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APPLICATION OF ARTIFICIAL FISH-SWARM NEURAL NETWORK IN COILING TEMPERATURE FORECASTING OF HOT ROLLED STRIP |
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Abstract: The coiling temperature forecasting, as a non-linear optimal problem, is very important to the performance of hot rolled strip products. Based on the individual local searching, the Artificial Fish-swarm Algorithm(AFSA) is a new optimal strategy, with good capability to avoid the local extremum and obtain the global extremum. In this paper, an Artificial Neural Network(ANN) based the forecasting model of AFSA is proposed, with the weights being trained by AFSA, and the neural network of AFSA being applied to coiling temperature forecasting. Applying the forecasting method to a certain actual hot rolled strip, it is shown that comparing with the traditional BP neural network forecasting method, the presented forecasting method has better adaptive ability and can give better forecasting results. The artificial fish-swarm algorithm network is trained and checked with the actual production data. The result indicates that the method can predict the strip coiling temperature in real-time.
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Received: 13 October 2009
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