Identification of Dynamic Nonlinear Systems using Computational Intelligence Techniques

Claudio Turchetti, Francesco Gianfelici
2007 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing  
A novel approach based on computational intelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loève Transform in representing stochastic processes and the approximation capabilities of multi-layer neural networks. Experimental results on nonlinear systems governed by difference equations demonstrate the effectiveness of the proposed approach that is based on a real-time learning
more » ... time learning algorithm. Exhaustive experimentation on specific case studies was performed and some experimental results were compared with other existing techniques such as the Lee-Schetzen method, Least Mean Square (LMS), Recursive Least Square (RLS) and Normalized Least Mean Square (NLMS) algorithms. A better identification-accuracy was also achieved, and a reduction of some orders of magnitude in training-times compared with the well-known Lee-Schetzen method was obtained, thus making the proposed methodology one of the current best practices in this field.
doi:10.1109/ciisp.2007.369291 fatcat:svtq4oclibg57dddbzxfwnmka4