P. S. Pal

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm.

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.

A hybrid backtracking search algorithm with wavelet mutation-based nonlinear system identification of Hammerstein models

In this paper a novel and accurate approach is presented to identify varieties of nonlinear Hammerstein models (closed loop and open loop) with the help of an optimization algorithm that combines a recently proposed backtracking search algorithm with wavelet theory-based mutation scheme (BSA-WM). The optimum output MSE associated with each plant along with its statistical information justifies the better precision and accuracy of BSA-WM-based identification approach as compared to the other methods reported in earlier literature.

Identification of fourth order nonlinear polynomial model using simplex particle swarm optimization algorithm

This paper presents an efficient approach for identification of fourth order nonlinear polynomial Hammerstein models using simplex particle swarm optimization (Simplex-PSO) algorithm. The accuracy of the proposed Simplex PSO based identification scheme has been justified with the optimal value of MSE and the corresponding comparative study with the other reported approaches. The statistical information of the MSE has been provided to justify the consistency of the Simplex-PSO algorithm for identification of Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify the efficiency of the proposed identification strategy.

Parametric identification of Box-Jenkins structured closed-loop Hammerstein systems using gravitational search algorithm

This paper presents a Gravitational Search Algorithm (GSA) based accurate parametric identification approach to identify a closed-loop Box-Jenkins structured Hammerstein model. The main objective of the employed algorithm is to estimate the parameters associated with the model by optimizing the fitness function which is the output mean square error (MSE) in this work. Efficient identification of a generalized practical closed-loop Hammerstein model has been achieved from the outcomes of the simulation studies. Convergence curves of the output MSE and the parameters show the consistency of the performance of the proposed GSA based approach. Effective identification in the presence of colour noise shows the robustness of the GSA based system identification problem.

Hammerstein model based system identification using craziness based particle swarm optimization algorithm

This paper proposes an accurate and efficient strategy for identification of Hammerstein model based nonlinear systems using Craziness based Particle Swarm Optimization (CRPSO) algorithm. The closeness of estimation of the proposed CRPSO based identification scheme has been justified with the optimal value of MSE and the corresponding comparative study with the other techniques reported in earlier literature. The statistical information of the MSE has also been provided to justify consistency of the CRPSO algorithm for identification of Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify the accuracy of the proposed identification scheme. Accurate identification of the linear block also ensures the stability of the overall system in the presence of noise.

Identification of a two stage cascaded nonlinear system of trigonometric nonlinearity using Particle Swarm Optimization with Aging Leader and Challengers

This paper proposes an accurate and efficient approach for identification of a two stage cascaded Hammerstein model using Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO) algorithm. To enhance the computational speed and to avoid the premature convergence criteria, ALC-PSO is used. The accuracy and the precision of the proposed ALC-PSO based identification scheme have been justified with the achieved optimal value of MSE along with its comparative study with the other reported earlier approaches. The statistical information of the MSE has also been provided to justify consistency of the ALC-PSO algorithm for identification of Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify efficiency of the proposed identification strategy. Proper identification of the linear counterpart of the considered model justifies the stability of the overall system.

Identification of Hammerstein model using bacteria foraging optimization algorithm

This paper presents an efficient approach for identification of a nonlinear Hammerstein model using Bacteria Foraging Optimization (BFO) Algorithm. The accuracy and the efficiency of the proposed BFO based identification scheme have been justified with the optimal value of MSE and the corresponding comparative statistical information. The statistical information of the MSE has also been provided to justify consistency of the BFO algorithm for identification of the Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify efficiency of the proposed identification strategy. The deviations of the estimated parameters from their actual values are also reported to justify precision and effectiveness of the BFO based identification approach.

Social emotional optimization algorithm based identification of nonlinear hammerstein model

In this paper an efficient and accurate approach for identification of Hammerstein model has been proposed using Social Emotional Optimization Algorithm (SEOA). In SEOA method, behaviour of human being for attaining superior position in society is structured. The accuracy of the proposed SEOA based identification scheme has been justified with the optimal value of MSE and their corresponding comparative study with the other approaches reported earlier. The statistical information of the MSE has also been provided to justify consistency of the SEOA algorithm for identification of Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify efficiency of the proposed identification strategy.