Affine Takagi-Sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion

SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. KEYWORDSchaos adaptive particle swarm optimization, fuzzy C-regression model clustering algorithm, identification, multiple-input multiple-output, Takagi-Sugeno fuzzy models

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“…5 The WRLS can be applied to estimate the consequent parameters for each rule. The WRLS algorithm is described as follows 26 :…”

Section: Fcrm Clustering Algorithm For Mimo Processmentioning

confidence: 99%

A fuzzy C‐regression model algorithm using a new PSO algorithm

Adel

Soltani

Châari

2017

Adaptive Control & Signal

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“…Based on the fundamental theories of knowledge base modelling and manipulation, knowledge base technology can be categorized into four groups: 1) the linguistic knowledge bases (Baker, 2014;Fellbaum, 1998;Speer & Havasi, 2012); 2) expert knowledge bases (Driankov et al, 2013;Kerr-Wilson & Pedrycz, 2016;Kung & Su, 2007); 3) ontology (Fensel, 2004;Sánchez, 2010;Studer et al, 1998;Van Heijst et al, 1997) and most recently 4) the cognitive knowledge base (Wang, 2015b). The various categories and types of knowledge base modelling figure 2.…”

Section: Classification Of Knowledge Base Modelling Techniquesmentioning

confidence: 99%

“…It classifies these technologies according to their development theories and structure, resulting to four categories; the linguistic knowledge bases (Collin F Baker, 2014;Fellbaum, 1998;Speer & Havasi, 2012), expert knowledge bases (Driankov, Hellendoorn, & Reinfrank, 2013;Kerr-Wilson & Pedrycz, 2016;Kung & Su, 2007), ontology (Khan, 2009;Fensel, 2004;David Sánchez, 2010;Studer etal., 1998;Van Heijst et al, 1997) and most recently the cognitive knowledge base (Wang, 2015b). Human knowledge is categorized at the levels of data, information, knowledge and intelligence.…”

Section: Introductionmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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“…Therefore, the outcome of software cost estimation can be listed as [6]:  At every step estimation for effort is carried out during software project development.…”

Section: Figure1 Software Effort Estimation Processmentioning

confidence: 99%

“…In software expense estimation approach a comparative, finished, task is recognized and its genuine exertion is utilized as the premise of the appraisal for the new venture [6]. It is infrequently utilized at the early phases of software advancement in light of such inalienable vulnerability and imprecision connected with property estimation.…”

Section: Related Workmentioning

confidence: 99%

An Effort Estimation Model for Software Development using Ensemble Learning

Kumar¹,

Datta²

2015

IJCA

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For a successful project development, it is important for any software organization that the project should be completed within time and budget, and the project should have requisite quality. This paper presents an Ensemble learning based Adaptive Neuro-Fuzzy Approach for Software Development Time Estimation. The concept behind this technique is based on ensemble learning methods. This technique combines multiple models into one model. The ensemble fits a new learner to the difference between the experiential response and the aggregated prediction of all learners which grown previously. In this paper, we describe a brief literature review of different techniques of software development time estimation along with a comparison of different techniques with our approach.

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Affine Takagi-Sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion

Cited by 95 publications

References 32 publications

A fuzzy C‐regression model algorithm using a new PSO algorithm

A fuzzy C‐regression model algorithm using a new PSO algorithm

Towards knowledge modeling and manipulation technologies: A survey

An Effort Estimation Model for Software Development using Ensemble Learning

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