Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, pre- and post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30%.

This paper presents the estimation of the transmission gain for an AC power line data network in an intelligent home. The estimated gain ensures the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given.

This paper presents the recognition of speech commands using a modified neural-fuzzy network. To train the parameters of the network, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an Electronic Book experimentally to illustrate the design and its merits.

This paper presents the recognition of Cantonese speech commands using a proposed neural fuzzy network with rule switches. By introducing a switch to each rule, the optimal number of rules can be learned. An improved genetic algorithm (GA) is proposed to train the parameters of the membership functions and the optimal rule set for the proposed neural fuzzy network. An application example of Cantonese command recognition in electronic books will be given to illustrate the merits of the proposed approach.

This paper addresses the stable fuzzy controller design problem of nonlinear systems. The methodology is based on a fuzzy logic approach and the genetic algorithm (GA). In order to analyze the system stability, the TSK fuzzy plant model is employed to describe the dynamics of the nonlinear plant. A fuzzy controller is then developed to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the GA. An application example on stabilizing an inverted pendulum system will be given. Simulation and experimental results will be presented to verify the applicability of the proposed approach.

We prove that a filter bank with set of decimators {2,4,4} achieves perfect reconstruction if and only if it can be represented via a tree structure and each branch of the tree structure achieves perfect reconstruction.

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.

This paper presents stability analysis of fuzzy model-based nonlinear control systems, and the design of nonlinear gains and feedback gains of the nonlinear controller using a genetic algorithm (GA) with arithmetic crossover and nonuniform mutation. A stability condition is derived based on Lyapunov's stability theory with a smaller number of Lyapunov conditions. A solution of the stability conditions is also determined using GA. An application example of stabilizing a cart-pole typed inverted pendulum system is given to show the stabilizability of the nonlinear controller.

This paper presents the learning of neural network parameters using a fuzzy genetic algorithm (GA). The proposed fuzzy GA is modified from the traditional GA with arithmetic crossover and non-uniform mutation. By introducing modified genetic operations, it will be shown that the performance of the proposed fuzzy GA are better than the traditional GA based on some benchmark test functions. Using the fuzzy GA, the parameters of the neural networks can be tuned. An application example on sunspot forecasting is given to show the merits of the proposed fuzzy GA.

This paper presents a proposed neural fuzzy network tuned by genetic algorithm (GA). By introducing a switch to each rule, the optimal number of rules can be learned. The membership functions of the neural fuzzy network are also tuned by GA. After training, the proposed neural fuzzy network is employed to interpret graffiti number inputs and commands for electronic books (eBooks).

This paper presents the stability analysis of fuzzy model based nonlinear control systems, and the design of nonlinear gains and feedback gains of the nonlinear controller using genetic algorithm with arithmetic crossover and nonuniform mutation. A stability condition is derived based on the Lyapunov stability theory with a smaller number of Lyapunov conditions. The solution of the stability conditions are also determined using GA. An application example of stabilizing a cart-pole type inverted pendulum system is given to show the stabilizability of the nonlinear controller.

In this paper, a stable fuzzy neural tracking control of a class of unknown nonlinear systems based on the fuzzy hierarchy approach is proposed. The adaptive fuzzy neural controller is constructed from the fuzzy neural network with a set of fuzzy rules. The corresponding network parameters are adjusted online according to the control law and update law for the purpose of controlling the plant to track a given trajectory. A stability analysis of the unknown nonlinear system is discussed based on the Lyapunov principle. In order to improve the convergence of the nonlinear dynamical systems, a fuzzy hierarchy error approach (FHEA) algorithm is incorporated into the adaptive update and control scheme. The simulation results for an unstable nonlinear plant demonstrate the control effectiveness of the proposed adaptive fuzzy neural controller and are consistent with the theoretical analysis.

This paper tackles the control problem of nonlinear systems subject to parameter uncertainties based on the fuzzy logic approach and genetic algorithm (GA). In order to achieve a stable controller, the TSK fuzzy plant model is employed to describe the dynamics of an uncertain nonlinear plant. A fuzzy controller and the corresponding stability conditions are derived. The parameters of the fuzzy controller and the solution to the stability conditions are determined using GA. In order to obtain the optimal performance, the membership functions of the fuzzy controller are obtained automatically by minimizing a defined fitness function using GA.

This paper presents a design approach of linear controllers for nonlinear systems with unknown parameters within known bounds. The plant is represented by a fuzzy model. Stability condition will be derived based on Lyapunov stability theory and formulated into an LMI (linear matrix inequality) problem. The linear controller can be designed by solving the LMIs. To illustrate the merits and the design procedure of the proposed linear controller, an application example on stabilizing an inverted pendulum on a cart with unknown parameters is given.

Stability of a fuzzy system controlled by a fuzzy observer-controller will be investigated in this paper. In general, the stability condition is a common solution of [p(p+1)+2p]/2 linear matrix inequalities (LMIs) where p is the number of rules of the fuzzy plant model. In this paper, the number is reduced to 2p+l. Furthermore, gains of the fuzzy observer-controller can be derived from the solution of the LMIs. Separation principle of the fuzzy observer-controller will be proved.

Deals with nonlinear plants subject to unknown parameters based on a model reference approach. The nonlinear plant is represented by a fuzzy plant model. A switching controller is designed based on the fuzzy plant model to drive the system states of the nonlinear plant to follow those of a reference model. An application example on controlling an inverted pendulum on a cart is given to illustrate the design procedure of the proposed switching controller.

In this paper, a short-term home daily load forecasting system realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It is also shown that the improved GA performs better than a traditional GA on some benchmark test functions. By introducing switches in the links of the NFN, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the NFN can be generated automatically. Simulation results for a short-term daily load forecast in an intelligent home are given.

Daily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.

This paper presents a fast method to plan the path, and control the position of wheeled mobile robots (WMR) in a robot soccer game. In a WMR system with position control, when the target position is given, the controller will generate the reference path and then the WMR will move to the target position following the reference path. To tackle the difficulties of the nonlinear control problem, the motion of the WMR is realized via changing the linear displacement and angular displacement in a separate manner such that the WMR is either rotating or moving in a straight line at any one time. This method takes advantage of a proposed fast path-planning algorithm. The complexity of design is thus significantly reduced.

This paper presents the stability analysis of a fuzzy-model-based control system consisting of a nonlinear plant and a nonlinear state feedback controller and the design of the nonlinear gains of the controller. The nonlinear plant is represented by a fuzzy model having p rules. A nonlinear state feedback controller is designed to close the feedback loop. Under this design, the stability condition is reduced to p linear matrix inequalities. An application example on stabilizing a mass-spring-damper system will be given.

This paper presents a switching control scheme for multivariable nonlinear systems. Based on a fuzzy plant model of the multivariable nonlinear system, a switching controller consisting of a number of simple sub-controllers is proposed. The parameters of the switching controller can be obtained by solving some linear matrix inequalities. A switching algorithm is developed. To illustrate the merits and the design procedures of the proposed switching controller, an application example is given.

Presents the stability analysis of a fuzzy control system consisting of a nonlinear plant and a fuzzy controller and the design of the membership functions of the fuzzy controller. The nonlinear plant is represented by a fuzzy model having p rules. A c-rule fuzzy state-feedback controller is used to close the feedback loop. A design methodology for the fuzzy controller is provided. Under this design, the stability condition is reduced to p linear matrix inequalities. An application example on stabilizing a mass-spring-damper system is given.

Presents the stability and robustness analysis for fuzzy control systems subject to parameter uncertainties, and the design of gains for the fuzzy controller. Based on Lyapunov stability theory, stability and robustness conditions are derived. The stability conditions involves only p+1 linear matrix inequalities, where p is the number of rules of the fuzzy controller. A design methodology for the gains of the fuzzy controller is given. An application example on stabilizing a nonlinear mass-spring-damper system subject to parameter uncertainties is given to illustrate the merits of the proposed fuzzy controller.

Presents the control of nonlinear systems subject to parameter uncertainties under a fuzzy control approach. The nonlinear plant tackled in this paper is an n-order nonlinear systems with n inputs. A design procedure of a fuzzy controller is provided such that the states of the closed-loop system will follow those of a user-defined stable reference model despite the presence of parameter uncertainties. A numerical example is given to show the design procedures and the merits of the proposed fuzzy controller.

Many existing stability analysis methods for fuzzy logic control systems like TS fuzzy model based methods usually tackle plants that are linear with respect to control. The paper proposes a method which does not have such restriction. The proposed method employs a Lyapunov function to prove the stability of the non-linear fuzzy logic control system. An illustrative example is given to demonstrate the ability of the method. The plant in this example has a control signal function composed of piecewise linear functions, saturation functions and a deadband.

This paper presents a novel fuzzy neural network which consists of an antecedent network and a consequent network. The antecedent network matches the premises of the fuzzy rules and the consequent network implements the consequences of the rules. In the network learning and training phase, a concise and effective algorithm based on the fuzzy hierarchy error approach is proposed to update the parameters of the network. This algorithm is simple to implement and it does not require as many calculations as some other classic neural network learning algorithms. A model reference adaptive control structure incorporating the proposed fuzzy neural network is studied. Simulation results of a cart-pole balancing system demonstrate the effectiveness of the proposed method.

Presents the stability and robustness analysis for multivariable fuzzy control systems subject to parameter uncertainties based on a single-grid-point (SGP) approach. To perform the analysis, we represent a multivariable nonlinear system using a TS-fuzzy plant model. Three design approaches of fuzzy controllers are introduced to close the feedback loop. By estimating the matrix measures of the system parameters and parameter uncertainties, stability and robustness conditions for different cases are derived. Application examples are given to show the design procedures and the merits of the proposed fuzzy controller.

According to the basic optimization principle of artificial neural networks, a novel kind of neural network model for solving the quadratic programming problem is presented. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. The stability and convergency of the neural network is investigated and the strategy of the neural optimization is discussed. The feasibility of the neural network method is verified with the computation examples. Results of the simulation of the neural network to solve optimum problems are presented to illustrate the computational power of the neural network method.

Recently we have proposed a simple circuit of winner-take-all (WTA) neural network. Assuming no external input, we have derived an analytic equation for its network response time. In this paper, we further analyze the network response time for a class of winner-take-all circuits involving self-decay and show that the network response time of such a class of WTA is the same as that of the simple WTA model.

A simple method is presented to derive the complete solution of the Maxnet network dynamics. Besides, the exact response time of the network is deduced.

In this paper, the method of elliptic Fourier descriptors using arc length parameterization is applied to tackle the problem of detection and recovery of symmetry under parallel projection. A simple and fast iteration algorithm together with the invariants of symmetry provides sufficient information for the detection and recovery of symmetry under parallel projection. The proposed method has been extensively tested using symmetric figures under different parallel projections. Simulation results of the algorithm are presented. The extension of this method for planar object recognition under parallel projection is also addressed.>

In this paper, a theory of optimal nonlinear estimation from sampled data signals where the a posteriori probability densities are approximated by Gaussian sums is adapted for application to phase and frequency estimation in high noise. The nonlinear estimators (demodulators) require parallel processing of the received signal. In the limit as the number of parallel processors becomes infinite the FM demodulators become optimum in a minimum mean square error sense and the PM demodulators become optimum in some well defined sense. For the clearly suboptimal case of one processor, the demodulators can be readily simplified to the familiar phase-locked loop. On the other hand, for the intermediate case, significant extension of the phaselocked loop threshold is achieved where (say) six parallel processors are involved.

Simulation results are presented which are very convincing in favor of FM demodulators driven by in-phase and quadraturephase signals. Application of the extended Kalman filtering algorithms to the appropriate signal model directly yields demodulators in which the error covariance equations are uncoupled from the state estimate equations. These demodulators perform a little better than others derived using different nonlinear filtering techniques and the necessary approximations to achieve "decoupling." More significantly, these demodulators using in-phase and quadrature-phase sampled signals are readily augmented to achieve demodulation with delay by the application of fixed-lag smoothing algorithms. Simulations highlight the attractive trade-offs between demodulation complexity and performance results for this class of demodulator.