Q'tron Neural Networks for Constraint Satisfaction

This paper proposes the methods to solve the constraint satisfaction problems (CSPs) using Q'tron neural networks (NNs). A Q'tron NN is local-minima free if it is built as a known-energy system and is incorporated with the proposed persistent noise-injection mechanism. The so-built Q'tron NN, as a result, will settle down if and only if a feasible solution is found. Additionally, such a Q'tron NN is intrinsically auto-reversible. This renders the NN operable in a question-answering mode for extracting interested information. A concrete example, i.e., to solve the N-queen problem, will be demonstrated to highlight the main concept.

• Type: Conference
• Year: 2004
• Where: HIS
• Authors: Tai-Wen Yue, Mei-Ching Chen
• Keywords: Constraint Satisfaction Problems, HIS 2004, HIS 2007, Neural Networks, Persistent Noise-injection Mechanism