Fuzzy neural networks-based quality prediction system for sintering process

In this paper, based on the property of sintering process, a hybrid fuzzy neural networks (FNN) and genetic algorithm (GA) system is proposed to solve the difficult and challenging problem of constructing a system model from the given input and output data to predict the quality of chemical components of the finished sinter mineral. A bidirectional fuzzy neural network (BFNN) is proposed to represent the fuzzy model and realize the fuzzy inference. The learning process of BFNN is divided into off-line and on-line learning. In off-line learning, the GA is used to train the BFNN and construct a system model based on the training data. During on-line operation, the algorithm inherited from the principle of backpropagation is used to adjust the network parameters and improve the system precision in each sampling period. The process of constructing a system model is introduced in details. The results obtained from the actual prediction demonstrate that the performance and capability of the proposed system are superior.