Zhiying Fang1, Tong Mao2, Jun Fan3

  • 1Institute of Applied Mathematics, Shenzhen Polytechnic University, Shenzhen, Guangdong, China fangzhiying@szpu.edu.cn.

Neural computation
|March 8, 2024
PubMed
概括

本研究使用信息理论学习分析深层卷积神经网络 (CNN). 它为使用CNN进行强大的回归提供了一个理论框架,显示特定函数的收率.

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Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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Neural Circuits01:25

Neural Circuits

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Associative Learning01:27

Associative Learning

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