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 i...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±0.5μmol/(m^(2)·s).展开更多
基金This research is financial support by the research grants from National Natural Science Foundation of China(31501224 and 31671587)Shaanxi Province,Agricultural Science and Technology Innovation and Research Projects of China(2016NY-125).
文摘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±0.5μmol/(m^(2)·s).
