Abstract
A dynamic crop growth model of undeterminate tomato variety (TOMGRO) consists of a large number (69) of difference equations that describe age classes of different organs. In order to find a fast working and low-dimensional equivalent with dynamic Neural Networks (NN), several model reduction approaches has been tried, including aggregation, PCA transformation, and bottleneck NN compression. Reduced data were used to train dynamic NN. Simulations with the NN model produced trajectories which agreed well with the original trajectories of TOMGRO.
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© 1995 Springer-Verlag/Wien
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Ioslovich, I., Seginer, I. (1995). Neural Networks for Dynamical Crop Growth Model Reduction and Optimization. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_92
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_92
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive