Optimized particle swarm BP network prediction method and system based on clustering

Abstract

The invention discloses an optimized particle swarm BP network prediction method and system based on clustering. The method comprises the steps that a database is established to store data measured bya monitor in real time; after the original data are preprocessed, similar data are classified into one class through clustering, so that the accuracy of a prediction model is improved; the initial weight and the threshold value of the BP network are assigned through the optimized particle swarm optimization algorithm, and compared with initialization of the weight and the threshold value througha random number, the convergence speed of the BP network can be increased on the basis that the BP network is prevented from falling into local optimum; a Sigmoid function is selected as an activationfunction, a BP network weight and a threshold value are updated by adopting a back propagation algorithm, a final model is obtained through training, and a prediction result is output. According to the method, the problems of inaccurate nonlinear data prediction and low prediction precision of traditional regression classification and other prediction models are solved, and the accuracy of a prediction result is effectively improved.

Description

technical field [0001] The invention belongs to the field of data analysis, and in particular relates to a cluster-based optimization particle swarm BP network prediction method and system. Background technique [0002] With the advent of the era of big data, data mining technology provides a new method for data prediction. In the case where the internal interaction rules and mechanism of the original data cannot be well determined, it can be considered to establish a prediction model from the perspective of data mining technology. At present, most of the domestic and foreign studies on the relationship between different components of landfill gas focus on the correlation between landfill gas and environmental factors such as wind direction, wind speed, temperature, and geographical factors such as depressions and valleys, or focus on the same landfill gas. Landfill gas change law between different seasons. [0003] To analyze and process data from the perspective of data ...

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Problems solved by technology

[0008] The present invention aims at the problems of inaccurate prediction of nonlinear data and low prediction accuracy in traditional prediction models such as regression classification, and proposes a cluster-based optimized particle swarm BP network prediction method and system

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