<i>An improved photosynthesis prediction model based on artificial neural networks intended for cucumber growth control</i>

2017 ◽  
Author(s):  
Pingping Xin ◽  
Jin Hu ◽  
Haihui Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Growth Control ◽  
Model Based ◽  
Artificial Neural

scholarly journals A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz

2017 ◽  
Vol 16 (3) ◽  
pp. 708-722 ◽  
Author(s):  
Bruno J. Cavalcanti ◽  
Gustavo A. Cavalcante ◽  
Laércio M. de Mendonça ◽  
Gabriel M. Cantanhede ◽  
Marcelo M.M. de Oliveira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Path Loss ◽  
Empirical Models ◽  
Model Based ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural

Gasification of food waste in supercritical water: An innovative synthesis gas composition prediction model based on Artificial Neural Networks

2021 ◽  
Vol 46 (24) ◽  
pp. 12739-12757
Author(s):  
Shribalaji Shenbagaraj ◽  
Pankaj Kumar Sharma ◽  
Amit Kumar Sharma ◽  
Geetanjali Raghav ◽  
Karthikeya Bharadwaj Kota ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Food Waste ◽  
Supercritical Water ◽  
Synthesis Gas ◽  
Gas Composition ◽  
Model Based ◽  
Artificial Neural

An Improved Photosynthesis Prediction Model Based on Artificial Neural Networks Intended for Cucumber Growth Control

2018 ◽  
Vol 34 (5) ◽  
pp. 769-787 ◽  
Author(s):  
Pingping Xin ◽  
Haihui Zhang ◽  
Jin Hu ◽  
Zhiyong Wang ◽  
Zhen Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Photosynthetic Rate ◽  
Prediction Models ◽  
Ann Model ◽  
Rate Prediction ◽  
Model Based ◽  
Proposed Model ◽  
Artificial Neural

Abstract. The existing photosynthetic rate prediction models consider only a single growing season. However, a photosynthetic rate prediction model intended for full growth of crops is needed. Therefore, a photosynthetic rate prediction model based on artificial neural networks (ANN), which establishes the prediction of the entire photosynthetic process, is presented in this article. The proposed model was developed using the multi-factor photosynthetic rate data obtained by experiments on cucumber seedlings and flowering stage. The ANN model was trained with the Levenberg-Marquardt (LM) training algorithm. In contrast to the single-phase photosynthetic rate prediction models, in the proposed model a fusion of parameters of all growing stages was applied, whereat all growing parameters were merged into one six-dimensional input signal (temperature, CO2 concentration, light intensity, relative humidity, chlorophyll content, and growth stage). Verification of model accuracy and performance has shown that merging of growing parameters has obvious advantage. Moreover, the proposed model satisfied the requirement in terms of training error. In addition, the determination correlation between measured and estimated values was 0.9517, thus, good correlation and estimation were achieved. Besides, the test average absolute error was 1.1454, which proves a high accuracy of the proposed model. Therefore, the proposed prediction model can provide the theoretical basis for the facilities light regulation and technical support. Keywords: Artificial neural networks, Cucumber, Full growth period, Photosynthetic rate, Prediction model.

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