Title :
Modular neural network architecture using piece-wise linear mapping
Author :
Subbarayan, Saravanan ; Kim, Kyung K. ; Manry, Michael T. ; Devarajan, Venkat ; Chen, Hung-Han
Author_Institution :
Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA
Abstract :
A new modular neural network for functional mapping is presented. A training algorithm for the network is presented which employs a clustering method, a weighted distance measure, and the deign of simple modules. Since the individual modules are linear, the network implements a piece-wise linear mapping. The efficiency of this structure in terms of training time and pattern storage capacity is discussed and the results of comparative performances with the multilayer preceptron, is presented. Examples are provided to verify the properties of the modular network.
Keywords :
approximation theory; learning (artificial intelligence); modules; multilayer perceptrons; neural net architecture; piecewise-linear techniques; clustering method; efficiency; functional approximation; functional mapping; linear modules; modular network; modular neural network architecture; module design; multilayer preceptron; pattern storage capacity; piecewise linear mapping; training algorithm; training time; weighted distance measure; Aircraft propulsion; Clustering algorithms; Clustering methods; Multi-layer neural network; Neural networks; Piecewise linear techniques; Postal services; Unsupervised learning; Vectors; Weight measurement;
Conference_Titel :
Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-7646-9
DOI :
10.1109/ACSSC.1996.599129
