Affiliations: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, P.R. China
Correspondence:
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Corresponding author. Xuemei Guan, College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, P.R. China. E-mail: guanxuemei742@163.com.
Abstract: In practical application, the parameters of RBF neural network are difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a genetic algorithm to optimize the centers and the widths of hidden nodes and the connection weights between hidden layer and output layer of RBF neural network globally. In contrast to optimizing RBF neural network by genetic algorithm partially, each generation group contains the whole parameters of RBF neural network. The fitness value of each individual is calculated by the adaptive function. The optimal individual is obtained by selecting, crossover and mutation by genetic algorithm. The optimal parameters are chosen as initial value of RBF neural network. According to the characteristics of wood dyeing, a predictive model of pigment formula for wood dyeing based on RBF neural network is proposed. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is optimized globally by genetic algorithm is only 0.87% in 20 generations. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.
Keywords: Genetic algorithm, RBF neural network, global optimization, pigment
formula
