Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
FCMAC: a fuzzified cerebellar model articulation controller with self-organizing capacity
Automatica (Journal of IFAC)
CMAC with general basis functions
Neural Networks
Kernel CMAC with Reduced Memory Complexity
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Kernel CMAC With Improved Capability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Cerebellar Model Articulation Controller (CMAC) has some attractive features: fast learning capability and the possibility of efficient digital hardware implementation. These features makes it a good choice for different control applications, like the one presented in this paper. The problem is to navigate a mobile robot (e.g a car) from an initial state to a fixed goal state. The approach applied is backpropagation through time (BPTT). Besides the attractive features of CMAC it has a serious drawback: its memory complexity may be very large. To reduce memory requirement different variants of CMACs were developed. In this paper several variants are used for solving the navigation problem to see if using a network with reduced memory size can solve the problem efficiently. Only those solutions are described in detail that solve the problem in an acceptable level. All of these variants of the CMAC require higher-order basis functions, as for BPTT continuous input-output mapping of the applied neural network is required.