General airplane air-material demand prediction method based on MPSO-BP network

A technology of MPSO-BP and demand forecasting, applied in neural learning methods, biological neural network models, etc.

Inactive Publication Date: 2016-05-11
SHENYANG AEROSPACE UNIVERSITY
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Problems solved by technology

[0008] In order to solve the problem of forecasting demand for general aircraft aviation materials, the present

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  • General airplane air-material demand prediction method based on MPSO-BP network
  • General airplane air-material demand prediction method based on MPSO-BP network
  • General airplane air-material demand prediction method based on MPSO-BP network

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Embodiment Construction

[0057] see Figure 1-Figure 6 And Table 1-Table 2, the general aircraft material demand forecasting method based on MPSO-BP network, this method analyzes and studies the main influencing factors of general aircraft material demand, adopts two strategies of adaptive variation and setting linear decreasing inertia weight The defects of the basic PSO algorithm are improved, and the prediction model of the improved particle swarm algorithm to optimize the BP network is constructed.

[0058] Step 1: Analysis of factors affecting the demand for general aircraft materials.

[0059] (1) Calculate the flight time (P 1 )

[0060] (2) Aviation material failure rate (P 2 )

[0061] (3) Average time between failures of aviation materials (P 3 )

[0062] (4) Technical level of maintenance personnel (P 4 )

[0063] (5) Environmental factors (P 5 )

[0064] Step 2: Improvements to the basic PSO algorithm.

[0065] The PSO algorithm randomly initializes a group of particles, and the...

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Abstract

The invention brings forward a general airplane air-material demand prediction method based on an MPSO-BP network with the combination of a particle swarm algorithm and a BP network. The method includes: firstly, analysis and research of main influence factors of the general airplane air-material demand are conducted, then the basic principle of the BP network, the PSO algorithm, and the improved strategy of the PSO algorithm are introduced, and a prediction model for improving the particle swarm algorithm and optimizing the BP network is built. Five main influence factors of the general airplane air-material demand are regarded as the input of the network, the demand quantity of the general airplane air-material is regarded as the output, and a nonlinear mapping relation between the input and the output is established. According to the method, accurate prediction of the general airplane air-material demand quantity can be realized, the non-linear fitting capability and generalization capability are very good, the convergence efficiency is improved, the possibility of falling to a local minimum value is reduced, the prediction precision is higher, and the application effect in the general airplane air-material demand prediction is good.

Description

technical field [0001] The invention belongs to the field of general aircraft aviation material management, and in particular relates to a method for forecasting general aircraft aviation material demand based on an intelligent algorithm to optimize a BP network. Background technique [0002] General aircraft material demand forecasting directly affects the material guarantee and cost management of general aviation enterprises. Inaccurate forecasting will cause material shortage or waste, while accurate forecasting can greatly reduce costs while meeting high guarantee rates. Therefore, how to scientifically Determining the demand for general aircraft materials has always been a key research topic for general aviation companies. [0003] At present, some research results have been obtained in the research on the forecasting method of aviation material demand. Jia Zhiyu researched the forecast method of aviation material consumption based on the summation autoregressive movin...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/08
Inventor 陈侠王拓
Owner SHENYANG AEROSPACE UNIVERSITY
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