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Abstract: In this paper, we propose a new learning algorithm that can be used to design TSK-type neuro-fuzzy networks. Though there has been a great deal of interest in the use of immune algorithms (IAs) for computer science and engineering, in terms of fundamental methodologies, they are not dramatically different from other algorithms. In order to enhance the IA performance, we propose the immune-based symbiotic particle swarm optimization (ISPSO) for use in TSK-type neuro-fuzzy networks for solving the prediction and skin color detection problems. The proposed ISPSO embeds the symbiotic evolution scheme in an IA and utilizes particle swarm optimization (PSO) to improve the mutation mechanism. In order to avoid trapping in a local optimal solution and to ensure the search capability of a near global optimal solution, mutation plays an important role. Therefore, we employed the advantages of PSO to improve the mutation mechanism and used a method that introduces chaotic mapping with certainty, ergodicity and the stochastic property into PSO to improve global convergence. Unlike the IA that uses each individual in a population as a full solution to a problem, symbiotic evolution assumes that each individual in a population represents only a partial solution to a problem. Complex solutions combine several individuals in the population. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a self-adaptive neural fuzzy network with group-based symbiotic evolution (SANFN-GSE) method. A self-adaptive learning algorithm consists of two major components. First, a self-clustering algorithm (SCA) identifies a parsimonious internal structure. An internal structure is said to be parsimonious in the sense that the number of clusters (fuzzy rules) is equal to the true number of clusters in a given training data set. The proposed SCA is an online method and is a distance-based connectionist clustering method. Unlike the traditional cluster techniques that only consider the total variation to updates the only one mean and deviation. The proposed SCA method considers the variation of each dimension for the input data. Second, a group-based symbiotic evolution learning (GSE) method is used to adjust the parameters for the desired outputs. The GSE method is different from traditional GAs (genetic algorithms), with each chromosome in the GSE method representing a fuzzy system. Moreover, in the GSE method, there are several groups in the population. Each group represents a set of the chromosomes that belong to a cluster computing by the SCA. In this paper we used numerical time series examples (one-step-ahead prediction, Mackey-Glass chaotic time series, and sunspot number forecasting) to evaluate the proposed SANFN-GSE model. The performance of the SANFN-GSE model compares excellently with other existing models in our time series simulations. [Copyright &y& Elsevier]

Abstract: In this paper, a new asymmetry-similarity-measure-based neural fuzzy inference system (ASM-NFIS) is proposed. A pseudo-Gaussian membership function can provide a neural fuzzy inference system which has a higher flexibility and can approach the optimized result more accurately. An on-line self-constructing learning algorithm is proposed to automatically construct the ASM-NFIS. It consists of structure learning and parameter learning that would create adaptive fuzzy logic rules. The structure learning is based on the similarity measure of asymmetric Gaussian membership functions, and the parameter learning is based on a supervised gradient descent method. Computer simulations were conducted to illustrate the performance and applicability of the proposed model. [Copyright &y& Elsevier]

Abstract: In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) for identification and prediction is proposed. The compensatory-based fuzzy method uses the adaptive fuzzy operations of neuro-fuzzy systems to make fuzzy logic systems more adaptive and effective. A recurrent network is embedded in the RCNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. In this paper, the RCNFS model is proved to be a universal approximator. Also, an online learning algorithm is proposed which can automatically construct the RCNFS. There are no rules initially in the RCNFS. They are created and adapted as online learning proceeds through simultaneous structure and parameter learning. Structure learning is based on the degree measure and parameter learning is based on the ordered derivative algorithm. Finally, the RCNFS is used in several simulations. The simulation results of the dynamic system model have shown that (1) the RCNFS model converges quickly; (2) the RCNFS model requires a small number of tuning parameters; (3) the RCNFS model can solve temporal problems and approximate a dynamic system. [Copyright &y& Elsevier]

This paper addresses the structure and an associated learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The genetic algorithm-based neural fuzzy system (GA-NFS) is based on Takagi–Sugeno–Kang (TSK) type model possessing a neural network''s learning ability. A hybrid learning algorithm is proposed for parameters learning. The proposed algorithm combines the genetic algorithm (GA) and the least-squares estimate (LSE) method to construct the GA-NFS. The genetic algorithm is used to tune membership functions at the precondition part of fuzzy rules while the LSE method is used to tune parameters at the consequent part of fuzzy rules. The performance of the GA-NFS is compared to that of the traditional PID controller and fuzzy logic controller on the water bath temperature control system. [Copyright &y& Elsevier]