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《Acta Electronica Sinica》 2004-09
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Particle Swarm Optimization Based Algorithm for Neural Network Learning

GAO Hai-bing,GAO Liang,ZHOU Chi,YU Dao-yuan (Inst of Automation,Dept.of Industrial Engineering,Huazhong Univ of Sci & Tech,Wuhan,Hubei 430074,China)  
This paper proposes a structure-improving particle swarm optimization (SPSO) algorithm for training artificial neural network (ANN).The algorithm is successfully applied to pattern classification problems including Iris,ionosphere and breast cancer.By tuning the structure and connection weights of ANN simultaneously,the proposed algorithm generates optimized ANN with problem-matched capacity for processing classification information.By doing this,it also eliminates some ill effects introduced by redundant input features and the corresponding structure of ANN.Compared with BP and GA based training techniques,SPSO can improve the classification accuracy while speeding up the convergence process.Simulation results show that SPSO is a potentially robust learning algorithm and could be extended to real world applications.
【Fund】: 国家自然科学基金 (No .50 30 50 0 8);; 国家高技术研究发展计划 (863计划 )课题 (No .2 0 0 2AA42 0 1 0 0 3)
【CateGory Index】: TP183
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