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A prediction method based on orthogonal reverse bottleneck optimization algorithm

A prediction method and optimization algorithm technology, applied in the computer field, can solve problems such as falling into local extremum, achieve the effect of improving quality and convergence efficiency, and reducing possibility

Pending Publication Date: 2019-01-29
WENZHOU UNIVERSITY
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Problems solved by technology

However, when the algorithm is aimed at complex problems, it may also fall into the possibility of local extremum

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  • A prediction method based on orthogonal reverse bottleneck optimization algorithm
  • A prediction method based on orthogonal reverse bottleneck optimization algorithm
  • A prediction method based on orthogonal reverse bottleneck optimization algorithm

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[0034] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0035] Such as figure 1 As shown, in the embodiment of the present invention, a method for constructing a prediction model based on an orthogonal reverse salp optimization algorithm is proposed, and the method includes the following steps:

[0036] Step S1: Parameter initialization; wherein, the initialized parameters include: the maximum number of iterations T, the number N of salps in the salp chain, and the search space of C [C min ,C max ] and the search space of γ [γ min ,γ max ];

[0037] Step S2: Initialize the position of the salp population: randomly generate the positions of N salps, and the position of the i-th salp is X i =(x i1 ,x i2 ), i=1,2,...,N; among them, x i1 Indicates the C value of salp i at the current position, x i2 Indicates the g...

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Abstract

The invention provides a prediction method based on an orthogonal reverse bottleneck optimization algorithm, comprising the steps of loading a data set and performing standard processing on sample data; the penalty coefficients and kernel widths are optimized by using the orthogonal inverse bottleneck optimization algorithm, and a predictive support vector machine model is constructed. By adoptingorthogonal learning and reverse learning strategies to optimize the penalty factor and the kernel width on the basis of the traditional optimization algorithm of Styela bottlenecks, the invention caneffectively reduce the possibility of falling into local optimization in the optimization process, and obviously improve the quality of the solution and the convergence efficiency.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a prediction method based on an orthogonal reverse salp optimization algorithm. Background technique [0002] Grid search and gradient descent are currently the two most commonly used parameter optimization methods for support vector machines (SVM). Grid search is an exhaustive search method. It generally divides the specified parameter space by setting reasonable interval upper and lower limits and interval steps, and then trains and predicts the parameter combinations represented by each grid node. These A group of parameters with the highest values ​​in the prediction results are used as the best parameters of the final SVM model. Although this exhaustive search method can guarantee the optimal parameter combination in a given parameter space to a certain extent, as the parameter space increases, its search efficiency will be greatly reduced, especially when setting reasona...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/00
CPCG06Q10/04G06N3/006
Inventor 焦珊陈慧灵徐粤婷罗杰张谦陈昊赵学华
Owner WENZHOU UNIVERSITY
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