以大豆叶面积指数(Leaf area index,LAI)反演为研究目标,利用PROSAIL模型和遗传算法优化后的BP神经网络模型,分别对重组自交系(Recombinant Inbred Lines,RIL)和自然野生大豆种群的LAI进行反演。结果表明,在对人工定向培育的RIL大豆种...以大豆叶面积指数(Leaf area index,LAI)反演为研究目标,利用PROSAIL模型和遗传算法优化后的BP神经网络模型,分别对重组自交系(Recombinant Inbred Lines,RIL)和自然野生大豆种群的LAI进行反演。结果表明,在对人工定向培育的RIL大豆种群冠层叶片LAI反演中,PROSAIL模型表现出了更优异的反演能力,而对品种繁多的自然野生大豆种群LAI反演中,遗传算法优化后的BP神经网络模型表现出了更好的适用性,并且上述2种模型在始粒期(R5)时性能最佳,PROSAIL模型和遗传算法优化后的BP神经网络模型R2分别为0.89和0.85,RMSE分别为0.11和0.13,EA均为97%,典型生育期内的反演性能均优于全生育期综合反演性能。因此,针对同一农作物不同种群的表型特征反演,需要根据研究对象的特征来选择合适的模型,以便于精确的估测大豆长势情况,为农作物的规模化育种监测提供数据支持。展开更多
The training process of Back Propagation Neural Network (BPNN) is easily converged at a local minimum, which slows the training process sharply.In this paper, an analysis is given to the chief formative reason of loca...The training process of Back Propagation Neural Network (BPNN) is easily converged at a local minimum, which slows the training process sharply.In this paper, an analysis is given to the chief formative reason of local minimum, and an improved Genetic Algorithm (GA) is introduced to overcome local minimum.Most BPNNs use Sigmoid function as the transfer function of network nodes, this paper indicates that the flat characteristic of Sigmoid function results in the formation of local minimum.In the improved GA, pertinent modifications are made to the evaluation function and the mutation model.The evaluation of solution is associated with both values of error function and gradient model corresponding to the certain solution, so that solutions away from local minimum are highly evaluated.The sensitivity of error function to network parameter is imported to form a self-adapting mutation model, which is powerful to diminish error function.Both modifications help to drive solutions out of local minimum.A case study of a real industrial process shows the advantage of the improved GA to overcome local minimum and to accelerate the training process.展开更多
针对一辆小型燃料电池电动车的2 k W质子交换膜燃料电池(PEMFC)动力系统,利用遗传算法优化的BP神经网络建立其电压输出特性模型,将PEMFC部分实测数据作为遗传算法优化的BP神经网络的训练样本对其进行训练,利用训练好的神经网络对电堆电...针对一辆小型燃料电池电动车的2 k W质子交换膜燃料电池(PEMFC)动力系统,利用遗传算法优化的BP神经网络建立其电压输出特性模型,将PEMFC部分实测数据作为遗传算法优化的BP神经网络的训练样本对其进行训练,利用训练好的神经网络对电堆电压输出特性进行预测,并与实验数据进行对比,结果显示:网络预测的输出电压与实测输出电压之间的最大相对误差均保持在4%之内.展开更多
文摘以大豆叶面积指数(Leaf area index,LAI)反演为研究目标,利用PROSAIL模型和遗传算法优化后的BP神经网络模型,分别对重组自交系(Recombinant Inbred Lines,RIL)和自然野生大豆种群的LAI进行反演。结果表明,在对人工定向培育的RIL大豆种群冠层叶片LAI反演中,PROSAIL模型表现出了更优异的反演能力,而对品种繁多的自然野生大豆种群LAI反演中,遗传算法优化后的BP神经网络模型表现出了更好的适用性,并且上述2种模型在始粒期(R5)时性能最佳,PROSAIL模型和遗传算法优化后的BP神经网络模型R2分别为0.89和0.85,RMSE分别为0.11和0.13,EA均为97%,典型生育期内的反演性能均优于全生育期综合反演性能。因此,针对同一农作物不同种群的表型特征反演,需要根据研究对象的特征来选择合适的模型,以便于精确的估测大豆长势情况,为农作物的规模化育种监测提供数据支持。
文摘The training process of Back Propagation Neural Network (BPNN) is easily converged at a local minimum, which slows the training process sharply.In this paper, an analysis is given to the chief formative reason of local minimum, and an improved Genetic Algorithm (GA) is introduced to overcome local minimum.Most BPNNs use Sigmoid function as the transfer function of network nodes, this paper indicates that the flat characteristic of Sigmoid function results in the formation of local minimum.In the improved GA, pertinent modifications are made to the evaluation function and the mutation model.The evaluation of solution is associated with both values of error function and gradient model corresponding to the certain solution, so that solutions away from local minimum are highly evaluated.The sensitivity of error function to network parameter is imported to form a self-adapting mutation model, which is powerful to diminish error function.Both modifications help to drive solutions out of local minimum.A case study of a real industrial process shows the advantage of the improved GA to overcome local minimum and to accelerate the training process.
文摘针对一辆小型燃料电池电动车的2 k W质子交换膜燃料电池(PEMFC)动力系统,利用遗传算法优化的BP神经网络建立其电压输出特性模型,将PEMFC部分实测数据作为遗传算法优化的BP神经网络的训练样本对其进行训练,利用训练好的神经网络对电堆电压输出特性进行预测,并与实验数据进行对比,结果显示:网络预测的输出电压与实测输出电压之间的最大相对误差均保持在4%之内.
