Abstract
In order to predict numerical value, we propose a new intelligent algorithm opposite degree computation algorithm. The opposite degree computation algorithm is based on the degree of antagonism between the data to analyze the approximate relationship. The experiment was conducted at Chinese Xinjiang Province, during year 1995 to year 2010. Opposite degree computation algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. By learning Chinese Xinjiang cotton production data from 1995 – 2005, forecasts 2006 – 2010 cotton production; the result of the absolute error is 9.3237%. Meanwhile, we introduce the prediction method based on BP neural network for the result comparison and found opposite degree computation method is superior to the BP neural network method. Cotton production prediction based on opposite degree computation proved the algorithm is feasible and effective and can be used in numerical value prediction.
Highlights
Information technology application in cotton production prediction is a useful research area for agriculture and social development, which has gained increasing popularity in China [1]
By analyzing the basic concept of the contrary degree, we proposed an opposite degree computation algorithm
The algorithm is based on the degree of antagonism between the data to analyze the approximate relationship
Summary
Information technology application in cotton production prediction is a useful research area for agriculture and social development, which has gained increasing popularity in China [1]. It is because cotton production prediction is very important for cultivation, consumption, exports [2]. Cotton belongs to market price crops, so its acreage may be very volatility; and climate change is essential for its production. Compared to food crops, the cotton production prediction has more difficulties [3]. We try to find a new way for cotton production prediction
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