Weights and structure determination (WASD) of multiple-input hermit orthogonal polynomials neural network (MIHOPNN)

Based on the theory of polynomial-interpolation and curve-fitting, 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 finally proposed, which aims at achieving the best approximation accuracy while obtaining the minimal number of hidden-layer neurons. Numerical results further substantiate the efficacy of the MIHOPNN model and WASD method.