This paper first establishes a neural network model for logic circuits from the truth table by using linear equations theory, presents a kind of ATPG neural network model, and investigates energy local minima for the network. And then, it proposes the corresponding techniques to reduce the number of energy local minima as well as some approaches to escaping from local minimum of energy. Finally, two simulation systems, the binary ATPG neural network and the continuous ATPG neural network, are implemented on SUN 3/260 workstation in C language. The experimental results and their analysis and discussion are given. The preliminary experimental results show that this method is feasible and promising.
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Supported in part by National Natural Science Foundation of China.
Zhang Zhong received his M.S. degree in electronics from the University of Electronic Science and Technology of China in 1988 and his Ph.D. degree in computer science from the Institute of Computing Technology, the Chinese Academy of Sciences in 1991. He is currently an Associate Professor of Computer Science in the National Research Center for Intelligent Computing Systems (NCIC), the Chinese Academy of Sciences. His research interests include neural networks, evolutionary computation and its applications, genetic algorithms, nonlinear dynamical systems, parallel and distributed computing, artificial intelligence, pattern recognition and so on.
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Zhang, Z. Simulation of ATPG neural network and its experimental results. J. of Comput. Sci. & Technol. 10, 310 (1995). https://doi.org/10.1007/BF02943500
- Neural networks
- logic circuits
- automatic test pattern generation (ATPG)
- local minimum
- linear equations theory