Applied system identification
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Convergence properties of the generalised least squares identitication method
Automatica (Journal of IFAC)
| Hi-index | 12.05 |
Parameter estimation is an important concept in engineering where a mathematical model of a system is identified with the help of input and output signals. Classical parameter estimation algorithms such as Least Squares (LS), Recursive Least Squares (RLS), Least Mean Squares (LMS) and Generalized Least Squares (GLS) give an unbiased estimate of the parameters when the system noise is white. This property is lost when the system noise is colored which is generally the case for many practical situations. In order to overcome the bias problem associated with the colored noise environment, one can use a whitening filter. The cost function of the estimation problem in the case of a colored noise environment becomes multimodal when the signal to noise ratio is high. Hence the motivation to use some intelligent optimization technique for the purpose of finding the global minimum of the parameter estimation problem. A new hybrid algorithm combining intelligent optimization techniques, i.e. enhanced continuous tabu search (ECTS) and elitism based genetic algorithm (GA) is proposed and is applied to the parameter estimation problem. In this work, the ECTS is used to define smaller search spaces, which are investigated in a second stage by a GA to find the respective local minima. Simulation results show that the parameters estimated using the proposed algorithm is unbiased in the presence colored noise. In addition, the hybrid algorithm is also tested with known multimodal benchmark problems.