Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy Sets and Systems - Special issue on diagnostics and control through neural interpretations of fuzzy sets
A self-organizing neural-network-based fuzzy system
Fuzzy Sets and Systems
A self-generating method for fuzzy system design
Fuzzy Sets and Systems
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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This paper describes an improved self-organizing CPN-based (Counter-Propagation Network) fuzzy system. Two self-organizing algorithms IUSOCPN and ISSOCPN, being unsupervised and supervised respectively, are introduced. The idea is to construct the neural-fuzzy system with a two-phase hybrid learning algorithm, which utilizes a CPN-based nearest-neighbor clustering scheme for both structure learning and initial parameters setting, and a gradient descent method with adaptive learning rate for fine tuning the parameters. The obtained network can be used in the same way as a CPN to model and control dynamic systems, while it has a faster learning speed than the original back-propagation algorithm. The comparative results on the examples suggest that the method is fairly efficient in terms of simple structure, fast learning speed, and relatively high modeling accuracy.