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Proceedings Paper

ANN learning algorithm using the offset control parameter
Author(s): Chun-Hwan Lim; Younggil Shin; Jaimin Ryu; Jong-An Park
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Paper Abstract

A common concern of neural network models has been the problem of relating the function of complex systems of neurons to what is known of individual neurons, their interconnections and offsets. In this paper, we propose a new model of neural networks that can control and produce the offset patterns of the input layer, the hidden layer, and the output layer neurons. It consists of the input layer for the signal patterns, the hidden layer for the offset patterns production, and memory part between the hidden layer and the output layer. The output of neurons is calculated by the offsets control parameter Rofj. The input layer calculates the input patterns to be learned so that the proposed neural network can control and produce the offset patterns, and sends the results to the next layer. The hidden layer produces the offset patterns after receiving the pattern information from the input layer, and it sends the output information of the hidden layer to the memory part. The memory part stores the learned output patterns of the hidden layer after comparing it with the input pattern, and sends the stored information to the output layer after the entire learning. Simulation results show that the proposed neural network can produce the offset patterns and it can be efficiently applied in the logic circuit design and pattern classification.

Paper Details

Date Published: 25 March 1998
PDF: 9 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998);
Show Author Affiliations
Chun-Hwan Lim, Chosun Univ. (South Korea)
Younggil Shin, Chosun Univ. (South Korea)
Jaimin Ryu, Dongshin College (South Korea)
Jong-An Park, Chosun Univ. (South Korea)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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