采用细胞神经网络设计的一种学习算法

IEEE International Workshop on Cellular Neural Networks and their Applications Pub Date : 1990-12-16 DOI:10.1109/CNNA.1990.207509

F. Zou, S. Schwarz, J. Nossek

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引用次数: 65

摘要

提出了一种细胞神经网络(CNN)的学习算法。该算法基于求解线性不等式集的松弛法，可以得到克隆模板。模板的对称性可以通过附加的等式约束来强制实现。仿真实例表明，应用该训练规则可以生成具有最小邻域N/sub 1/(i, j)的有用模板。

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Cellular neural network design using a learning algorithm

A learning algorithm for cellular neural networks (CNN) is proposed. The cloning templates can be obtained by using this algorithm, which is based on the relaxation method for solving sets of linear inequalities. The symmetry of templates can be forced through additional equality constraints. Simulation examples show that some useful templates with the smallest neighborhood N/sub 1/(i, j) are generated by the application of the training rule.<>

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IEEE International Workshop on Cellular Neural Networks and their Applications

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