Kernel function combination-based PSO-LSSVM fluctuating wind speed prediction method

A technology of fluctuating wind speed and prediction method, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as building collapse, failure of accessory components, and fatigue damage of external wall components and accessories.

Inactive Publication Date: 2015-09-09
SHANGHAI UNIV
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Problems solved by technology

Buffeting and chattering under extreme wind loads can cause building collapse or serious damage; dynamic displacement exceeding the limit can easily cause wall cracking and damage to attached components; large vibrations can cause discomfort in living and living; frequent pulsating winds It will also cause fatigue damage to exterior wall components and appendages

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  • Kernel function combination-based PSO-LSSVM fluctuating wind speed prediction method
  • Kernel function combination-based PSO-LSSVM fluctuating wind speed prediction method
  • Kernel function combination-based PSO-LSSVM fluctuating wind speed prediction method

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Embodiment Construction

[0057] The idea of ​​the present invention is as follows: the Gaussian kernel function (RBF) is a local kernel function, and the polynomial kernel function (POLY) is a global kernel function. The local kernel function has strong learning ability and weak generalization performance, while the global kernel function has strong generalization performance and weak learning ability. According to Mercer's theorem, any kernel function k(x i ,x j ) Gram matrix K is symmetric and positive semi-definite, and satisfies a certain number of enclosing properties, which allow complex kernels to be created from simple kernels. Linearly combine the POLY kernel function and the RBF kernel function to construct a new kernel function:

[0058] k ( x i , x j ) = ( 1 - a ) · [ ( ...

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Abstract

The invention provides a kernel function combination-based PSO-LSSVM fluctuating wind speed prediction method, which comprises the following steps: using an ARMA model to simulate and generate a vertical space point fluctuating wind speed sample, dividing the fluctuating wind speed sample of each space point into a train set and a test set, and performing normalizing treatment on the train set and the test set; establishing a PSO-LSSVM model based on a combination kernel function; transforming the fluctuating wind speed train sample to a kernel function matrix by using the combination kernel function after PSO optimization, and mapping to a high dimensional feature space; obtaining a nonlinear model of the fluctuating wind speed train sample, and predicting the fluctuating wind speed test sample by using the model; comparing the test sample and a fluctuating wind speed result predicted by using the PSO-LSSVM of the combination kernel function, and calculating an average error and a root-mean-square error of the predicted wind speed and an actual wind speed and related coefficients. The invention ensures the prediction accuracy of the fluctuating wind speed.

Description

technical field [0001] The present invention relates to a single-point fluctuating wind speed prediction method based on a particle swarm optimized least squares support vector machine combined with a Gaussian kernel function and a polynomial kernel function, specifically a PSO-LSSVM (particle swarm optimization method) based on a combination of kernel functions. Optimized least square support vector machine) fluctuating wind speed prediction method. Background technique [0002] With the advancement of structural systems, building materials, design and construction technologies, and the renewal of architectural concepts, super high-rise buildings gradually present the characteristics of light weight, high flexibility, and low damping. The high flexibility and low damping characteristics lead to a significant increase in the wind-induced dynamic response of the structure, and the increase in the wind sensitivity of the structure makes the design of the structural wind load a...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 李春祥迟恩楠曹黎媛丁晓达
Owner SHANGHAI UNIV
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