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A Constraint Satisfaction Neural Network

GUO Bao-long; GUO Lei; DAI Guan-Zhong (Dept. of Measurement Control and Instrument, Xidian University Xi'an 710071, China; Dept. of Automatic Control, Northwest Polytechnic University, Xi'an 710072, China)  
Although the operating of a general artificial neural network (NN) can eliminate the superior contradiction, its solution still contains the inferior contradiction [8~11] which is unacceptable for actual problems such as the constraint satisfaction problems (CSPs) that contain contradictions. For this reason, a constraint satisfaction neural network (CSNN) based on the logical analytic theory of NNs is presented,which is composed of a general need network named a basic network and a coned system which has the adaptability to cope with complex problems.The former provides the necessary constraints and the lathe gives the closed-open control for the constraints inside the basic network. Since contradictions in many natural problems are often inevitable, the property of the CSNN is of great significance for intelligent problems.
【Fund】: 国家自然科学基金;; 国家高技术计划!(863)
【CateGory Index】: TP183
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