ScholarLens Back to search
2008•Unpublished venueRequires access

The learning convergence of CMAC in frequency domain and a modified algorithm

Zhang Lei, Cao Qixin

Open publisher page 0 citations

Abstract

The analysis on the learning convergence of CMAC in frequency domain is firstly extended to a more general case where the training samples are evenly distributed in the quantitative range and the learning rate is other than one. The convergence condition is presented and the influence of the learning rate beta on the convergence range is analyzed. If 0< beta<1, CMAC is convergent in the whole frequency domain. If 1lesbeta<2, the convergence of CMAC will become more unstable with beta becoming larger. To overcome this problem, a modified algorithm is proposed and simulation results prove the stability of CMAC can be improved significantly.

About this research paper

What this paper is about

The analysis on the learning convergence of CMAC in frequency domain is firstly extended to a more general case where the training samples are evenly distributed in the quantitative range and the learning rate is other than one. The convergence condition is presented and the influence of the learning rate beta on the convergence range is analyzed. If 0< beta<1, CMAC is convergent in the whole frequency domain. If 1lesbeta<2, the convergence of CMAC will become more unstable with beta becoming larger. To overcome this problem, a modified algorithm is proposed and simulation results prove the stability of CMAC can be improved significantly.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The analysis on the learning convergence of CMAC in frequency domain is firstly extended to a more general case where the training samples are evenly distributed in the quantitative range and the learning rate is other than one. The convergence condition is presented and the influence of the learning rate beta on the convergence range is analyzed. If 0< beta<1, CMAC is convergent in the whole frequency domain. If 1lesbeta<2, the convergence of CMAC will become more unstable with beta becoming larger. To overcome this problem, a modified algorithm is proposed and simulation results prove the stability of CMAC can be improved significantly.

Key concepts: Convergence (economics), Stability (learning theory), Frequency domain, Rate of convergence, Range (aeronautics), Computer science, Domain (mathematical analysis), Algorithm

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
The learning convergence of CMAC in frequency domain and a modified algorithm — Research Paper | ScholarLens