A resource-allocating network for function interpolation
Neural Computation
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Robust adaptive control
A course in fuzzy systems and control
A course in fuzzy systems and control
Stability of Adaptive Controllers
Stability of Adaptive Controllers
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Adaptive Filtering Prediction and Control
Adaptive Filtering Prediction and Control
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
A TSK fuzzy inference algorithm for online identification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameterlearning TSK model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
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
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm
IEEE Transactions on Fuzzy Systems
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Identification of evolving fuzzy rule-based models
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Neural-network construction and selection in nonlinear modeling
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Hybrid intelligent modeling schemes for heart disease classification
Applied Soft Computing
Adaptive fuzzy control of aircraft wing-rock motion
Applied Soft Computing
Evolving intelligent algorithms for the modelling of brain and eye signals
Applied Soft Computing
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In this paper, an online self-organizing fuzzy modified least-square (SOFMLS) network is proposed. The algorithm has the ability to reorganize the model and adapt itself to a changing environment where both the structure and learning parameters are performed simultaneously. The network generates a new rule if the smallest distance between the new data and all the existing rules (the winner rule) is more than a prespecified radius. The major contributions of this paper are as follows: 1) A new network is proposed, in which unidimensional membership functions are used, and only two parameters for each rule are employed, thus reducing the number of parameters. The network avoids the singularity produced by the widths in the antecedent part for online learning; 2) a new pruning algorithm based on the density is proposed, where the density is the number of times each rule is used in the algorithm. The rule that has the smallest density (the looser rule) in a selected number of iterations is pruned if the value of its density is smaller than a prespecified threshold; and 3) the stability of the proposed algorithm is proven, and the bound for the average of the identification error is found. The condition that led the algorithm to avoid the local minimum is found, and it is proven that the parameter error is bounded by the initial parameter error. Three simulations give the effectiveness of the suggested algorithm.