Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 23, Issue 2, Pages 342-347Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2011.2178559
Keywords
Adaptive computation algorithm; modelling; nonlinear systems; radial basis function neural networks
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Funding
- National 863 Scheme Foundation of China [2009AA04Z155, 2007AA04Z160]
- National Science Foundation of China [61034008, 60873043]
- Ph.D. Program Foundation from Ministry of Chinese Education [200800050004]
- Beijing Municipal Natural Science Foundation [4092010]
- Academic Human Resources Development [PHR(IHLB)201006103]
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A novel learning algorithm is proposed for nonlinear modelling and identification using radial basis function neural networks. The proposed method simplifies neural network training through the use of an adaptive computation algorithm (ACA). In addition, the convergence of the ACA is analyzed by the Lyapunov criterion. The proposed algorithm offers two important advantages. First, the model performance can be significantly improved through ACA, and the modelling error is uniformly ultimately bounded. Secondly, the proposed ACA can reduce computational cost and accelerate the training speed. The proposed method is then employed to model classical nonlinear system with limit cycle and to identify nonlinear dynamic system, exhibiting the effectiveness of the proposed algorithm. Computational complexity analysis and simulation results demonstrate its effectiveness.
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