Title: The Training and Application of Process Neural Network based on Genetic-Simulated Annealing Algorithm
Abstract: Process neural network (PNN) is a new neural network,whose inputs and weights are time-varying functions. While there exist some problems for PNN that the learning algorithm has high complexity and is sensitive to initial value. Considering the deficiency of BP algorithm, a learning algorithm of PNN is presented. PNN trains the network by combi- ning genetic algorithm and simulated annealing algorithm, whose input functions and network weights are expanded based on orthogonal basis. The algorithm of PNN avoids trapping into local minimum, and overcomes the problem that the iteration number of simulated annealing algorithm will be increased when finding the optimal solutions, which makes the network have fast convergence rate and high approximation accuracy. The corresponding learning steps and parameter selec- tion method are given in this paper. The experiment of water flooded layer identification is taken as an example to verify the effectiveness of the algorithm.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
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