Prediction method of LSSVM non-Gaussian fluctuating wind speed

Abstract

The invention provides a prediction method of an LSSVM non-Gaussian fluctuating wind speed, which comprises seven steps, wherein the specific steps are that a non-Gaussian random fluctuating wind speed sample is generated through simulation with a memory-less nonlinear conversion method, the non-Gaussian fluctuating wind speed sample is divided into a training set and a testing set, and normalization is carried out to the two sets respectively; and training learning is carried out to the LSSVM by the training set, the testing set is used for prediction, fitness of each chromosome in a groups is calculated, whether algorithm convergence criterions are satisfied, a combined solution is put into a set if an optimal parameter combination is satisfied and then the fifth step is started, and otherwise the fourth step will be started. The prediction method of the LSSVM non-Gaussian fluctuating wind speed provided by the invention combines a genetic algorithm and an ant colony algorithm to intelligently extract the optimal parameter combination of the LSSVM in order to establish an optimized LSSVM prediction model and predict the testing set. In this way, a predicted time interval spectrum of the non-Gaussian fluctuating wind speed can be obtained.

Description

technical field [0001] The invention relates to an LSSVM (least squares support vector machine) non-Gaussian pulsating wind speed prediction method, in particular to a LSSVM non-Gaussian pulsating wind speed prediction method using a mixture of genetic algorithm (GA) and ant colony algorithm (ACO). Background technique [0002] In architectural engineering design, wind load is one of the main loads of various building structures. The wind is usually divided into average wind and fluctuating wind. The fluctuating wind has random characteristics and its period is shorter, which is closer to the natural vibration period of the building. It will cause the structure to vibrate in the downwind direction, gallop in the cross-wind direction, and vortex shedding. Wind-induced random vibration in the form of torsional-divergent vibration and other coupled vibrations. The time-domain analysis of wind vibration can more comprehensively understand the wind-induced vibration response cha...

AI Technical Summary

Problems solved by technology

At present, the common optimization methods for LSSVM mainly include artificial fish swarm algorithm, genetic algorithm, ant colony algorithm and particle swarm algorithm. To a certain extent, various optimization algorithms have achieved certain results in the optimization of LSSVM parameters, but the obtained The prediction accuracy and speed of the prediction model still have certain defects

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