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6th Seminar on Neural Network Applications in Electrical Engineering最新文献

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On linear filtering capabilities of 1-D CNNs with minimum-size templates 最小模板下一维cnn的线性滤波能力
Pub Date : 2002-12-10 DOI: 10.1109/NEUREL.2002.1057981
R. Matei, L. Goras
In this paper we investigate the linear filtering capabilities of the standard cellular neural network in the general case of non-symmetric templates. We refer to 1D Cellular Neural Networks (CNN's) with templates of minimum size (1/spl times/3). A detailed analysis of the spatial transfer function is made, emphasizing the useful filtering functions that can be obtained. We present an approach from a designer's point of view, establishing a set of relations to be satisfied by the template parameters, in order to obtain the desired filtering function with specified characteristics - central frequency, bandwidth, selectivity. Symmetric templates are treated as a particular case. For each type of filtering the characteristics are shown and simulation results are presented as well.
本文研究了标准细胞神经网络在一般非对称模板情况下的线性滤波能力。我们指的是具有最小尺寸模板(1/spl times/3)的1D细胞神经网络(CNN)。对空间传递函数进行了详细的分析,强调了可以得到的有用的滤波函数。我们提出了一种从设计者的角度出发的方法,即建立一组由模板参数所满足的关系,以获得具有特定特征(中心频率、带宽、选择性)的期望滤波函数。对称模板被视为一种特殊情况。给出了每种滤波的特性,并给出了仿真结果。
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引用次数: 6
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6th Seminar on Neural Network Applications in Electrical Engineering
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