A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory

Authors:
Xiangping Meng;Jianzhong Wang;Yuzhen Pi;Quande Yuan
Affiliations:
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
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2009

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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.