Adaptive signal processing
Multilayer feedforward networks are universal approximators
Neural Networks
Fuzzy engineering
Fuzzy control of unknown multiple-input—multiple-output plants
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A TSK-type neurofuzzy network approach to system modeling problems
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
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Design of adaptive filter using Jordan/Elman neural network in a typical EMG signal noise removal
Advances in Artificial Neural Systems
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A new intelligent noise filtering approach using Computational Intelligence (CI) is proposed for the problem of adaptive noise cancellation (ANC). Since the traditional linear filtering may not be good enough to handle with the noise complexity and time-varying statistic property, a self-constructing neuro-fuzzy system (SCNFS) is used as an adaptive filter to deal with the nonlinearity of noise. A hybrid machine learning algorithm with the methods of both random optimization algorithm (RO) and least square estimation (LSE) is introduced to enable the SCNFS with learning capability. The learning capability includes both the parameter learning and the structure learning. In the parameter learning phase, the premises and the consequents of the SCNFS are updated by RO and LSE, respectively. In the SCNFS structure learning, the system structure can be generated or rearranged using the proposed mechanism with rule-splitting and/or rule-expanding. To demonstrate the feasibility and the capability of the proposed approach, an example of adaptive speech noise cancellation is illustrated. With the experimental results, the SCNFS shows excellent filtering performance for noise cancellation.