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In the present study, an amplifying neuron and attenuating neuron, which canbe easily implemented into neural networks without any significant additionalcomputational effort, are proposed. The activated output value is squared forthe amplifying neuron, while the value becomes its reciprocal for theattenuating one. Theoretically, the order of neural networks increases when theamplifying neuron is placed in the hidden layer. The performance assessments ofneural networks were conducted to verify that the amplifying and attenuatingneurons enhance the performance of neural networks. From the numericalexperiments, it was revealed that the neural networks that contain theamplifying and attenuating neurons yield more accurate results, compared tothose without them.

Improving Neural Networks by Adopting Amplifying and Attenuating Neurons