Infinite impulse response system identification using average differential evolution algorithm with local search

2021 ◽  
Author(s):  
Burhanettin Durmuş
Keyword(s):  
System Identification ◽  
Local Search ◽  
Differential Evolution ◽  
Impulse Response ◽  
Differential Evolution Algorithm ◽  
Infinite Impulse Response ◽  
Response System ◽  
Evolution Algorithm

scholarly journals Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3199
Author(s):  
Hasnat Bin Tariq ◽  
Naveed Ishtiaq Chaudhary ◽  
Zeshan Aslam Khan ◽  
Muhammad Asif Zahoor Raja ◽  
Khalid Mehmood Cheema ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
System Identification ◽  
Nonlinear System ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Nonlinear System Identification ◽  
Differential Evolution Algorithm ◽  
Identification Problem ◽  
Intelligent Computing ◽  
Evolution Algorithm

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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