Multilayer feedforward networks are universal approximators
Neural Networks
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
A constructive approach to neuro-fuzzy networks
Signal Processing - Special issue on neural networks
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Scale-based approach to hierarchical fuzzy clustering
Signal Processing - Special issue on fuzzy logic in signal processing
A pruning method for the recursive least squared algorithm
Neural Networks
Adaptive Control
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Soft Computing and Fuzzy Logic
IEEE Software
A fast learning algorithm for parismonious fuzzy neural systems
Fuzzy Sets and Systems - Information processing
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Simplification of fuzzy-neural systems using similarity analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organized fuzzy system generation from training examples
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
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
Adaptive resolution min-max classifiers
IEEE Transactions on Neural Networks
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
A novel pruning algorithm for self-organizing neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Non-uniform self-selective coder for fuzzy rules and its application
Expert Systems with Applications: An International Journal
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
An evolving-onstruction scheme for fuzzy systems
IEEE Transactions on Fuzzy Systems
Online self-reorganizing neuro-fuzzy reasoning in interval-forecasting for financial time-series
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
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
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Information Sciences: an International Journal
Modeling data uncertainty on electric load forecasting based on Type-2 fuzzy logic set theory
Engineering Applications of Artificial Intelligence
Fuzzy Control of a Helio-Crane
Journal of Intelligent and Robotic Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. A proof of the convergence of both the estimation error and the linear network parameters is also given in the paper. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and compact structure. Simulation work shows that the SOFNN has the capability of self-organization to determine the structure and parameters of the network automatically.