A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory. (27th January 2009)

• Record Type: Journal Article
• Authors: Meng, Xiangping; Wang, Jianzhong; Pi, Yuzhen; Yuan, Quande
• Is Part Of: International journal of intelligent systems technologies and applications. Volume 6:Number 1/2(2009)
• Page Start: 50
• Page End: 60
• Publication Date: 2009-01-27
• Subjects: ANNa -- MAS -- Q-learning -- quantum computing -- artificial neural networks -- neural network training -- multi-agent systems -- agent-based systems -- reinforcement learning; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 006.3
• Languages: English
• ISSNs: 1740-8865

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.