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2017 | OriginalPaper | Buchkapitel

A Method for Non-linear Modelling Based on the Capabilities of PSO and GA Algorithms

verfasst von : Piotr Dziwiński, Łukasz Bartczuk, Huang Tingwen

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

The most nonlinear dynamic objects have their Approximate Nonlinear Model (ANM). Their parameters are known or can be determined by one of the typical identification procedures. The model obtained in this way describes well the main features of the identified dynamic object only in some Operating Point (OP). In this approach we use hybrid model increasing accuracy of the modeling. The hybrid model is composed of two parts: base ANM and Takagi-Sugeno (TS) fuzzy system. A Particle Swarm Optimization with Genetic Algorithm (PSO-GA) was used for identification of the parameters of the ANM and TS fuzzy system. An important advantage of the proposed approach is the obtained characteristics of the unknown parameters of the ANM described by the Fuzzy Rules (FR) of the TS fuzzy system. They provide the valuable knowledge for the experts about the nature of the unknown phenomena.

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Vorheriges Kapitel The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming

Nächstes Kapitel Linguistic Habit Graphs Used for Text Representation and Correction

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Titel: A Method for Non-linear Modelling Based on the Capabilities of PSO and GA Algorithms
verfasst von: Piotr Dziwiński
Łukasz Bartczuk
Huang Tingwen
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-59059-2

Electronic ISBN: 978-3-319-59060-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59060-8_21

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