Journal ArticleDOI
Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Haiping Du,Nong Zhang +1 more
- Vol. 8, Iss: 1, pp 676-686
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TLDR
It is shown by the validation applications that the developed evolving T-S fuzzy model can identify the nonlinear system satisfactorily with acceptable number of rules and appropriate inputs.Abstract:
In this paper, a new encoding scheme is presented for learning the Takagi-Sugeno (T-S) fuzzy model from data by genetic algorithms (GAs). In the proposed encoding scheme, the rule structure (selection of rules and number of rules), the input structure (selection of inputs and number of inputs), and the antecedent membership function (MF) parameters of the T-S fuzzy model are all represented in one chromosome and evolved together such that the optimisation of rule structure, input structure, and MF parameters can be achieved simultaneously. The performance of the developed evolving T-S fuzzy model is first validated by studying the benchmark Box-Jenkins nonlinear system identification problem and nonlinear plant modelling problem, and comparing the obtained results with other existing results. Then, it is applied to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper of which identification problem is significantly difficult due to its inherently hysteretic and highly nonlinear dynamics. It is shown by the validation applications that the developed evolving T-S fuzzy model can identify the nonlinear system satisfactorily with acceptable number of rules and appropriate inputs.read more
Citations
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Journal ArticleDOI
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Babak Rezaee,M. H. Fazel Zarandi +1 more
TL;DR: Comparing the proposed approach with some other fuzzy systems and neural networks, it is shown that the developed TSK fuzzy system exhibits better results with higher accuracy and smaller size of architecture.
Journal ArticleDOI
OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi–Sugeno Fuzzy Modeling
TL;DR: A new T-S fuzzy system parameters searching strategy called OptiFel with a heterogeneous multiswarm PSO (MsPSO) to enhance the searching performance and generate a good fuzzy system model with high accuracy and strong generalization ability.
Journal ArticleDOI
Automatically extracting T-S fuzzy models using cooperative random learning particle swarm optimization
TL;DR: An improved version of the original PSO algorithm, the cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of PSO.
Journal ArticleDOI
Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review
TL;DR: An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented, which focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models.
Journal ArticleDOI
Hydraulic turbine governing system identification using T-S fuzzy model optimized by chaotic gravitational search algorithm
TL;DR: A novel Takagi-Sugeno (T-S) fuzzy model identification method based on chaotic gravitational search algorithm (CGSA) is proposed and applied in the modeling of HTGS and the experimental results show that the approach can identify the HTGS satisfactorily with acceptable accuracy.
References
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Journal ArticleDOI
A fuzzy-logic-based approach to qualitative modeling
Michio Sugeno,T. Yasukawa +1 more
TL;DR: A general approach to quali- tative modeling based on fuzzy logic is discussed, which proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model.
Journal ArticleDOI
Phenomenological model for magnetorheological dampers
TL;DR: In this article, a model for controllable fluid dampers is proposed that can effectively portray the behavior of a typical magnetorheological (MR) damper and compared with experimental results for a prototype damper.
Journal ArticleDOI
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction
Nikola Kasabov,Qun Song +1 more
TL;DR: It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
Journal ArticleDOI
An approach to online identification of Takagi-Sugeno fuzzy models
TL;DR: An approach to the online learning of Takagi-Sugeno (TS) type models is proposed, based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning.
Journal ArticleDOI
Neuro-fuzzy rule generation: survey in soft computing framework
Sushmita Mitra,Yoichi Hayashi +1 more
TL;DR: This article proposes to bring the various neuro-fuzzy models used for rule generation under a unified soft computing framework, and includes both rule extraction and rule refinement in the broader perspective of rule generation.
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