Automatic recommendation method for signal reconstruction scheme

Abstract

The invention discloses an automatic recommendation method for a signal reconstruction scheme. The method comprises the following steps: 1, constructing a single-target optimization model for signal reconstruction; 2, constructing a data set; 3, for the data set, using a deep recurrent neural network to train a recommendation model; 4, fitting a signal to reconstruct a corresponding tree structure and acquiring features; and 5, inputting features obtained after fitting into the model so as to recommend a signal reconstruction scheme. According to the method, automatic recommendation of the signal reconstruction scheme can be realized, and the recommendation accuracy and efficiency are improved.

Description

technical field [0001] The invention belongs to the field of signal reconstruction, in particular to an automatic recommendation method for a signal reconstruction scheme. Background technique [0002] The purpose of the signal reconstruction problem is to find the sparsest signal with the minimum loss, and different solutions to solve the signal reconstruction problem have obvious performance differences, so how to automatically recommend a solution for a specific problem is very important. [0003] At present, the difficulty of automatic recommendation methods for signal reconstruction schemes lies in two aspects: First, due to the complexity of continuous optimization problems such as signal reconstruction, most existing recommendation methods regard them as black-box problems to extract features. Specifically, by sampling some decision vectors in the decision space and calculating their target values, and then calculating various features based on these target values, bu...

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Problems solved by technology

[0003] At present, the difficulty of automatic recommendation methods for signal reconstruction schemes lies in two aspects: First, due to the complexity of continuous optimization problems such as signal reconstruction, most existing recommendation methods regard them as black-box problems to extract features. Specifically, by sampling some decision vectors in the decision space and calculating their target values, and then calculating various features based on these target values, but as the problem dimension increases, a large amount of computing cost is required to extract more accurate features ;Secondly, the existing recommendation methods usually use a small number of benchmark questions to train the model, so it is difficult to get an effective recommendation model

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