A Modified CMAC Algorithm Based on Credit Assignment

Autor: Lei Zhang, Qixin Cao, Jay Lee, Yanzheng Zhao
Rok vydání: 2004
Předmět:
Zdroj: Neural Processing Letters. 20:1-10
ISSN: 1370-4621
DOI: 10.1023/b:nepl.0000039430.04088.78
Popis: A Credit-Assignment CMAC (CA-CMAC) algorithm is proposed to reduce learning interference in conventional CMAC. In the proposed CA-CMAC, the error of the training sample distributed to the addressed memory cell is proportional to the cell's credibility, which is the inverse of the cell's activated times. The learning process of CA-CMAC is analyzed and conventional CMAC is proved to be a special case of CA-CMAC. Furthermore, the convergence properties of CA-CMAC both in batch learning and in incremental learning are investigated; meanwhile, the convergence theorems in the two learning schemes are obtained, respectively. Finally, simulations are carried out to testify the theorems and compare the performance of CA-CMAC with that of CMAC. Simulation results prove that CA-CMAC converges faster than conventional CMAC.
Databáze: OpenAIRE