS-fuzzy-model-based control systems can be minimized. In this paper, the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). By using the OFA and the LMI-based robust stabilizability condition, the robust quadratic-optimal PDC control problem for the uncertain TS-fuzzy-model-based dynamic systems is transformed into a static constrained-optimization problem represented by the algebraic equations with constraint of LMI-based robust stabilizability condition, thus greatly simplifying the robust optimal PDC control design problem. Then, for the static constrained-optimization problem, the HTGA is employed to find the robust quadratic-optimal PDC controllers of the uncertain TS-fuzzy-model-based control systems. Two design examples of the robust quadratic-optimal PDC controllers for an uncertain inverted pendulum system and an uncertain nonlinear mass–spring–damper mechanical system are given to demonstrate the applicability of the proposed integrative approach.

This article presents a hybrid multiobjective genetic algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed algorithm extends from the original method of inequality-based multiobjective genetic algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

For the finite-horizon optimal control problem of the Takagi-Sugeno (TS) fuzzy-model-based time-delay control systems, by integrating the delay-dependent stabilizability condition, the shifted-Chebyshev-series approach (SCSA), and the hybrid Taguchi-genetic algorithm (HTGA), an integrative method is presented to design the stable and quadratic optimal parallel distributed compensation (PDC) controllers. In this paper, the delay-dependent stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). Based on the SCSA, an algebraic algorithm only involving the algebraic computation is derived in this paper for solving the TS fuzzy-model-based time-delay feedback dynamic equations. In addition, by using the SCSA, the stable and quadratic optimal PDC control problem for the TS fuzzy-model-based time-delay control systems is replaced by a static parameter optimization problem represented by the algebraic equations with constraint of the LMI-based stabilizability condition, thus greatly simplifying the stable and optimal PDC control design problem. The computational complexity for both differential and integral in the stable and optimal PDC control design of the original dynamic systems may therefore be reduced considerably. Then, for the static constrained optimization problem, the HTGA is employed to find the stable and quadratic optimal PDC controllers of the TS fuzzy-model-based time-delay control systems. A design example of the stable and quadratic optimal PDC controllers for the continuous-stirred-tank-reactor system is given to demonstrate the applicability of the proposed integrative approach.

By complementarily fusing the orthogonal-functions approach (OFA) and the hybrid Taguchi-genetic algorithm (HTGA), an integrative method is presented to design the quadratic-finite-horizon-optimal fuzzy parallel-distributed-compensation (PDC) controllers of a class of time-varying Takagi-Sugeno (TS) fuzzy-model-based time-delay control systems. The proposed integrative method fusing the OFA and the HTGA can be viewed as one of the hard-computing-assisted soft-computing category, where the OFA belongs to the hard computing constituents and the HTGA is one of the soft computing constituents. A design example of the quadratic-optimal PDC controllers for the pendulum time-delay system with the vibration in the vertical direction on the pivot point is given to demonstrate the applicability of the proposed new integrative approach.

This paper considers the design problem of the robust quadratic-optimal parallel-distributed-compensation (PDC) controllers for the Takagi-Sugeno (TS) fuzzy-modelbased control systems with both elemental parametric uncertainties and norm-bounded approximation error. An integrative method, which complementarily fuses the robust stabilizability condition, the orthogonal-functions approach (OFA) and the hybrid Taguchi-genetic algorithm (HTGA), is presented in this paper to design the robust quadratic-optimal PDC controllers, in which the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs).

In this paper, we propose a method of multi-objective optimization Evolutionary Computation by using Evaluated Preference Genetic Algorithm (EPGA). The method is applied to quickly solve a time-effective aircraft routing in response to the schedule disruption of short-haul flights and tried to optimize objective functions including flight connection, flight duty swap, total flight delay time, delayed flights and flights over 30 minutes delay. The proposed EPGA approach here is a novel genetic algorithm to effectively resolve multi-objective optimization problems; it can consider multiple objectives simultaneously and then explore the optimal solution. Traditionally, airline schedule disruption management problem is solved by Operations Research (OR) techniques, which always require a precise mathematical model. But in real-world airline operation environment, there are too many factors to be considered dynamically, and thus it is very difficult to define a precise mathematical model in time. In this research, we propose EPGA to deal with Robust Airline Schedule Recover Problem (RASRP) which is easier to model the practical problems. Furthermore, this method is verified by real flight schedules of Taiwan domestic airlines. The results show that the high quality solutions can be obtained in a few minutes. Therefore, EPGA can be used as a fast decision support tool for practical complex airline operations.

By the use of the elegant operational properties of the orthogonal functions, a direct computational algorithm for solving the Takagi-Sugeno (TS) fuzzy-model-based feedback dynamic equations is first developed in this paper. The basic idea is that the state variables are expressed in terms of the orthogonal functions. The new method simplifies the procedure of solving the TS fuzzy-model-based feedback dynamic equations into the successive solution of a system of recursive formulas taking only two terms of the expansion coefficients. Based on the presented recursive formulas, the developed computational algorithm only involves the straightforward algebraic computation. Then, the developed algorithm is integrated with the hybrid Taguchi-genetic algorithm (HTGA) to design both the quadratic optimal fuzzy parallel-distributed-compensation (PDC) controller and the quadratic-optimal non-PDC controller (quadratic optimal linear-state feedback controller) of the TS fuzzy-model-based control systems under the criterion of minimizing a quadratic integral performance index, where the quadratic integral performance index is also converted into the algebraic form by using the orthogonal-function approach (OFA). The proposed new approach, which integrates the OFA and the HTGA, is nondifferential, nonintegral, straightforward, and well adapted to the computer implementation. The computational complexity can, therefore, be reduced remarkably. Thus, this proposed approach facilitates the design tasks of the quadratic optimal controllers for the TS fuzzy-model-based control systems. A design example of the quadratic optimal controllers for the translational oscillator system with an eccentric rotational proof mass actuator is given to demonstrate the applicability of the proposed approach

This paper considers the stable and quadratic finite-horizon optimal design problem of the linear state feedback controllers for the Takagi-Sugeno (TS) fuzzy-model-based control systems by integrating the stabilizability condition, the shifted-Chebyshev-series approach (SCSA), and the hybrid Taguchi-genetic algorithm (HTGA), where the stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). Based on the SCSA, an algorithm only involving the algebraic computation is derived in this paper for solving the TS-fuzzy-model-based feedback dynamic equations, and then is integrated with both the proposed sufficient LMI condition and the HTGA to design the stable and quadratic optimal linear state feedback controllers of the TS-fuzzy-model-based control systems under the criterion of minimizing a quadratic integral performance index, where the quadratic integral performance index is also converted into the algebraic form by using the SCSA. The presented new approach, which integrates the proposed LMI-based stabilizability condition, the SCSA and the HTGA, is non-differential, non-integral, straightforward, and well-adapted to computer implementation. The computational complexity may therefore be reduced remarkably. Thus, this proposed approach facilitates the design task of the stable and quadratic optimal linear state feedback controllers for the TS-fuzzy-model-based control systems. A design example of stable and quadratic optimal linear state feedback controller for the ball-and-beam system is given to demonstrate the applicability of the proposed new integrative approach

This paper considers the robust-optimal design problems of output feedback controllers for linear systems with both time-varying elemental (structured) and norm-bounded (unstructured) parameter uncertainties. A new sufficient condition is proposed in terms of linear matrix inequalities (LMIs) for ensuring that the linear output feedback systems with both time-varying elemental and norm-bounded parameter uncertainties are asymptotically stable, where the mixed quadratically-coupled parameter uncertainties are directly considered in the problem formulation. A numerical example is given to show that the presented sufficient condition is less conservative than the existing one reported recently. Then, by integrating the hybrid Taguchi-genetic algorithm (HTGA) and the proposed LMI-based sufficient condition, a new integrative approach is presented to find the output feedback controllers of the linear systems with both time-varying elemental and norm-bounded parameter uncertainties such that the control objective of minimizing a quadratic integral performance criterion subject to the stability robustness constraint is achieved. A design example of the robust-optimal output feedback controller for the AFTI/F-16 aircraft control system with the time-varying elemental parameter uncertainties is given to demonstrate the applicability of the proposed new integrative approach.

This paper considers the robust quadratic finite-time optimal design problem of the quadratic optimal linear state feedback controllers for the Takagi-Sugeno (TS) fuzzy-model-based control systems with elemental parametric uncertainties by integrating the robust stability condition, the shifted-Chebyshev-series approach (SCSA), and the hybrid Taguchi-genetic algorithm (HTGA), where the robust stability condition takes the elemental information of parametric uncertain matrices into consideration and is proposed in terms of linear matrix inequalities (LMls). Based on the SCSA, an algorithm only involving the algebraic computation is derived in this paper for solving the nominal TS-fuzzy-model-based feedback dynamic equations. By using the SCSA and the LMI-based robust stabilizability condition, the robust quadratic finite-time optimal linear state feedback control problem for the uncertain TS-fuzzy-model-based dynamic systems is transformed into a static constrained-optimization problem represented by algebraic equations with constraint of LMI-based robust stabilizability condition; thus greatly simplifying the robust optimal linear state feedback control design problem. Then, for the static constrained-optimization problem, the HTGA is employed to find the robust quadratic optimal linear state feedback controllers of the uncertain TS-fuzzy-model-based control systems.

A direct computational algorithm, by using the elegant operational properties of the shifted Chebyshev series (SCS), for solving the time-varying Takagi-Sugeno (TS) fuzzy-model-based feedback dynamic equations is first developed in this paper. The developed computational algorithm only involves the straightforward algebraic computation. Then, the developed algorithm is integrated with the hybrid Taguchi-genetic algorithm (HTGA) to design the optimal non-parallel-distributed-compensation (non-PDC) controller (linear state feedback controller) of the time-varying TS-fuzzy-model-based control systems under the criterion of minimizing a quadratic integral performance index, where the integral performance index is also converted into the algebraic form by using the SCS approach (SCSA). Thus, this proposed approach facilitates the design task of the non-PDC optimal controller for the time-varying TS-fuzzy-model-based control systems. A design example of the optimal non-PDC controller for the pendulum system with the vibration in the vertical direction on the pivot point is given to demonstrate the applicability of the proposed approach.