基于自适应网络的量子模糊推理系统  

Adaptive Network-Based Quantum Fuzzy Inference System

作　　者：闫丽丽 颜金歌 张仕斌 YAN Lili;YAN Jinge;ZHANG Shibin(School of Cybersecurity,Chengdu University of Information Technology,Chengdu 610225)

机构地区：[1]成都信息工程大学网络空间安全学院,成都610225

出　　处：《电子科技大学学报》2023年第4期482-488,共7页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(62076042,62102049);四川省自然科学基金(2022NSFSC0535);四川省科技厅重点研发项目(2021YFSY0012,2021YFG0332);四川省量子安全通信创新团队项目(17TD0009)。

摘　　要：基于ANFIS与量子BP神经网络(QBP)提出了一种基于自适应网络的量子模糊推理系统(ANQFIS)。不同于ANFIS,ANQFIS以量子门旋转的方式将模糊规则强度与QBP相结合,最后以量子态的测量概率作为输出,QBP的加入使得模型的输出准确率更高,且凭借量子计算的速度优越性提升了模型的计算速度。根据梯度下降法,给出了该系统中参数的学习算法。在仿真实验中,分别使用低维数据和高维数据作为数据集来训练模型,使用攻击算法生成对抗样本进行测试,结果表明ANQFIS在输出准确率、鲁棒性方面优于ANFIS与QBP。In this paper,a quantum fuzzy inference system based on adaptive network(ANQFIS)is proposed based on ANFIS and quantum BP(QBP)neural network.Different from ANFIS,ANQFIS combines the strength of fuzzy rules with QBP in the way of quantum gate rotation,and finally takes the measurement probability of quantum states as the output.The addition of QBP makes the output accuracy of the model higher,and the calculation speed of the model is improved by virtue of the speed advantage of quantum computing.According to the gradient descent method,the parameters learning algorithm of the system is given.In the simulation experiment,low-dimensional data and high-dimensional data are used as data sets to train the model,and attack algorithms are used to generate adversarial examples for testing.The results show that ANQFIS is superior to ANFIS and QBP in output accuracy and robustness.

关 键 词：量子神经网络 模糊神经网络 量子机器学习 量子计算 量子模糊机器学习 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置]