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Automatic recommendation method for signal reconstruction scheme

A technology for signal reconstruction and recommendation methods, applied in the direction of neural learning methods, neural architecture, digital data information retrieval, etc., can solve problems such as large computing costs and difficulty in obtaining recommended models, so as to reduce computing costs and solve the shortage of training samples , the effect of improving the accuracy

Pending Publication Date: 2022-04-05
ANHUI UNIVERSITY
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AI Technical Summary

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

Method used

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  • Automatic recommendation method for signal reconstruction scheme
  • Automatic recommendation method for signal reconstruction scheme
  • Automatic recommendation method for signal reconstruction scheme

Examples

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Embodiment Construction

[0053] In this example, if figure 1 As shown, an automatic recommendation method for a signal reconstruction scheme is performed as follows:

[0054] Step 1, using formula (1) to construct a single-objective optimization model for signal reconstruction;

[0055]

[0056] In formula (1), x represents the decision vector corresponding to the reconstructed signal, and x=(x 1 ,x 2 ,...,x i ,...,x d ), where x i Represents the i-th decision variable, and the i-th variable x i Corresponding to the i-th signal value, y is the observed signal, A is the perception matrix, f 1 (x) is the Lq paradigm of the reconstructed signal x, which is used to represent the sparse value of the reconstructed signal, q represents the parameter used to adjust the sparseness of the reconstructed signal, f 2 (x) represents the loss value of the reconstructed signal, and λ represents the weight, which is used to balance the sparsity and feasibility of the reconstructed signal;

[0057] Step 2. B...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06N3/04G06N3/08G06N3/00
Inventor 田野田应滋俞晓山张兴义
Owner ANHUI UNIVERSITY
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