NDLI: Study on Commercial Bank Branches Performance Evaluation Using Self-Adaptive RBFNN and UDM

Content Provider	IEEE Xplore Digital Library
Author	Diao XiaoHua Kang Shiying
Copyright Year	2010
Abstract	The commercial bank branches performance evaluation indicator system and it’s detail normalization piecewise function are established in the paper. By using scientific Uniform Design method(UDM), the large number of representative uniformly distributed samples are designed for training RBFNN and solving the problem of RBFNN model’s poor generalization ability. The experiments show the result of self-adaptive RBFNN evaluation is very close to the expected result of the experts fuzzy comprehensive evaluation(FCE). The evaluation method realizes the self-adaptive and non-linear approaching ability, meantime conquers the capability limitation of traditional BP network and non-preciseness of lacking experiment design, and avoids the subjectivity and uncertainty of traditional evaluation.
Starting Page	566
Ending Page	569
File Size	297808
Page Count	4
File Format	PDF
ISBN	9781424491148
DOI	10.1109/CIS.2010.129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Training Design methodology nearest neighbor-clustering algorithm(NNCA) Adaptation model Commercial bank branches performance evaluatio Clustering algorithms Artificial neural networks The generalization ability Indexes Radial Basis Function neural network (RBFNN)
Content Type	Text
Resource Type	Article

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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
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