Search results

The purpose of this paper is to propose an improved optimization methodology of information granulation‐based fuzzy radial basis function neural networks (IG‐FRBFNN). In the IG‐FRBFNN, the membership functions of the premise part of fuzzy rules are determined by means of fuzzy c‐means (FCM) clustering. Also, high‐order polynomial is considered as the consequent part of fuzzy rules which represent input‐output relation characteristic of sub‐space and weighted least squares learning is used to estimate the coefficients of polynomial. Since the performance of IG‐RBFNN is affected by some parameters such as a specific subset of input variables, the fuzzification coefficient of FCM, the number of rules and the order of polynomial of consequent part of fuzzy rules, we need the structural as well as parametric optimization of the network. The proposed model is demonstrated with the use of two kinds of examples such as nonlinear function approximation problem and Mackey‐Glass time‐series data.

The type of polynomial of each fuzzy rule is determined by selection algorithm by considering the local error as performance index. In addition, the combined local error is introduced as a performance index considered by two kinds of parameters such as the polynomial type of each rule and the number of polynomial coefficients of each rule. Besides this, other structural and parametric factors of the IG‐FRBFNN are optimized to minimize the global error of model by means of the hierarchical fair competition‐based parallel genetic algorithm.

The performance of the proposed model is illustrated with the aid of two examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

The proposed hybrid optimization methodology is interesting for designing an accurate and highly interpretable fuzzy model. Hybrid optimization algorithm comes in the form of the combination of the combined local error and the global error.

The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation (IG‐FRBFNN) and their optimization realized by means of the Multiobjective Particle Swarm Optimization (MOPSO).

In fuzzy modeling, complexity, interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. Since the performance of the IG‐RBFNN model is directly affected by some parameters, such as the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials in the consequent parts of the rules, the authors carry out both structural as well as parametric optimization of the network. A multi‐objective Particle Swarm Optimization using Crowding Distance (MOPSO‐CD) as well as O/WLS learning‐based optimization are exploited to carry out the structural and parametric optimization of the model, respectively, while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.

The performance of the proposed model is illustrated with the aid of three examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

A MOPSO‐CD as well as O/WLS learning‐based optimization are exploited, respectively, to carry out the structural and parametric optimization of the model. As a result, the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators.

To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier.

Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator. Through different experiments, the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators. Furthermore, by extending the conclusions and expounding the obtained results, one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions. Based on the exerted experiments and implementing the method, the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks. Additionally, the derived gray box method has a higher degree of preference over the black box identifiers, as it allows a manipulated expert to extract the knowledge of the system at hand.

The originality of the research paper is twofold. From the practical (engineering) point of view, the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model. From the computational point of view, the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand, and high computational expense pertinent to the structure of black-box identifiers.