用于数字式细胞神经网络设计的学习算法
LEARNING ALGORITHM FOR THE DESIGN OF DIGITAL CELLULAR NEURAL NETWORK
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摘要: 本文针对数字式细胞神经网络(DCNN)的应用,给出了DCNN模板的设计方法,即不等式构造法,并提出一个基于松弛法的DCNN有教师学习算法,为DCNN的设计提供了理论根据。在连通片检测等应用中的模拟表明了算法的有效性和正确性。Abstract: The application of cellular neural networks is determined by its templates. In accordance with the application of digital cellular neural network the author proposed before, a method for the design of its templates, i. e., inequalities construcion method, is given and a supervised learning algorithm is proposed based on the relaxation method. The learning algorithm provides theoretical basis for the design of DCNN. Simulation results on examples such as connected component detection shows the effectiveness and feasibility of the algorithm.
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Chua L O, Yang L. Cellular neural networks:Theory and applications. IEEE Trans. on CAS, 1988, 35(10): 1257-1290.[2]Chua L O, Thiran P. An Analytic method for designing simple CNNs. IEEE Trans. on CAS, 1991, 38(11): 1332-1341.[3]乔长阁.数字式细胞神经网络理论与应用研究:[博士论文].西安:西北工业大学,1993.[4]Matsumoto T, et al. CNN cloning template:Connected component detector. IEEE Trans. on CAS, 1990, 37(5):633-635.[5]Motzkin T S, Schoenberg L J. The Relaxation method for linear inequalities[J].Canada Journal of Mathematics.1954, 6(3):393-404 -
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