Using self-powered sensors, traffic data may be collected continuously, efficiently, and sustainably once connected autonomous vehicles (CAVs) are a part of metaverse technology. Metaverse self-powered sensors can capture uninterrupted data that allow for activities such as the management of the traffic network, the optimization of transportation facilities, and the management of urban and intercity journeys to be performed. In addition, metaverse technology creates a new field of study. Evaluating the systems involved in current transportation activities together with the metaverse can increase the efficiency and sustainability of transportation. The main purpose of this study is to prioritize four alternatives of CAVs in metaverse with self-powered sensors using a novel decision making model. The proposed hybrid decision making framework includes two stages. In the first stage the fuzzy full consistency method (fuzzy FUCOM) is applied to find the weighting coefficients of criteria. In the second stage, a fuzzy non-linear model based on fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) is defined to rank the alternatives. Four alternatives are defined and evaluated using twelve different criteria under four headings, namely, technical advancement, environmental, implementation, and financial aspects. A case study has been created for the experts to evaluate the alternatives most effectively. The results of the study indicate that using self-powered sensors for integrating real-time traffic management in the metaverse is the most advantageous alternative.

In this article a design methodology for Mamdani interval type-3 fuzzy systems with center-of-sets type reduction is outlined. The methodology utilizes statistical measures, fuzzy c-means clustering and granular computing, to establish the justifiable footprint of uncertainty (JFOU) of the fuzzy granules, as explainable semantic abstractions that form the fuzzy model. The design methodology is presented in three general steps, first we use the principle of justifiable granularity to build a diagram of the justifiable information granule that contains a data structure with the descriptive measures of the experimental evidence of the data set. These measures are obtained from the partition matrix of the utilized clustering process, and these measures are used to evaluate the parameters of membership functions and characterize their JFOU. Second, we use the data structure of the justifiable information granule to characterize and parameterize the asymmetric interval type-3 membership functions. Lastly, the main procedure to obtain all the justifiable information fuzzy granules that define the knowledge base and the inference process of the fuzzy model is presented. Experiments were made with synthetic and real benchmark data from automated learning repositories, computing R adj 2 ${R}_{\mathrm{adj}}^{2}$ and root-mean-squared error to measure the reliability of the methodology, while keeping the justifiable uncertainty of the model.

This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Dow Jones time series. The Dow Jones time series is used to the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Genetic Algorithms (GAs) were used for the optimization of membership function parameters of each interval type-2 fuzzy integrator. In the experiments we optimized Gaussian, Generalized Bell and Triangular membership functions parameter for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.

Soft computing can be used to build hybrid intelligent systems for achieving different goals in real-world applications.Soft computing techniques include, at the moment, fuzzy logic, neural networks, genetic algorithms, chaos theory methods, and similar techniques that have been proposed in recent years.Each of these techniques has advantages and disadvantages, and several real-world problems have been solved, by using one of these techniques.However, many real-world complex problems require the integration of several of these techniques to really achieve the efficiency and accuracy needed in practice.In particular, evolutionary computing can be used to optimize the topology of a fuzzy or a neural system.Also, there are neuro-fuzzy approaches or even neuro-fuzzy-genetic approaches for designing the best intelligent system for a particular application.This special issue consists of five papers that consider the use and integration of different soft computing techniques for the development of hybrid intelligent systems for modeling, simulation, and control of nonlinear dynamical systems.The five papers, of this special issue, describe different applications of soft computing techniques to real-world problems and can be considered a significant contribution to the field of hybrid intelligent systems.The first paper, "An Artificial Bee Hive for Continuous Optimization" by Mario A. Muñoz et al., deals with an artificial bee-hive algorithm for optimization in continuous search spaces based on a model aimed at individual bee behavior.The algorithm defines a set of behavioral rules for each agent to determine what kind of actions must be carried out.In addition, the proposed algorithm includes some adaptations not considered in the biological model to increase the performance in the search for better solutions.To compare the performance of the algorithm to other swarm-based algorithms a statistical analysis was performed.The second paper, "A Levenberg-Marquardt Learning Applied for Recurrent Neural Identification and Control for a Wastewater Treatment Bioprocess" by Ieroham Baruch and Carlos R. Mariaca-Gaspar, describes a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants.The proposed RNN identifier is implemented in direct and indirect adaptive

An integration of a cognitive map and a fuzzy inference engine is presented, as a cognitive–fuzzy model, targeting online fuzzy logic controller (FLC) design and self fine-tuning. The proposed model is different than previous proposed fuzzy cognitive maps in that it presents a hierarchical architecture in which the cognitive map process, available plant, and control objective data on represented knowledge to generate a complete FLC architecture and parameters description. Online assessment of measured data is processed for linguistic characterization of performance to determine the required FLC parameter's adjustments, the process is repeated until the control objective is reached. A mathematical model of the proposed approach is presented, and sample numerical data illustrate the following: (a) cognitive map construction, (b) start-up self fine-tuning, and (c) system's response to error of plant descriptive data. Simulation results demonstrate model interpretability, which suggests that the model is scalable and offers robust capability to generate near optimal controller, emulating human iterative design flow, and fine-tuning within the knowledge domain of cognitive map. © 2009 Wiley Periodicals, Inc.

We describe a new approach for face recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces. Also, the method for achieving response integration is based on the fuzzy Sugeno integral. Response integration is required to combine the outputs of all the modules in the modular network. We have applied the new approach for face recognition to a real database of faces of students at our institution. Recognition rates with the modular approach were compared against the monolithic single neural network approach to measure the improvement. The results of the new modular neural network approach were excellent overall and also in comparison to the monolithic approach. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 275–291, 2005.

In this article we analyzed the explorative behavior of the Breeder Genetic Algorithm. We tested this algorithm using fuzzy recombination and the classical discrete mutation operator. A general review of the most common linear model structures is given for recommending where to use this type of evolutionary algorithm. We selected for convenience a finite impulse response structure model because it has global optima; we considered this characteristic advisable for analyzing the evolution of the best and poorest individuals in the population through the generations. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 293–311, 2005.

We describe in this article a new hybrid fuzzy-fractal approach for plant monitoring. We use the concept of the fractal dimension to measure the complexity of a time series of observed data from the plant. We also use fuzzy logic to represent expert knowledge on monitoring the process in the plant. In the hybrid fuzzy-fractal approach, a set of fuzzy if-then rules are used to classify different conditions of the plant. The fractal dimension is used as an input linguistic variable in the fuzzy system to improve the accuracy in the classification. An implementation of the proposed approach is shown to describe in more detail the method. © 2002 Wiley Periodicals, Inc.