NDLI: Reviewing and designing pre-processing units for RBF networks: initial structure identification and coarse-tuning of free parameters

Content Provider	Springer Nature Link
Author	Kayhan, Gökhan Ozdemir, Ali Ekber Emiglu, İlyas
Copyright Year	2012
Abstract	This paper reviews some frequently used methods to initialize an radial basis function (RBF) network and presents systematic design procedures for pre-processing unit(s) to initialize RBF network from available input–output data sets. The pre-processing units are computationally hybrid two-step training algorithms that can be named as (1) construction of initial structure and (2) coarse-tuning of free parameters. The first step, the number, and the locations of the initial centers of RBF network can be determined. Thus, an orthogonal least squares algorithm and a modified counter propagation network can be employed for this purpose. In the second step, a coarse-tuning of free parameters is achieved by using clustering procedures. Thus, the Gustafson–Kessel and the fuzzy C-means clustering methods are evaluated for the coarse-tuning. The first two-step behaves like a pre-processing unit for the last stage (or fine-tuning stage—a gradient descent algorithm). The initialization ability of the proposed four pre-processing units (modular combination of the existing methods) is compared with three non-linear benchmarks in terms of root mean square errors. Finally, the proposed hybrid pre-processing units may initialize a fairly accurate, IF–THEN-wise readable initial model automatically and efficiently with a minimum user inference.
Starting Page	1655
Ending Page	1666
Page Count	12
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	22
Issue Number	7-8
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-07-10
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Counter propagation network (CPN) Fuzzy C-means (FCM) Gustafson–Kessel (GK) Radial basis function (RBF) Hybrid training and modeling Partition validations Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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