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
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Convex incremental extreme learning machine
Neurocomputing
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An empirical comparison of nine pattern classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Approximation theory of fuzzy systems-MIMO case
IEEE Transactions on Fuzzy Systems
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Channel equalization using neural networks: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Clustering in extreme learning machine feature space
Neurocomputing
Aircraft recognition using modular extreme learning machine
Neurocomputing
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In this correspondence, an online sequential fuzzy extreme learning machine (OS-Fuzzy-ELM) has been developed for function approximation and classification problems. The equivalence of a Takagi-Sugeno-Kang (TSK) fuzzy inference system (FIS) to a generalized single hidden-layer feedforward network is shown first, which is then used to develop the OS-Fuzzy-ELM algorithm. This results in a FIS that can handle any bounded nonconstant piecewise continuous membership function. Furthermore, the learning in OS-Fuzzy-ELM can be done with the input data coming in a one-by-one mode or a chunk-by-chunk (a block of data) mode with fixed or varying chunk size. In OS-Fuzzy-ELM, all the antecedent parameters of membership functions are randomly assigned first, and then, the corresponding consequent parameters are determined analytically. Performance comparisons of OS-Fuzzy-ELM with other existing algorithms are presented using real-world benchmark problems in the areas of nonlinear system identification, regression, and classification. The results show that the proposed OS-Fuzzy-ELM produces similar or better accuracies with at least an order-of-magnitude reduction in the training time.