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Parameter Estimation of Wiener Model Based on Improved Bacterial Foraging Optimization

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2 Author(s)
Weifeng Huang ; Fac. of Inf. Sci. & Technol., Ningbo Univ., Ningbo, China ; Weixing Lin

Identification of a nonlinear model is a main topic of modern identification. It is presented that a new approach to parameters estimation for one type of nonlinear models (Wiener model) by using improved bacterial foraging optimization (IBFO) algorithm. Firstly, the basic principle of bacterial foraging optimization (BFO) algorithm is introduced, and then proposed an IBFO. Parameters estimation for a Wiener model is exchanged to its optimization using IBFO. Comparing with BFO, IBFO and improved particle swarm optimization (IPSO) in different signal to noise ratio (SNR), a numerical example is presented to illustrate the effectiveness of the proposed methods.

Published in:

Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on  (Volume:1 )

Date of Conference:

23-24 Oct. 2010