Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm

IEEE Transactions on Neural Networks
Jinn-Tsong TsaiTung-Kuan Liu

Abstract

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the HTGA approach can be more robust, statistically sound, and quickly convergent. First, the authors evaluate the performance of the presented HTGA approach by studying some global numerical optimization problems. Then, the presented HTGA approach is effectively applied to solve three examples on forecasting the sunspot numbers, tuning the associative memory, and solving the XOR problem. The numbers of hidden nodes and the links of the feedforward neural network are chosen by increasing them from small numbers until the learning performance is good enough. As a result, a...Continue Reading

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Citations

May 3, 2008·International Journal of Neural Systems·Sotaro Kawata, Akira Hirose
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems· Tzyy-Chyang Lu Jyh-Ching Juang
Aug 15, 2008·IEEE Transactions on Neural Networks·Shuning WangKhan M Junaid
Feb 14, 2008·IEEE Transactions on Neural Networks·Jian Xun PengGeorge W Irwin
May 29, 2007·IEEE Transactions on Neural Networks·Ivan Jordanov, Antoniya Georgieva
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Nov 13, 2015·IEEE Transactions on Neural Networks and Learning Systems·Shida LiuChenkun Yin
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Aug 27, 2017·Neural Networks : the Official Journal of the International Neural Network Society·Mohammad Bataineh, Timothy Marler

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