Algorithms for clustering data
Algorithms for clustering data
A general scheme for multi-model controller using trust
Mathematics and Computers in Simulation - Special issue: signal processing and neural networks
k-means: a new generalized k-means clustering algorithm
Pattern Recognition Letters
Multi-model modelling and predictive control based on local model networks
Control and Intelligent Systems
Brief paper: Nonlinear multivariable adaptive control using multiple models and neural networks
Automatica (Journal of IFAC)
A fuzzy-neural multi-model for nonlinear systems identification and control
Fuzzy Sets and Systems
Identification of piecewise affine systems by means of fuzzy clustering and competitive learning
Engineering Applications of Artificial Intelligence
Fuzzy c-Means Algorithms for Data with Tolerance Using Kernel Functions
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
Clustering: A neural network approach
Neural Networks
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computational Biology and Chemistry
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
ASOD: Arbitrary shape object detection
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Hybrid-fuzzy modeling and identification
Applied Soft Computing
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The multimodel approach was recently developed to deal with the issues of complex systems modeling and control. Despite its success in different fields, it is still faced with several design problems, in particular the determination of the number and parameters of the different models representative of the system as well as the choice of the adequate method of validities computation used for multimodel output deduction. In this paper, a new approach for complex systems modeling based on both neural and fuzzy clustering algorithms is proposed, which aims to derive different models describing the system in the whole operating domain. The implementation of this approach requires two main steps. The first step consists in determining the structure of the model-base. For this, the number of models must be firstly worked out by using a neural network and a Rival Penalized Competitive Learning (RPCL). The different operating clusters are then selected referring to two different clustering algorithms (K-means and fuzzy K-means). The second step is a parametric identification of the different models in the base by using the clustering results for model orders and parameters estimation. This step is ended in a validation procedure which aims to confirm the efficiency of the proposed modeling by using the adequate method of validity computation. The proposed approach is implemented and tested with two nonlinear systems. The obtained results turn out to be satisfactory and show a good precision, which is strongly related to the dispersion of the data and the related clustering method.