NDLI: The performance of a modified MEBML system in a noisy environment

Content Provider	IEEE Xplore Digital Library
Author	Chengyi Sun Lijun Wei Yan Sun
Copyright Year	1999
Description	Author affiliation: Comput. Centre, Taiyuan Univ. of Technol., China (Chengyi Sun)
Abstract	Mind-evolution based machine learning (MEBML) is a new evolution learning method that was proposed recently by the authors (1998). MEBML substitutes similartaxis and dissimilation for crossover and mutation operators used in GA. MEBML can solve numerical problems as well as non-numerical ones. In this paper, a new form of similartaxis, called fitted similartaxis, is put forward. In fitted similartaxis, individual's data in the groups are fitted and the positions of new winners of groups are estimated. Using the least square method in the process of fitting, the influence of noise in the target function is eliminated and the speed of similartaxis is improved. In MEBML, the improvements of speed of similartaxis and dissimilation both help to improve the convergence of the algorithm. Experiments show that MEBML with fitted similartaxis can get high accurate solution of global optima without increasing much of the calculating cost.
Starting Page	613
Ending Page	617
File Size	438963
Page Count	5
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.815622
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Working environment noise Testing Costs Sun Machine learning Learning systems Noise measurement Performance evaluation Genetic mutations Least squares methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Genetic algorithms in noisy environment

Bearings-only location using nonlinear least squares

Automated Registration Evaluation System (ARES)

Self-modifying reinforcement learning

Chaotic system reconstruction from noisy time series measurements using improved least squares genetic programming

Induction as optimization

Application of adaptive noise cancelling in a noisy reverberant environment

Unbiased bilinear equation error system identification

Feature Normalization via ANN/HMM Inversion for Speech Recognition Under Noisy Conditions

The performance of a modified MEBML system in a noisy environment

Similar Documents

Genetic algorithms in noisy environment

Bearings-only location using nonlinear least squares

Automated Registration Evaluation System (ARES)

Self-modifying reinforcement learning

Chaotic system reconstruction from noisy time series measurements using improved least squares genetic programming

Induction as optimization

Application of adaptive noise cancelling in a noisy reverberant environment

Unbiased bilinear equation error system identification

Feature Normalization via ANN/HMM Inversion for Speech Recognition Under Noisy Conditions

The performance of a modified MEBML system in a noisy environment