A novel nonlinear synapse neuron model guaranteeing a global minimum-Wavelet Neuron

T. Yamakawa
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引用次数: 9

Abstract

A novel nonlinear synapse neuron model named Wavelet Neuron is proposed, which realizes effective establishment of approximate wavelet expansion by learning instead of analytical procedure. The distinctive features of the model are guarantee of a global minimum and three decades higher speed of learning than feedforward multilayer neural networks.
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一种新的保证全局最小小波神经元的非线性突触神经元模型
提出了一种新的非线性突触神经元模型——小波神经元模型,该模型通过学习代替解析过程实现了近似小波展开的有效建立。该模型的显著特点是保证了全局最小值和比前馈多层神经网络快30年的学习速度。
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A novel nonlinear synapse neuron model guaranteeing a global minimum-Wavelet Neuron Minimal test set generation for fault diagnosis in R-valued PLAs A finite basis of the set of all monotone partial functions defined over a finite poset Application of neuron-MOS to current-mode multi-valued logic circuits Upper and lower bounds on the number of fuzzy/c switching functions
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