System Identification and Prediction of Dynamic System and Microwave Thermal Process Using a Recurrent Fuzzy Quantum Neural Network
Chaoyin Li, Qingyu Xiong, Kai Wang, Yang Yu, Tong Liu · IOP Conference Series Materials Science and Engineering · 2018
Microwave heating is a time-varying, non-linear process. Mechanism modeling of the microwave thermal process is extremely difficult because of the complex microwave heating environment. This paper presents a recurrent fuzzy quantum neural network with full feedbacks (RFQNN) for prediction and identification of dynamic systems and the actual microwave heating process. In the RFQNN, a quantum neural network is introduced to the consequent part of the fuzzy rules to improve the mapping ability and the identification precision. All of the rules are generated and learned online through a simultaneous structure and parameter learning. During the structure learning, an online clustering algorithm combined with Mahalanobis distance elimination algorithm perform effectively in generating or removing fuzzy rules. And then a gradient descent algorithm is introduced to update the parameters during the parameter learning process. And finally, we test the RFQNN by dynamic plants and the microwave thermal process. The results show that it performs well in dynamic system processing compared with other recurrent fuzzy neural networks.