The Application of Data Mining Algorithm in Grain Output Prediction
Genetic algorithm and neural network are two basic methods of data mining. This paper studies the related theory of genetic algorithm and neural network and, on this basis, proposes an improved mining method basing on the combination of genetic algorithm
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The Application of Data Mining Algorithm in Grain Output Prediction Yu Fan
Abstract Genetic algorithm and neural network are two basic methods of data mining. This paper studies the related theory of genetic algorithm and neural network and, on this basis, proposes an improved mining method basing on the combination of genetic algorithm and LM optimization algorithm. This method uses genetic algorithm in two stages to improve the quality of network training. It first through the genetic algorithm to get an approximation of the overall solution by coarse control, as the initial value, and then takes genetic algorithm and LM optimized neural network algorithm to alternately train the network. By an example calculation, we can see it is feasible for crop output prediction model basing on neural network to finally inquire of the future crop output by fitting the historical data. The model achieves good results through the optimization of genetic algorithm and LM. Keywords Data mining • Genetic algorithm • Neutral network • Crop output
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Introduction
With the rapid development of computer technology and its wide application in the production process, the capacities of formation, collection, storage and processing data of the enterprise have greatly increase and the amount of data grows with each passing day. As we all know, data is wealth, but this value is implied. In order to locate the truly valuable thing—knowledge from mountains of data, from 1990s, people began the study of data mining [1]. It is worth nothing that for different applications, it should design specific data mining solutions, in order to achieve the efficiency of knowledge acquisition. According to characteristics for agricultural production, the
Y. Fan (*) Department of Computer Science and Engineering, Guangdong Peizheng College, 53# Peizheng Avenue, Chini Town, Huadu District, Guangzhou City, Guangdong Province 510830, China e-mail: [email protected] S. Zhong (ed.), Proceedings of the 2012 International Conference on Cybernetics 757 and Informatics, Lecture Notes in Electrical Engineering 163, DOI 10.1007/978-1-4614-3872-4_97, # Springer Science+Business Media New York 2014
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Y. Fan
use of data mining technology can access qualitative and quantitative knowledge from the field to help agricultural workers improve the planting structure. Data mining do not have a uniform definition currently in the world [2]. One of the more representative views regards that data mining is the process of extracting or digging out a model that is credible, innovative, effective, and has potential value and can be understood from large amounts of data. This process is non-normal process [3]. From view of technology, it is the process of extracting information and knowledge which is implicit, unknown, but is potentially useful from a large number of incomplete, noisy, fuzzy, random data [4]. Genetic algorithm, basing on natural population evolution mechanism, is an efficient exploration algorithm which abandons the traditional searching method, simula
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