A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
A resource-allocating network for function interpolation
Neural Computation
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
A real-time clustering microchip neural engine
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fuzzy engineering
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A hybrid clustering and gradient descent approach for fuzzymodeling
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
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Identification of evolving fuzzy rule-based models
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
An ART1 microchip and its use in multi-ART1 systems
IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Evolving fuzzy systems (EFSs) use online learning to extract knowledge from data, perform a high-level adaptation of the network structure, and learn parameters. In this paper, we describe the performance of an EFS that is called similarity mapping, where the training pairs (xi and zi) are compressed into input and output clusters. The predictive error is minimized using a procedure that is very similar to the one implemented in fuzzy-adaptive resonance theory map (ARTMAP) and resource-allocation networks. However, in the recall phase, a fuzzy membership grade is calculated for each input cluster and used in the weighting of the output clusters to obtain the final output vector. By modifying the spread of the cluster membership function, different approximative interpolating functions can be implemented. A similarity function, which was initially proposed for ART 1 implementations, is extended to the processing of analog vectors and used to calculate the membership grades of the input clusters. Several examples showthe behavior of the network, as well as its capability to classify, eliminate noise, and predict chaotic time series.