Weights and Structure Determination (WASD) of Multiple-Input Hermit Orthogonal Polynomials Neural Network (MIHOPNN)
Based on the theory of polynomial-interpolation and curve-.tting, a new multiple-input feed-forward neural network activated by Hermit orthogonal polynomials is proposed and investigated. Besides, the design makes the multiple-input Hermit orthogonal polynomials neural network (MIHOPNN) have no weakness of dimension explosion. To determine the optimal weights of the MIHOPNN, the weight direct determination (WDD) method is presented. To obtain the optimal structure of the MIHOPNN, the so-called weight and structure determination (WASD) method is .nally proposed, which aims at achieving the best approximation accuracy while obtaining the minimal number of hidden-layer neurons. Numerical results further substantiate the ef.cacy of the MIHOPNN model and WASD method.
Neural network Multi-input Hermit orthogonal polynomials Weights and structure determination
Yunong Zhang Junwei Chen Senbo Fu Lin Xiao Xiaotian Yu
School of Information Science and Technology,Sun Yat-sen University, Guangzhou 510006, P. R. China School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, P. R. China
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
1106-1111
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）