Adaptive signal processing
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neural network design
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
Noise reduction by fuzzy image filtering
IEEE Transactions on Fuzzy Systems
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An intelligent learning-based approach using neural network and fuzzy logic to the problem of interference canceling is proposed in the paper. The famous signal-processing structure of adaptive noise canceling is used for the research of interference signal canceling, in which a neuro-fuzzy system is used as the adaptive notch filter. Four T-S fuzzy rules are in the neuro-fuzzy filter. The filter integrates the adaptation capability of neural network and the inference ability of fuzzy logic, so that the signal-processing policy and the meaning of learning are transparent. To explore the excellent nonlinear mapping ability of the neurofuzzy adaptive, an appropriate machine-learning algorithm has to be used for the learning purpose, so that the optimal or near-optimal parameter set of the neuro-fuzzy filter can be obtained. The well-known Random Optimization (RO) algorithm and the famous Least Square Estimate (LSE) algorithm are used in hybrid way for the filter. To demonstrate the proposed approach, an exemplar experiment is implemented. The proposed neurofuzzy adaptive filter shows great filtering performance. A good discussion for the approach is given.