System identification and linearisation using genetic algorithms with simulated annealing

Tan, K.C., Li, Y. , Murray-Smith, D. and Sharman, K.C. (1995) System identification and linearisation using genetic algorithms with simulated annealing. In: Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA 95), Sheffield, UK, 12-14 Sep 1995, pp. 164-169.

Abstract

This paper develops high performance system identification and linearisation techniques, using a genetic algorithm. The algorithm is fine tuned by simulated annealing, which yelds a faster convergence and a more accurate search. This global search technique is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to approximate a nonlinear multivariable system by a linear time-invariant state-space model. Results obtained show that simple step inputs can be used for effective system identification and linearisation with much higher performance than is possible by conventional means.

• Item Type: Conference Proceedings
• Keywords: System identification, linearisation, genetic algorithm, simulated annealing, optimisation
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor David and Li, Professor Yun
• Authors: Tan, K.C., Li, Y., Murray-Smith, D., and Sharman, K.C.
• Subjects: Q Science > QA Mathematics > QA76 Computer software; T Technology > TJ Mechanical engineering and machinery; T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy