“Fast learning in multi-resolution hierarchies”
Advances in neural information processing systems 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering
Neural Processing Letters
A fuzzy CMAC model for color reproduction
Fuzzy Sets and Systems
Improving the fuzzy system performance by fuzzy system ensemble
Fuzzy Sets and Systems
Decomposition of a complex fuzzy controller for the truck-and-trailer reverse parking problem
Mathematical and Computer Modelling: An International Journal
A multi-context processor for real-time concurrent tasks fuzzy reasoning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper proposes a CMAC-based fuzzy logic controller (FLC) with a fast learning capability and an accurate approximation ability. The proposed CMAC-based FLC has the fast learning capability because it pursuits the local generalization and only a small number of activated units in the network are participated in the forward and backward computation. It also produces an accurate input-output approximation ability, because it adjusts the MF's model parameters of the input and output variables simultaneously and it considers both centers and widths of output membership functions to compute a crisp defuzzified value. Application to the truck backer-upper control problem of the proposed CMAC-based FLC is presented. Simulation results validate the fast learning and the accurate approximation of the proposed CMAC-based FLC.