Case Studies for Genetic Algorithms in System Identification Tasks

2010 ◽  
pp. 243-260
Author(s):  
Aki Sorsa ◽  
Riikka Peltokangas ◽  
Kauko Leiviskä
Keyword(s):  
Genetic Algorithms ◽  
System Identification ◽  
Case Studies

scholarly journals Nonlinear System Identification in coherence with Nonlinearity measure for Dynamic Physical systems- Case Studies

2021 ◽  
Author(s):  
Joanofarc Xavier ◽  
Rames C Panda ◽  
SK Patnaik
Keyword(s):  
Nonlinear Dynamics ◽  
System Identification ◽  
Nonlinear System ◽  
Case Studies ◽  
Nonlinear System Identification ◽  
Least Square ◽  
Measurement Tool ◽  
Identification Methods ◽  
Physical Systems ◽  
Highly Nonlinear

Abstract With the recent success of using the time series to vast applications, one would expect its boundless adaptation to problems like nonlinear control and nonlinearity quantification. Though there exist many system identification methods, finding suitable method for identifying a given process is still cryptic. Moreover, to this notch, research on their usage to nonlinear system identification and classification of nonlinearity remains limited. This article hovers around the central idea of developing a ‘kSINDYc’ (key term based Sparse Identification of Nonlinear Dynamics with control) algorithm to capture the nonlinear dynamics of typical physical systems. Furthermore, existing two reliable identification methods namely NL2SQ (Nonlinear least square method) and N3ARX (Neural network based nonlinear auto regressive exogenous input scheme) are also considered for all the physical process-case studies. The primary spotlight of present research is to encapsulate the nonlinear dynamics identified for any process with its nonlinearity level through a mathematical measurement tool. The nonlinear metric Convergence Area based Nonlinear Measure (CANM) calculates the process nonlinearity in the dynamic physical systems and classifies them under mild, medium and highly nonlinear categories. Simulation studies are carried-out on five industrial systems with divergent nonlinear dynamics. The user can make a flawless choice of specific identification methods suitable for given process by finding the nonlinear metric (Δ0). Finally, parametric sensitivity on the measurement has been studied on CSTR and Bioreactor to evaluate the efficacy of kSINDYc on system identification.

Evolutionary Computing Approaches to System Identification

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.

Application of Natural-Inspired Paradigms on System Identification

2018 ◽  
pp. 1-50
Author(s):  
Mateus Giesbrecht ◽  
Celso Pascoli Bottura
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
System Identification ◽  
Genetic Programming ◽  
Linear Time ◽  
Identification Problem ◽  
Multivariable System ◽  
Swarm Optimization ◽  
Future Directions ◽  
Time Invariant

In this chapter, the application of nature-inspired paradigms on system identification is discussed. A review of the recent applications of techniques such as genetic algorithms, genetic programming, immuno-inspired algorithms, and particle swarm optimization to the system identification is presented, discussing the application to linear, nonlinear, time invariant, time variant, monovariable, and multivariable cases. Then the application of an immuno-inspired algorithm to solve the linear time variant multivariable system identification problem is detailed with examples and comparisons to other methods. Finally, the future directions of the application of nature-inspired paradigms to the system identification problem are discussed, followed by the chapter conclusions.

A Study on Evolutionary Technique to Predict the Sales During COVID-19

2021 ◽  
pp. 376-402
Author(s):  
Manu PriyaDarshani ◽  
Mohan Prasad Sinha ◽  
Keshav Sinha
Keyword(s):  
Growth Rate ◽  
Genetic Algorithms ◽  
Case Studies ◽  
Indian Market ◽  
Heavy Industry ◽  
Traditional Methods ◽  
Sales Prediction ◽  
Enormous Amount ◽  
Evolutionary Technique

COVID-19 has affected the growth of every industry; in the meantime, an enormous amount of demand is present in the field of telecom and automobiles. In this chapter, the authors present case studies based on sales prediction for the Indian market. The analysis of the study is based on the various traditional methods like growth rate (GR), percentage growth rate (PGR), and the evolutionary techniques like genetic algorithms (GA). The data are collected for the report of telecommunication and heavy industry ministry (Republic of India). The results are used to analyze the sale of automobiles and telecommunication devices and to predict the growth at the time of the COVID-19 pandemic. The prediction is used to identify the upcoming sale and counterparts with demand.

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