神經回路網을 이용한 知能形 制御시스템의 制御機 設計 및 具現에 관한 硏究

Abstract

An artificial neural network is an information-processing system that has certain performance characteristics in common with biological neural networks. Artificial neural networks have been developed as generalizations of mathematical models of human cognition or neural biology, based on the assumptions.

In this study, this system makes use of the analog sensor and converts the feature of fish outline when sensor is operating with CPU(80C196KC). Then, after signal processing, this feature is classified a special feature and a outline of fish by using the neural network, one of the artificial intelligence scheme. This neural network classifies fish pattern of very simple and short calculation. This has linear activation function and the errror back propagation is used as a learning algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long when random initial weights are used, off-line learning is induced to decrease the progress time

An "Fillet machines" is a fillet extracting-tail cutting machine that is commonly used in the fish processing industry.

Millions of dollars worth of "pollack" are wasted annually due to inaccurate fillet cutting using these somewhat outdated machines. The main cause of wastage is the "over-feed problem". This occurs when a pollack is inaccurately positioned with point to the cutter blade so that the cutting location is into fillet of a pollack. An effort has been made to correct this situation by sensing the position of the fillet using sensors accordingly.

We confirmed this method has better performance than somewhat outdated machines.