An Optimization Method of Hidden Nodes for Neural Network
Pengyi Gao, Chuanbo Chen, Sheng Qin · 2010
The selection for the number of hidden nodes for a neural network is of critical importance. This paper proposes a novel algorithm to determine the number of hidden nodes of a neural network and optimize it. In the method, the number of hidden nodes H is first computed by empirical formulas, and the range of H is determined according to computed result. Then, the "three points search" is applied to search the best number of hidden nodes within the range. Finally, a GTA (Genetic algorithm and Tabu search Algorithm Approach) is developed to train the weights of neural network constructed with the best H. Test results obtained by using Iris data set has shown to be efficient, and better than those by the most commonly used optimization techniques.