Influence of allele frequency on predicting animal phenotype using back-propagation artificial neural networks

Li, Xue Bin; Yu, Xin; Guo, Yun; Xiang, Zhi Feng; Zhao, Kaixuan; Ren, Fei

doi:10.1007/s11859-011-0719-x

Cited by 3 publications

(1 citation statement)

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“…However, they still have limitations, such as a low fitting level and a complex fitting formula, which cannot be applied to the fusion of multiple environmental factors of a photosynthetic rate model. Artificial neural networks have been widely used as a new intelligent modeling method for various types of multidimensional modeling of complex systems [17,18] . Recently, related research has been presented on photosynthetic rate models.…”

Section: Introduction mentioning

confidence: 99%

Model for tomato photosynthetic rate based on neural network with genetic algorithm

Xin

Zhang³

et al. 2019

International Journal of Agricultural and Biological Engineering

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A photosynthetic rate model provides a theoretical basis for fine-grained control of light, and has become the key component to determine the effectiveness of light-controlled environments. Therefore, it is critical to identify an intelligent algorithm that can be used to build an efficient and precise photosynthetic rate model. Depending on the initial weights of a BP (Back Propagation) neural network algorithm for arbitrary random numbers, the establishment of a regressive prediction model can be easily trapped in a partially-flat area. Existing photosynthetic rate models based on neural networks are facing problems such as a slow convergence speed and a long training time, and this study presents a photosynthetic rate model of a heuristic neural network for tomatoes based on a genetic algorithm to address the above problems. The performance of the model can be effectively improved using a genetic algorithm to optimize the initial weights. A multi-factor nesting experiment was firstly conducted to obtain 825 groups of tomato seedling photosynthesis rate test data in the foundation, and the photosynthetic rate model of the heuristic neural network for the tomato is established through BP network structure construction and data preprocessing. The genetic algorithm was used to optimize the network weights and threshold, and the LM (Levenberg-Marquardt) training method for network training. On this basis, the training performance and precision of the photosynthetic rate prediction models can be further compared with the genetic neural network model and the neural network model. The test results have shown that the training effects and accuracy of the genetic neural network prediction model of the photosynthetic rate were better than those of the neural network prediction model. The correlation coefficient between the model predicted data and the measured data is 0.987, and the absolute error of the photosynthetic rate is less than ±0.5 μmol/(m 2 · s).

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Section: Introduction mentioning

confidence: 99%