A New Optimized GA-RBF Neural Network Algorithm

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Abstract
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer’s neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
Funding Information
  • National Natural Science Foundation of China (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)
  • National Natural Science Foundation of China (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)
  • National Natural Science Foundation of China (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)
  • Priority Academic Program Development of Jiangsu Higher Education Institutions (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)
  • National Science & Technology Pillar Program (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)
  • Doctoral Program of Higher Education (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)
  • Ordinary University Graduate Student Research Innovation Projects of Jiangsu Province (60875052, 61203014, 61379101, 2011BAD20B06, 20133227110024, KYLX 14 1062)

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