Full TGIF Record # 237785
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Web URL(s):http://lunwen.zhiwutong.com/47/14B4D641-32BB-4D14-9F85-7E6838D6F4CA.html
    Last checked: 02/23/2018
    Notes: ITem is an abstract
Publication Type:
Author(s):Xiao, Bo; Song, Gui-long; Han, Lie-bao; Bao, Yong-xia; Li, Fei-fei; Chen, Ai-xia
Author Affiliation:Bo, Song, Han, Bao, and Li: Institute of Turfgrass Science, Beijing Forestry University, Beijing; Xiao and Han: College of Gardening and Horticulture, Yangtze University; Chen: Jingzhou Institute of Technology, Jingzhou, China
Title:A comprehensive evaluation of turfgrass quality based on a BP and RBF neural network model
Source:[Caoye Xuebao] [Acta Prataculturae Sinica]. Vol. 21, No. 4, August 20 2012, p. 275-281.
# of Pages:7
Publishing Information:[Lanzhou Shi, China]: ["Cao Ye Xue Bao" Bian Ji Wei Yuan Hui]
Abstract/Contents:"Based on the recent turfgrass quality evaluation system, eleven indexes (density, texture, color, uniformity, green period, disease resistance, coverage, traffic tolerance, seedling establishment, turf strength and biomass) were used to select 20 Poa pratensis cultivars in 2010. The values of eleven indexes from 15 of the P. pratensis cultivars were selected as input data for the system using the principles of neural networks and the Matlab neural network toolbox. The output was expert graded data. Performance optimization was carried out by running the neural networks with different parameters and then models of the BP and RBF neural networks for evaluation of turfgrass quality were established. Methods of establishing neural network models and steps for Matlab are listed. Quality of the other 5 P. pratensis cultivars was evaluated using the trained neural network model. The predicted errors of the RBF neural network were less than 2% and the predicted errors of the BP neural network were more than 5% when they were applied as a comprehensive evaluation of turfgrass quality. Therefore the RBF neural network with smaller error odds was able to provide a more accurate evaluation of turfgrass quality than the BP neural network and it can be used to evaluate turfgrass quality. Compared with traditional methods, such as the weighting method, analytic hierarchy process, and fuzzy synthesis, the RBF neural networks accuracy reduces the influences of subjective factors and simplifies the calculating procedures. It provides a new idea for comprehensive evaluation of turfgrass quality."
Note:Abstract also appears in English
Sum No. 99"
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Xiao, B., G.-l. Song, L.-b. Han, Y.-x. Bao, F.-f. Li, and A.-x. Chen. 2012. A comprehensive evaluation of turfgrass quality based on a BP and RBF neural network model. (In Chinese) [Caoye Xuebao] [Acta Prataculturae Sinica]. 21(4):p. 275-281.
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DOI: 10.11686/cyxb20120433
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    Last checked: 02/23/2018
    Notes: ITem is an abstract
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MSU catalog number: b10289426
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