Hierarchical clustering for efficient memory allocation in CMAC neural network

Authors:
Sintiani D. Teddy;Edmund M.-K. Lai
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

CMAC Neural Network is a popular choice for control applications. One of the main problems with CMAC is that the memory needed for the network grows exponentially with each addition of input variable. In this paper, we present a new CMAC architecture with more effective allocation of the available memory space. The proposed architecture employs hierarchical clustering to perform adaptive quantization of the input space by capturing the degree of variation in the output target function to be learned. We showed through a car maneuvering control application that using this new architecture, the memory requirement can be reduced significantly compared with conventional CMAC while maintaining the desired performance quality.