Neural Networks Pattern Classification for Certainty in Measurement of Position and Momentum with Heisenberg Uncertainty Principle

Examining the Role of Neural Networks in Resolving Heisenberg's Uncertainty Principle

Abstract

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.