title: A new optimized GA-RBF neural network algorithm.
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.
authors.affiliation: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
authors.forename: Weikuan
authors.identifier: 0000-0001-6242-3269
authors.initials: W
authors.lastname: Jia
authors.affiliation: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
authors.forename: Dean
authors.initials: D
authors.lastname: Zhao
authors.affiliation: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
authors.forename: Tian
authors.identifier: 0000-0002-8754-7513
authors.initials: T
authors.lastname: Shen
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China.
authors.forename: Chunyang
authors.initials: C
authors.lastname: Su
authors.affiliation: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
authors.forename: Chanli
authors.initials: C
authors.lastname: Hu
authors.affiliation: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China ; Changzhou College of Information Technology, Changzhou 213164, China.
authors.forename: Yuyan
authors.initials: Y
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016018:Least-Squares Analysis; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D060388:Support Vector Machine