LSSVM Fluctuating Wind Velocity Prediction Method Based on Ant Colony and Particle Swarm Integration

A technology of fluctuating wind speed and prediction method, applied in special data processing applications, instruments, electrical and digital data processing, etc. awesome effect

Inactive Publication Date: 2018-03-06
SHANGHAI UNIV
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Problems solved by technology

At present, the common ways to optimize LSSVM mainly include particle swarm algorithm, genetic algorithm, ant colony algorithm and artificial bee colony algorithm. To a certain extent, various optimization algorithms have achieved certain results in optimizing LSSVM parameters, but the obtained The prediction accuracy and speed of the prediction model are still not ideal

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  • LSSVM Fluctuating Wind Velocity Prediction Method Based on Ant Colony and Particle Swarm Integration
  • LSSVM Fluctuating Wind Velocity Prediction Method Based on Ant Colony and Particle Swarm Integration
  • LSSVM Fluctuating Wind Velocity Prediction Method Based on Ant Colony and Particle Swarm Integration

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Embodiment Construction

[0043] The implementation of the present invention will be described in further detail below with reference to the accompanying drawings.

[0044] For the commonly used kernel function of LSSVM, the RBF kernel has only one parameter to be determined, and the fitting accuracy is relatively high. Therefore, LSSVM with the kernel function as the RBF kernel is adopted, and then the ACO and PSO serial hybrid method is used to quickly select the best kernel The function parameter σ and the regularization parameter C are combined. The solution process of ant colony algorithm is relatively complex, and each step has several parameters that need to be adjusted. The whole algorithm iteration takes a long time and is prone to stagnation, which is not conducive to finding a better solution. Its convergence performance is compared with the setting of initialization parameters. Sensitive, but with high accuracy; particle swarm algorithm uses fitness value to evaluate the system, and conducts a...

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Abstract

The invention provides an LSSVM pulsating wind speed prediction method based on the integration of ant colony and particle swarm, which includes the following steps: performing normalization processing; calculating the pheromone concentration of each ant; moving other ants in the ant colony to the position of the head ant. Global search; update the ant pheromone concentration at each position during the iterative process, check whether the iteration termination condition is satisfied, if not, return to the third step; otherwise, the algorithm ends and outputs the optimal parameter combination; initializes the relevant parameters of the particle swarm; The fitness value of each particle's own optimal position is compared with the fitness value of the group's optimal position; the predicted fluctuating wind speed time history spectrum is obtained. The invention has the characteristics of high optimization precision, high convergence precision, few iteration times, high success rate and the like.

Description

Technical field [0001] The invention relates to a method for predicting pulsating wind speed based on intelligent optimization and integration of LSSVM (least squares support vector machine), in particular to a method for predicting pulsating wind speed based on the integration of ant colony (ACO) and particle swarm (PSO). Background technique [0002] For high-rise structures, high-rise building structures, long-span spatial structures, long-span bridge structures, and high-voltage power transmission tower-line systems, wind load is an important type of random dynamic load that must be considered in structural design. The improper design of wind load will not only affect the comfort of people using the building structure, but also cause certain damage and destruction to the building structure, causing huge loss of life and property to people. Wind is usually divided into average wind and pulsating wind for analysis. The pulsating wind has random characteristics, which will cause...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00G06N3/00
Inventor 李春祥丁晓达迟恩楠
Owner SHANGHAI UNIV
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