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Method for determining construction land area influence factor weight values by using neural network algorithm

A technology of neural network algorithm and influencing factors, which is applied in the field of evaluation index weight coefficient prediction, can solve problems such as undiscovered neural networks, and achieve a more reasonable and scientific calculation method

Active Publication Date: 2021-03-19
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0004] But at present, the inventor has not found that there is a research on land area index evaluation using neural network to solve the weight value of the above-mentioned land area influencing factors.

Method used

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  • Method for determining construction land area influence factor weight values by using neural network algorithm
  • Method for determining construction land area influence factor weight values by using neural network algorithm
  • Method for determining construction land area influence factor weight values by using neural network algorithm

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Embodiment Construction

[0022] Below in conjunction with specific embodiment, further illustrate the present invention.

[0023] This embodiment takes Ya'an City as an example to illustrate how to use the neural network algorithm to determine the weight value of each influencing factor of the construction land area, specifically including the following steps:

[0024] S1. Collect the construction land area data of Ya'an City in the past 12 years and the corresponding data of 14 influencing factors after the maximum and minimum normalization processing as sample data 1, which contains 12 samples.

[0025] S2. Expand sample data - because sample data 1 contains an even number of samples, sample data 1 is divided into two groups A1 and A2 according to the year. Group A1 contains the sample data of the first 6 years; group A2 contains the sample data of the next 6 years Sample data, use the sample data of each construction land area in group A2 to subtract the sample data of each construction land area i...

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Abstract

The invention discloses a method for determining a construction land area influence factor weight value by using a neural network algorithm, which comprises the following steps of: expanding originalsample data, dividing the original sample data into two groups, subtracting the sample data of the first group from the sample data of the second group in sequence to form new expanded sample data, and determining the construction land area influence factor weight value. By adopting the method of firstly grouping and then calculating the difference values in a one-to-one correspondence manner, thesamples are expanded, and the importance degree of each influence factor to the construction land area is not changed. In addition, a new BP neural network is established to train an expanded sampleand extract related rules, and MIV is considered as one of the indexes with the best variable correlation in the neural network. The calculation method for determining the influence factor and the construction land area weight coefficient is more reasonable and scientific.

Description

technical field [0001] The invention relates to a method for determining the weight value of influencing factors of construction land area by using a neural network algorithm, and belongs to the technical field of evaluation index weight coefficient prediction. Background technique [0002] The area of ​​construction land is one of the most basic indicators to measure the sustainable and healthy development of villages and towns. It is very necessary to study the degree of influence of various factors affecting the area of ​​construction land, that is, the weight. This requires determining the weight according to the importance of the influencing factors size. In the previous evaluation methods, the determination method of the traditional evaluation index weight coefficient has a lot of ambiguity, sometimes the deviation is too large, and at the same time, the influence of human factors in the weight determination is also great. With the passage of time and space, the degre...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/08
CPCG06Q10/06393G06N3/084
Inventor 高磊周亚州赵静瑶祝晓凡郭凯睿黄勇
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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