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學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Access type: Metadata onlySubject: search.filters.subject.NEURAL NETWORKS

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    RELAXATION BY THE HOPFIELD NEURAL NETWORK
    (1992-02-01)
    YU, SS
    ;
    TSAI, WH
    ;
    資訊工程學系
    ;
    Department of Computer Science
    The relaxation process is a useful technique for using contextual information to reduce local ambiguity and achieve global consistency in various applications. It is basically a parallel execution model, adjusting the confidence measures of involved entities based on interrelated hypotheses and confidence measures. On the other hand, the neural network is a computational model with massively parallel execution capability. The output of each neuron depends mainly on the information provided by other neurons. Therefore, there exist certain common properties in the relaxation process and the neural network technique. A mapping method that makes the Hopfield neural network perform the relaxation process is proposed. By this method, the neural network technology can be easily adapted to solve the many problems which have already been solved by the relaxation process. An advantage of this is that the relaxation process can be performed in real time since the Hopfield network can be implemented by conventional analog circuits. Experimental results are given to demonstrate the feasibility of the proposed method by performing the image thresholding operation on the proposed neural network.
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    SOLVING SYSTEM PARTITIONING PROBLEM USING A MASSIVELY-PARALLEL BIO-COMPUTING NETWORK
    (1994)
    CHANG, RI
    ;
    HSIAO, PY
    ;
    交大名義發表
    ;
    資訊工程學系
    ;
    National Chiao Tung University
    In this paper a massively parallel bio-computing network model is presented for the system partitioning problem in VLSI CAD under timing and capacity constraints. Experiments show that the proposed method was able to find optimal solutions with fast feedback. The future research is to develop a neural network based CAD system for resolving time-consuming VLSI design problems.