A novel social emotional optimisation algorithm for IIR system identification problem

Most real-time systems are nonlinear in nature, and their optimization is very difficult due to inherit stiffness and complex system representation. The computational intelligent algorithms of evolutionary computing paradigm (ECP) effectively solve various complex, nonlinear optimization problems. The differential evolution algorithm (DEA) is one of the most important approaches in ECP, which outperforms other standard approaches in terms of accuracy and convergence performance. In this study, a novel application of a recently proposed variant of DEA, the so-called, maximum-likelihood-based, adaptive, differential evolution algorithm (ADEA), is investigated for the identification of nonlinear Hammerstein output error (HOE) systems that are widely used to model different nonlinear processes of engineering and applied sciences. The performance of the ADEA is evaluated by taking polynomial- and sigmoidal-type nonlinearities in two case studies of HOE systems. Moreover, the robustness of the proposed scheme is examined for different noise levels. Reliability and consistent accuracy are assessed through multiple independent trials of the scheme. The convergence, accuracy, robustness and reliability of the ADEA are carefully examined for HOE identification in comparison with the standard counterpart of the DEA. The ADEA achieves the fitness values of 1.43 × 10−8 and 3.46 × 10−9 for a population size of 80 and 100, respectively, in the HOE system identification problem of case study 1 for a 0.01 nose level, while the respective fitness values in the case of DEA are 1.43 × 10−6 and 3.46 × 10−7. The ADEA is more statistically consistent but less complex when compared to the DEA due to the extra operations involved in introducing the adaptiveness during the mutation and crossover. The current study may consider the approach of effective nonlinear system identification as a step further in developing ECP-based computational intelligence.

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Global Optimization by Annealing Type of Evolutionary Programming and Its Application to System Identification Problem Using Neural Networks

Transactions of the Institute of Systems Control and Information Engineers ◽

10.5687/iscie.7.439 ◽

1994 ◽

Vol 7 (11) ◽

pp. 439-447

Author(s):

Takashi SHIMA

Keyword(s):

Neural Networks ◽

Global Optimization ◽

System Identification ◽

Identification Problem ◽

Evolutionary Programming

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Servo System Identification Based on Curve Fitting to Phase-Plane Trajectory

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4045431 ◽

2019 ◽

Vol 142 (3) ◽

Author(s):

Xiaoli Shi ◽

Yong Han ◽

Jianhua Wu ◽

Zhenhua Xiong

Keyword(s):

System Identification ◽

Curve Fitting ◽

Phase Plane ◽

Least Squares Method ◽

Approximation Error ◽

Servo System ◽

Identification Problem ◽

Identification Accuracy ◽

System Parameters ◽

Plane Trajectory

Abstract Many methods have been proposed to identify servo system parameters. However, problems still remain in widely applied offline identification methods, for example, the describing-function-based relay feedback method has the ineradicable approximation error, and acceleration information is indispensable for the least-squares method. In order to identify systems accurately and efficiently with less servo system information, this article proposes a novel method to identify servo system parameters through curve fitting to the phase-plane trajectory under the help of one optimization method. Specifically, the phase-plane trajectory expression of the single-degree-of-freedom system is derived; the process on how to convert the servo system identification problem to a curve-fitting optimization problem is described in detail; and the guidelines of the initial parameter setting are introduced. Simulations and experiments are carried out to verify the efficiency of the proposed method. Finally, a feed-forward control based on the identified parameters is designed to further validate the identification accuracy.

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Evolutionary Computing Approaches to System Identification

Handbook of Research on Computational Intelligence Applications in Bioinformatics - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-0427-6.ch015 ◽

2016 ◽

pp. 329-368

Author(s):

Bidyadhar Subudhi ◽

Debashisha Jena

Keyword(s):

System Identification ◽

Complex Systems ◽

Evolutionary Computation ◽

Case Studies ◽

Evolutionary Computing ◽

Identification Problem ◽

Engineering Problem ◽

Important Class ◽

Essential Requirement ◽

The Past

In this chapter, we describe an important class of engineering problem called system identification which is an essential requirement for obtaining models of system of concern that would be necessary for controlling, analyzing the systems. The system identification problem is essentially to pick up the best model out of the several candidate models. Thus, the problem of system identification or modeling building turns out to be an optimization problem. The chapter explain what are different evolutionary computing techniques used in the past and the state- of the art technologies on evolutionary computation. Then, some case studies have been included how the system identification of a number of complex systems effectively achieved by employing these evolutionary computing techniques.

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