Title: A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory
Authors: Xiangping Meng, Jianzhong Wang, Yuzhen Pi, Quande Yuan
Addresses: Department of Electrical Engineering, Changchun Institute of Technology, 130012, PR China. ' Department of Information Engineering, Northeast Dianli University, 132012, PR China. ' Department of Electrical Engineering, Changchun Institute of Technology, 130012, PR China. ' Department of Electrical Engineering, Changchun Institute of Technology, 130012, PR China
Abstract: Artificial Neural Networks (ANNs) are powerful tools that can be used to model and investigate various complex and non-linear phenomena. In this study, we construct a new ANN, which is based on Multi-Agent System (MAS) theory and quantum computing algorithm. All nodes in this new ANN are presented as Quantum Computational (QC) agents, and these agents have learning ability. A novel ANN training method was proposed via implementing QCMAS reinforcement learning. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show that this method is effective.
Keywords: ANNa; MAS; Q-learning; quantum computing; artificial neural networks; neural network training; multi-agent systems; agent-based systems; reinforcement learning.
International Journal of Intelligent Systems Technologies and Applications, 2009 Vol.6 No.1/2, pp.50 - 60
Available online: 25 Jan 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article