Title :
Hybrid BP-GA for multilayer feedforward neural networks
Author :
Lu, Chun ; Shi, Bingxue ; Chen, Lu
Author_Institution :
Inst. of Microelectron., Tsinghua Univ., Beijing, China
Abstract :
BP (backpropagation algorithm) and GA (genetic algorithm) are among the most effective algorithms of neural networks (NN). As deterministic gradient-descent algorithm and stochastic optimizing algorithm respectively, there exists great compatibility between their advantages and disadvantages. The proposed hybrid BP-GA learning method for multilayer feedforward neural networks blends the merits of both BP and GA. Based on BP-GA, a two-layer feedforward neural network is designed. HSPICE simulation results have proved its ability to solve the XOR problem
Keywords :
SPICE; backpropagation; deterministic algorithms; feedforward neural nets; genetic algorithms; gradient methods; multilayer perceptrons; HSPICE simulation; XOR problem; deterministic gradient-descent algorithm; hybrid BP-GA; multilayer feedforward neural networks; stochastic optimizing algorithm; two-layer network; Arithmetic; Convergence; Feedforward neural networks; Genetic algorithms; Hardware; Learning systems; Microelectronics; Multi-layer neural network; Neural networks; Stochastic processes;
Conference_Titel :
Electronics, Circuits and Systems, 2000. ICECS 2000. The 7th IEEE International Conference on
Conference_Location :
Jounieh
Print_ISBN :
0-7803-6542-9
DOI :
10.1109/ICECS.2000.913035
