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Xing Zong-yi,Zhang Yuan,Qin Yong,Jia Li-min,Wu Ying-ying 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
The paper presents an approach to model the electrohydraulic system of a certain mine-sweeping weapon using the Radial Basis Function (RBF) neural networks. In order to obtain accurate and simple RBF neural networks efficiently, a hierarchical genetic algorithm (HGA) is used to train the neural networks, in which the number of hidden units and the parameters of centers are optimized by the HGA simultaneously. The spread factors and the weights of the neural networks are calculated by the linear algebra methods for relieving computational burden. The proposed algorithm is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can model the hydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional methods.
On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm
Zong-Yi Xing,Yong Zhang,Yuan-Long Hou,Li-Min Jia 대한전기학회 2007 International Journal of Control, Automation, and Vol.5 No.4
An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-Ⅱ algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.
Zong-yi Xing,Xue-miao Pang,Hai-yan Ji,Yong Qin,Li-min Jia 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.4
The paper presents an approach to model nonlinear dynamic behaviors of the Automatic Depth Control Electrohydraulic System (ADCES) of a certain minesweeping weapon with Radial Basis Function (RBF) neural networks trained by hierarchical genetic algorithm. In the proposed hierarchical genetic algorithm, the control genes are used to determine the number of hidden units, and the parameter genes are used to identify center parameters of hidden units. In order to speed up conver-gence of the proposed algorithm, width and weight parameters of RBF neural network are calculated by linear algebra methods. The proposed approach is applied to the modelling of the ADCES, and ex-perimental results clearly indicate that the obtained RBF neural network can emulate complex dynamic characteristics of the ADCES satisfactorily. The comparison results also show that the proposed approach performs better than the traditional clustering-based method.
Study on Interpretable Fuzzy Classification System Based on Neural Networks
Qin Yong,Xing Zong-yi,Jia Li-min,Wu Ying-ying 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper describes a comprehensive method to construct fuzzy classification system considering bothprecision and interpretability. Fuzzy classification system, initialized by modified Gath-Geva fuzzy clustering algorithm, is transformed into neural network. After training the neural network, fuzzy sets similarity measure is adopt to mergeredundant fuzzy sets to improve interpretability, and a constraint genetic algorithm is applied to improve precision. The simulation result on Iris data problem demonstrates the effectiveness of the proposed method