Jia Limin

A Multi-objective Cooperative Coevolutionary Algorithm for Constructing Accurate and Interpretable Fuzzy systems

A novel approach to construct accurate and interpretable fuzzy classification system based on the multi-objective cooperative coevolutionary algorithm (MOCCA) is proposed in this paper. First, feature selection is used to reduce the dimensionality of the data in order to both improve the performance and reduce computational effort. Then the fuzzy clustering algorithm is employed to identify the initial fuzzy system. Third, the MOCCA with three species is carried out to evolve the initial fuzzy system to optimize its structures and parameters. In MOCCA, the interpretability-driven simplification techniques are used to reduce the fuzzy system, thus the interpretability of the fuzzy system is improved; the number of rules, the antecedents of the fuzzy rules and the parameters of the antecedents are optimized simultaneously. Finally, the proposed approach is applied to six benchmark problems, and the results show its validity.

Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by genetic algorithm-based technique. In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network. The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of the proposed technique is relieved. The proposed technique is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the electrohydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method.

Research on fuzzy modeling based on modified fuzzy clustering and its application

A new method of fuzzy modeling based on T-S fuzzy model is presented. In this approach, a modified fuzzy clustering algorithm is combined with the least square approximation method to identify the structure and parameters of the fuzzy model from a set of data. An L-M technique is used to optimize initial fuzzy model. The proposed method is applied to coke-oven temperature system, and the simulation results demonstrate its effectiveness.