Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Structural Learning with M-Apoptosis in Neurofuzzy GMDH
Takashi OHTANIHidetomo ICHIHASHITetsuya MIYOSHIKazunori NAGASAKAYoshihiko KANAUMI
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1998 Volume 11 Issue 5 Pages 251-260

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Abstract
It is known in the physiology that the apoptosis is an active form of cell death in most multicellular organisms and one of the two mechanisms by which cell death occurs (the other being the pathological process of necrosis). Apoptosis is the mechanism responsible for the physiological deletion of cells and appears to be intrinsically programmed. We propose a procedure called M-apoptosis for the structure clarification of Neurofuzzy GMDH model whose partial descriptions are represented by the Radial Basis Functions network. The proposed method prunes the larger network to identify, still more to clarify the network structure by minimizing the Minkowski norm of the gradient with respect to input variables of the partial descriptions. The method was validated through graphical representations of the identified structures in the numerical example of function approximation and classification of Iris data.
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