Yong-Ji Xu

A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications

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.

A novel genetic reinforcement learning for nonlinear fuzzy control problems

Abstract Unlike a supervise learning, a reinforcement learning problem has only very simple “evaluative” or “critic” information available for learning, rather than “instructive” information. A novel genetic reinforcement learning, called reinforcement sequential-search-based genetic algorithm (R-SSGA), is proposed for solving the nonlinear fuzzy control problems in this paper. Unlike the traditional reinforcement genetic algorithm, the proposed R-SSGA method adopts the sequential-search-based genetic algorithms (SSGA) to tune the fuzzy controller. Therefore, the better chromosomes will be initially generated while the better mutation points will be determined for performing efficient mutation. The adjustable parameters of fuzzy controller are coded as real number components. We formulate a number of time steps before failure occurs as a fitness function. Simulation results have shown that the proposed R-SSGA method converges quickly and minimizes the population size.

A hybrid evolutionary learning algorithm for TSK-type fuzzy model design

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, and there are multiple groups in a population. Moreover, the proposed HELA adopts the compact genetic algorithm (CGA) to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method gives a better performance than some existing methods.

Face detection in color images using efficient genetic algorithms

Face detection from images is a key problem in human computer interaction studies and pattern recognition research. In this work, we propose an efficient genetic algorithm (EGA) that solves the face detection problem in color images. The proposed EGA is based on the Takagi-Sugeno-Kang(TSK)-type fuzzy model employed to perform parameter learning. Compared with traditional genetic algorithms, the EGA uses the sequential-search based-efficient generation (SSEG) method to generate an initial population to determine the most efficient mutation points. Experimental results show that the performance of the EGA is superior to the existing traditional genetic methods.

A Self-Constructing Neural Fuzzy Network with Dynamic-Form Symbiotic Evolution

Abstract In this paper, we propose aself-constructing neural fuzzy network with dynamic-form symbiotic evolution (SCNFN-DSE) for solving various problerns. A novel hybrid learning approach, which consists of the self-clustering algorithm (SCA) and the dynamic-form symbiotic evolution (DSE), is proposed for adjusting the parameters of neural fuzzy networks. First, the proposed SCA is used to identify a pazsimonious internal structure. The SCA is an online clustering method and is a distance-based connectionist clustering method. Second, the proposed DSE uses the sequential-search based dynamic evolution (SSDE) method. The better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic-mutation. Simulation results have shown that 1) the SCNFN-DSE model converges quickly; 2) the SCNFNDSE model requires a small number of population sizes; 3) the SCNFN-DSE model construct only 4 fuzzy models in every generation.

An efficient genetic algorithm for TSK-type neural fuzzy identifier design

In this paper, an efficient genetic algorithm (EGA) for TSK-type neural fuzzy identifier (TNFI) is proposed for solving identification problem. For the proposed EGA method, the better chromosomes will be initially generated while the better mutation points will be determined for performing efficient mutation. The adjustable parameters of a TNFI model are coded as real number components and are searched by EGA method. The advantages of the proposed learning algorithm are that, first, it converges quickly and the obtained fuzzy rules are more precise. Secondly, the proposed EGA method only takes a few population sizes.

An efficient genetic algorithm for TSK‐type neural fuzzy identifier design

Abstract In this paper, an efficient genetic algorithm (EGA) of the Takagi‐Sugeno‐Kang (TSK) ‐type neural fuzzy identifier (TNFI) is proposed for solving various identification problems. For the proposed EGA method, the better chromosomes will be initially generated while the better mutation points will be determined to perform efficient mutation. The advantages of the proposed learning algorithm are that, first, it converges quickly and the obtained fuzzy rules are precise. Secondly, the proposed EGA method only requires a small population sizes.

A novel evolution learning for recurrent wavelet-based neuro-fuzzy networks

This study presents a recurrent wavelet-based neuro-fuzzy network with dynamic symbiotic evolution (RWNFN-DSE) model which combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). The proposed RWNFN-DSE is used to dynamic system processing. A novel evolution learning called dynamic symbiotic evolution (DSE) is used to tune the parameter of the RWNFN-DSE model. Better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic-mutation. Simulation results have shown that the proposed RWNFN-DSE model obtains better performance than other existing models