Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

LSSVM fluctuating wind speed prediction method based on integration of ant colony and particle swarm

A technology of pulsating wind speed and prediction method, which is applied in special data processing applications, instruments, electrical digital data processing, etc. It can solve the problems of unsatisfactory prediction accuracy and speed of prediction models, and achieve high success rate, high optimization accuracy, and convergence accuracy high effect

Inactive Publication Date: 2015-09-09
SHANGHAI UNIV
View PDF2 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • LSSVM fluctuating wind speed prediction method based on integration of ant colony and particle swarm
  • LSSVM fluctuating wind speed prediction method based on integration of ant colony and particle swarm
  • LSSVM fluctuating wind speed prediction method based on integration of ant colony and particle swarm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

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

[0044] For the common kernel function of LSSVM in the present invention, the RBF kernel has only one undetermined parameter, and the fitting accuracy is high, so the LSSVM whose kernel function is the RBF kernel is adopted, and then the best kernel is quickly selected by using the method of serial mixing of ACO and PSO The function parameter σ and the regularization parameter C are combined. The solution process of the ant colony algorithm is complex, and there are several parameters that need to be adjusted in each step. The entire algorithm takes a long time to iterate once, and it is prone to stagnation, which is not conducive to finding a better solution. Sensitive, but high precision; particle swarm optimization algorithm uses the fitness value to evaluate the system, and conducts a certain random search according to the fitness val...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides an LSSVM fluctuating wind speed prediction method based on integration of an ant colony and particle swarm, and the method comprises the following steps: performing normalizing treatment; calculating pheromone concentration of each ant; moving other ants to the position of the head ant in the ant colony to perform global search; in an iteration process, updating the ant pheromone concentration at each position, inspecting whether an iteration terminal condition is met or not, if not, returning to step 3; otherwise, ending an algorithm and outputting an optimal parameter combination; initializing related parameters of the particle swarm; comparing the fitness value of a self optimal position of each particle with the fitness value of the optimal position of the swarm; obtaining the predicted fluctuating wind speed time interval spectrum. The method has the characteristics of high optimized speed, high convergence precision, less iteration times, high successful rate and the like.

Description

technical field [0001] The invention relates to a method for predicting fluctuating wind speed based on intelligent optimization and integration of LSSVM (least squares support vector machine), specifically an LSSVM fluctuating wind speed prediction method based on the integration of ant colony (ACO) and particle swarm (PSO). Background technique [0002] For towering structures, high-rise building structures, long-span space structures, long-span bridge structures, and high-voltage transmission tower systems, wind loads are an important type of random dynamic load that must be considered in structural design. Improper design of wind load will not only affect the comfort level of people using the building structure, but also cause certain damage and damage to the building structure, which will bring huge loss of life and property to people. Generally, the wind is divided into average wind and fluctuating wind for analysis. The fluctuating wind has random characteristics, whi...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/00
Inventor 李春祥丁晓达迟恩楠
Owner SHANGHAI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com