SIAM Journal on Matrix Analysis and Applications
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The restricted total least squares problem: formulation, algorithm, and properties
SIAM Journal on Matrix Analysis and Applications
When are Simple LS Estimators Enough? An Empirical Study of LS, TLS, and GTLS
International Journal of Computer Vision
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
Extended generalized total least squares method for theidentification of bilinear systems
IEEE Transactions on Signal Processing
Knowledge bounded least squares method for the identification of fuzzy systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Modeling, identification, and control of a class of nonlinear systems
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
Local model network based dynamic battery cell model identification
IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing
Local model network identification for online engine modelling
Information Sciences: an International Journal
Optimal experiment design based on local model networks and multilayer perceptron networks
Engineering Applications of Artificial Intelligence
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In this paper, nonlinear system identification utilizing generalized total least squares (GTLS) methodologies in neurofuzzy systems is addressed. The problem involved with the estimation of the local model parameters of neurofuzzy networks is the presence of noise in measured data. When some or all input channels are subject to noise, the GTLS algorithm yields consistent parameter estimates. In addition to the estimation of the parameters, the main challenge in the design of these local model networks is the determination of the region of validity for the local models. The method presented in this paper is based on an expectation-maximization algorithm that uses a residual from the GTLS parameter estimation for proper partitioning. The performance of the resulting nonlinear model with local parameters estimated by weighted GTLS is a product both of the parameter estimation itself and the associated residual used for the partitioning process. The applicability and benefits of the proposed algorithm are demonstrated by means of illustrative examples and an automotive application.