NDLI: Artificial neural networks to predict daylily hybrids

Content Provider	IEEE Xplore Digital Library
Author	Gosukonda, R.M. Naghedolfeizi M. Carter, J.
Copyright Year	2005
Description	Author affiliation: Fort Valley State Univ., GA, USA (Gosukonda, R.M.; Naghedolfeizi M; Carter, J.)
Abstract	Artificial Neural Networks (ANN) were employed to predict daylily (Hemerocalli spp.) hybrids from known characteristics of parents used in hybridization. Features such as height, diameter, foliage, blooming habit, pioidy, blooming sequence were included in the initial training and testing. Data pre-processing was performed to meet the format requirements of ANN. Backpropagation (BP), Kalman filter (KF) learning algorithms were used to develop nonparametric models between the input and output data sets. These networks were compared with traditional multiple linear regression models. Prediction plots for both height and diameter indicated that the regression model had a better accuracy in predicting unseen patterns. However, ANN models were able to more robustly generalize and interpolate unseen patterns within the domain of training.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Bioinformatics
Starting Page	187
Ending Page	188
File Size	133721
Page Count	2
File Format	PDF
ISBN	0769524427
DOI	10.1109/CSBW.2005.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	International trade Accuracy Parameter estimation Linear regression Artificial neural networks Backpropagation algorithms Predictive models Spatial databases Robustness Testing
Content Type	Text
Resource Type	Article

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