System Identification and Prediction of Dynamic System and Microwave Thermal Process Using a Recurrent Fuzzy Quantum Neural Network

2018 2nd annual International Conference on Cloud Technology and Communication Engineering

计算机科学;无线电电子学

Li, Chaoyin^1,2 ; Xiong, Qingyu^1,3 ; Wang, Kai^1,2 ; Yu, Yang^1,3 ; Liu, Tong^1,2

Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, China^1

School of Automation, Chongqing University, Chongqing

400044, China^2

School of Big Data and Software, Chongqing University, Chongqing

401331, China^3

关键词: Dynamic system processing;    Fuzzy quantum neural network;    Gradient descent algorithms;    Identification precision;    Mahalanobis distances;    Microwave heating process;    Quantum neural networks;    Recurrent fuzzy neural network;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/466/1/012106/pdf
DOI  :  10.1088/1757-899X/466/1/012106

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.