基于海森堡测不准原理的位置动量测量确定性的神经网络模式分类

Punam, O. P. Prakash
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引用次数: 1

摘要

摘要:利用具有反向传播学习原理的人工神经网络模式分类技术,讨论了海森堡不确定性原理。在这个过程中,可以构建一个合适的安排,包含两个实验的控制,其中一个被设计用于测量位置,另一个被设计用于测量亚原子粒子(电子)的动量。控制系统将确定任意时刻任何波长的电子动量的不确定性较小的位置,并开始用相应的实验测量不确定性较小的量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Networks Pattern Classification For Certainty In Measurement of Position and Momentum With Heisenberg Uncertainty Principle
Abstratct – Heisenberg uncertainly principle can be discussed by using the pattern classification technique of Artificial Neutral Networks with Back propagation learning rue. In this process a suitable arrangement can be constructed that contains the control of two experiments, one of which is designed to measure the position and other one is designed to measure the momentum of a sub-atomic particle (the electron). The Control system will determine either the position of momentum of electron, which less uncertain for any wavelength of light at any instant and start measuring the less uncertain quantity with the corresponding experiment.
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