“Fast learning in multi-resolution hierarchies”
Advances in neural information processing systems 1
A self-organizing associative memory system for control applications
Advances in neural information processing systems 2
Operational fault tolerance of CMAC networks
Advances in neural information processing systems 2
Brains, Behavior and Robotics
On the Training of a Kolmogorov Network
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Incremental personalized web page mining utilizing self-organizing HCMAC neural network
Web Intelligence and Agent Systems
Minimal Structure of Self-Organizing HCMAC Neural Network Classifier
Neural Processing Letters
Adaptive Growing Quantization for 1D CMAC Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
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This paper shows that the Cerebellar Model Articulation Controller (CMAC) is structurally similar to networks derived from a theorem of Kolmogorov. As a foundation for this comparison, we review of a proof of Kolmogorov's theorem. From this proof and an analysis of the CMAC we derive two lemmas describing functions that cannot be modeled by a CMAC. The first lemma states that such functions have zero average value over response regions of CMAC association cells. The second lemma states that such functions have local oscillations exceeding a quantifiable percentage of the global maximum absolute value of error. This second lemma gives bounds on errors caused by hash tables used as association cells in the CMAC. We present three examples illustrating the lemmas.