NEURAL NETWORK PREDICTION MODELING FOR A CONTINUOUS, SNACK FOOD FRYING PROCESS

Automatic control is a primary concern of a continuous, snack food frying process. For the purpose of controlling product quality, two neural network paradigms were applied to develop prediction models to deal with the complexity of the process. Based on the modeling assumptions of the process, the neural network one-step-ahead and multiple-step-ahead predictors were established mathematically, the training algorithms for the two network predictors were developed, and a procedure for network prediction model identification was established. Results of model identification and predictions of the continuous, snack food frying process were presented in one-step-ahead and multiplestep- ahead modes. Prediction models developed in this article are ready for development of control loops.