Abstract
Identification problem of furnace heat patterns by using a neural network is described. The neural network used in this paper can identify a nonlinear system whose structure is very large and complex. For the application to practical identification problems, it is necessary to construct a neural network which is very accurate and effective for practical use. In this paper, by using a prediction error for new data which are not used for learning, a suitable neural network structure and a suitable number of learning are found out, and a neural network which is very effective for identification of furnace heat patterns is constructed. The prediction results obtained by using the neural network is compared with the results obtained by using GMDH (Group Method of Data Handling) models. It is shown that the neural network in this paper gives better prediction results as compared with GMDH models.
