Fuzzy Identification And Neural Networks Learning Based On Cooperative PSO Algorithms

Particle swarm optimization (PSO) algorithm is a novel swarm intelligence-based algo-rithm which mimics the movement of birds ?ocking or fish schooling looking for food. Thealgorithm finds optimal solution by the cooperation among particles. Similar to the geneticalgorithms, PSO algorithm is an optimal technology based on population, which generatesa set of random solutions and searches the space cooperatively through pursuing the bestexperiences of the swarm. PSO algorithm draws more and more attention and becomes aresearch focus because it has such features as easy operation, fewer tuning parameters, highconvergence speed, etc.Cooperative search is one of the many areas that have been extensively studied in thepast decade to solve many large size optimization problems. The main idea involves havingmore than one search module running and exchanging information among each other inorder to explore the search space more efficiently and reach better solutions. Together withtheir applications in fuzzy identification and neural networks learning, the cooperative PSOalgorithms, which combine the cooperative search and PSO algorithms, are mainly studied.First, the social background, research contents and open problems of PSO algorithmare introduced; next, the basic framework and analysis of parameters are provided; thenthe cooperative random learning PSO algorithm is presented to enhance the performanceof the original PSO algorithm; finally, the improved algorithm and the coevolutionary PSOalgorithm are used in fuzzy modeling and neural network learning. The main contributionsof this dissertation can be summarized as follows:1. A cooperative random learning PSO, which employs several sub-swarms to searchthe space simultaneously, is proposed. In the evolving process, the information among dif-ferent sub-swarms exchanges randomly. The particles learn the best history information toupdate the velocities and positions, thus the diversity of the swarm is maintained. At thesame time, the convergence speed of the algorithm is kept by employing more useful infor-mation during the iteration. The cooperative random learning strategy balances the globaland local search abilities very well. 2. A two-stage fuzzy identification method based on subtractive clustering and coop-erative random learning PSO algorithm is proposed. In the proposed method, subtractiveclustering is utilized to partition the input space and extract a set of fuzzy rules and cooper-ative random learning PSO algorithm is used to find the optimal membership functions andconsequent parameters of the rule base. Furthermore, the subtractive clustering is used toinitialize the swarm of cooperative random learning PSO algorithm. The proposed methodcan extract compact and accurate fuzzy model effectively.3. The cooperative random learning PSO algorithm is introduced into the single mul-tiplicative neuron (SMN) model to enhance its learning ability for time series prediction.The SMN model can be considered as a neural network with simple structure and fewer pa-rameters to take the place of the multiple layers neural network for function approximation.The learning efficiency and robustness of the SMN model are improved through training bycooperative random learning PSO algorithm.4. A new approach for constructing the fuzzy systems automatically by coevolutionaryPSO algorithm is proposed. At first, a maximum rule number is predefined according to priorknowledge. Every rule has a label to determine whether it belongs to the fuzzy inferencesystem. The labels, antecedent and consequent parameters of the model are encoded intothree particles and searched by three PSO algorithms cooperatively. The model structure isobtained through evolving of the labels; meanwhile, the optimal parameters are reached. Theproposed method can construct the accurate fuzzy model from the input-output data directly.5. A coevolutionary PSO-based method to tune the structure and parameters of a neu-ral network for tackling the connections redundant problems is proposed. A neural networkwith switches is introduced firstly, and the switches have two values 0 and 1. The struc-ture of a neural network is decided by the switches. Then, coevolutionary PSO algorithm,which uses a binary PSO algorithm and a standard PSO algorithm, is employed to optimizethe structure and parameters of the proposed model. This method can obtain an accuratepartially-connected neural network effectively.