A Learning Algorithm of CMAC Based on RLS
Neural Processing Letters
A Modified CMAC Algorithm Based on Credit Assignment
Neural Processing Letters
Continuous CMAC-QRLS and Its Systolic Array
Neural Processing Letters
Minimal Structure of Self-Organizing HCMAC Neural Network Classifier
Neural Processing Letters
Design and analysis of direct-action CMAC PID controller
Neurocomputing
Closed-loop method to improve image PSNR in pyramidal CMAC networks
International Journal of Computer Applications in Technology
Stock Prediction Using FCMAC-BYY
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
CMAC-Based PID Control of an XY Parallel Micropositioning Stage
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A novel associative memory approach to speech enhancement in a vehicular environment
Expert Systems with Applications: An International Journal
CMAC-based compensator for limiting bound required in supervisory control systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Vibration control of suspension system based on a hybrid intelligent control algorithm
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Diagnosis of liver disease by using CMAC neural network approach
Expert Systems with Applications: An International Journal
ART-type CMAC network classifier
Neurocomputing
Eigenanalysis of CMAC neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A balanced learning CMAC neural networks model and its application to identification
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
The improved CMAC model and learning result analysis
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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CMAC is one useful learning technique that was developed two decades ago but yet lacks adequate theoretical foundation. Most past studies focused on development of algorithms, improvement of the CMAC structure, and applications. Given a learning problem, very little about the CMAC learning behavior such as the convergence characteristics, effects of hash mapping, effects of memory size, the error bound, etc. can be analyzed or predicted. In this paper, we describe the CMAC technique with mathematical formulation and use the formulation to study the CMAC convergence properties. Both information retrieval and learning rules are described by algebraic equations in matrix form. Convergence characteristics and learning behaviors for the CMAC with and without hash mapping are investigated with the use of these equations and eigenvalues of some derived matrices. The formulation and results provide a foundation for further investigation of this technique