On-line Identification of Continuous-time Hammerstein Systems via RBF Networks and Immune Algorithm

This paper deals with an on-line identification method of continuous-time Hammerstein systems by using the radial basis function (RBF) networks and immune algorithm (IA). An unknown nonlinear static part to be estimated is approximately represented by the RBF network. The IA is efficiently combined with the recursive least-squares (RLS) method. The objective function for the identification is regarded as the antigen. The candidates of the RBF parameters such as the centers and widths are coded into binary bit strings as the antibodies and searched by the IA. On the other hand, the candidates of both the weighting parameters of the RBF network and the system parameters of the linear dynamic part are updated by the RLS method. Simulation results are shown to illustrate the proposed method.

Authors:

• Tomohiro Hachino
• Kengo Nagatomo
• Hitoshi Takata

Keywords:

• Continuous-time System
• Hammerstein System
• OnlineIdentification
• Immune Algorithm
• RBF network.

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