Time sequence signal prediction method based on quantum filtering model

Abstract

The invention provides a time sequence signal prediction method based on a quantum filtering model, and the method comprises the steps: setting a filtering parameter value and an initial value of each function, obtaining all time sequence signal data, sequentially calculating a difference function at each moment, updating a weight function, and calculating a potential energy function. Calculating a wave function and filtered time sequence signal data according to a Schrodinger equation in a difference form, calculating the value of each quantile of the density matrix, carrying out regression on each quantile, predicting the quantile value of each moment, and sequentially predicting the time sequence signal data value of the next moment; and the time sequence signal data with the time greater than the last moment in the time sequence signal data is predicted. According to the method, on the basis of a basic quantum filtering model, filtering processing can be achieved without making any assumption processing on the distribution form of noise signals, and the prediction function of quantum filtering is achieved.

Description

technical field [0001] The invention belongs to the technical field of signal filtering and prediction, in particular to a time series signal prediction method based on a quantum filtering model. Background technique [0002] In the field of filtering and prediction of time series signal data, most of the commonly used methods, such as Kalman filter, extended Kalman filter and unscented Kalman filter, all need to assume that the noise signal obeys Gaussian distribution. It is difficult for these methods to effectively filter out the non-Gaussian noise signal existing in the data without knowing the specific form of the noise distribution. [0003] Quantum filtering draws on the principle of "swarm response" of a large number of particles in physics for filtering processing. This method does not make any assumptions about noise signals, so it is suitable for filtering and denoising signals when the distribution of noise is unknown. However, the existing quantum filter model ...

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Problems solved by technology

However, the existing quantum filtering model cannot realize the prediction function. At present, the existing literature only proposes the abstract concept of "predictor" for the quantum filtering model, but does not realize the specific prediction function of the "predictor".

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