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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
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
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The Development of Incremental Models
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
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In this paper, we propose a new learning approach for designing an incremental model that has a cascade learning structure combined with a rough and fine tuning method for the learning scheme. Recently, various fuzzy logic-based modeling methods, with fuzzy if-then type rules, have been proposed in an attempt to obtain good approximations and generalization performances. In contrast to these various modeling methods, the new proposed incremental modeling scheme presented here is combined with a rough and fine tuning scheme, to learn and construct the best architecture for the model. A compensation idea is introduced in the fine tuning stage to solve the over-fitting problem caused from testing data. For this purpose, a construct of an extreme learning machine (ELM) is used as a global model, and this is compensated through a conditional fuzzy C-means (CFCM)-based fuzzy inference system (FIS) with a Takagi-Sugeno-Kang (TSK)-type method, which captures the remaining localized nonlinearities of the model. The experimental results, obtained by the proposed model have proved to show better performances in comparison with previous works.