BGNN Neural Network Based on Improved E.Coli Foraging Optimization Algorithm Used in the Nonlinear Modeling of Hydraulic Turbine

Authors:
Yijian Liu;Yanjun Fang
Affiliations:
School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing, China 210042 and Department of Automation, Wuhan University, Wuhan, China 430072;Department of Automation, Wuhan University, Wuhan, China 430072
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

A novel Bayesian-Gaussian neural network (BGNN) is proposed in this paper for the nonlinear modeling of hydraulic turbine which is difficult to obtain its mathematical model because of its complex and nonlinear characteristics. The topology and connection weights of BGNN can be set immediately when the training samples are available. The threshold matrix parameters of BGNN are updating based an improved E.Coli foraging optimization algorithm (IEFOA) which is an evolutionary optimization algorithm imitating the behaviors of E.Coli bacteria. Simulation results for the nonlinear model of hydraulic turbine generating unit are provided and demonstrate the effectiveness and shorter training time and more effective self-tuning compared with the BP neural network for the identification of hydraulic turbine generating unit.