以1年生三七(Panax notoginseng)为研究对象,通过正交试验考察光、水、营养物质对三七茎粗的影响,利用麻雀搜索算法(Sparrow search algorithm,SSA)优化4种模型,分别为反向传播神经网络(Back propagation neural network,BPNN)、长短期记忆神经网络(Long short term memory,LSTM)、随机森林(Random forest,RF)和广义回归神经网络(General regression neural network,GRNN),并应用这4种模型对三七茎粗进行预测.结果表明,光照、水肥等非生物因素对三七茎粗具有明显影响,各因素对三七茎粗的影响程度依次为遮光层数>土壤含水量>矿源黄腐酸钾含量>光照时长.SSA-GRNN模型的决定系数最高,为0.865 6,其次为SSA-RF模型、SSA-BPNN模型、SA-LSTM模型;SSA-GRNN模型的MAE和MSE分别为0.064 1、0.008 7,均低于SSA-BPNN模型、SSA-LSTM模型、SSA-RF模型;SSA-RF模型和SSA-LSTM模型的适应度较大,且陷入了局部最优的情况,从而无法达到全局最优解,SSA-GRNN模型的适应度最小且以最少的迭代次数达到了最佳的适应度.
Evaluation of four neural network models optimized based on sparrow search algorithm for predicting the stem thickness of Panax notoginseng
Taking 1-year-old Panax notoginseng as the research object,the effects of light,water,and nutrients on the stem diame-ter of Panax notoginseng were investigated through orthogonal experiments,sparrow search algorithm(SSA)was used to optimize four models,namely back propagation neural network(BPNN),Long short term memory(LSTM),random forest(RF),and general re-gression neural network(GRNN),and these four models were applied to predict the stem thickness of Panax notoginseng.The results showed that non-biological factors such as light,water,and fertilizer had a significant impact on the stem diameter of Panax notogin-seng.The degree of influence of each factor on the stem diameter of Panax notoginseng was shading layer>soil moisture content>potas-sium fulvic acid content from mineral sources>light duration.The SSA-GRNN model had the highest coefficient of determination,which was 0.865 6,followed by the SSA-RF model,SSA-BPNN model,and SA-LSTM model;the MAE and MSE of the SSA-GRNN model were 0.064 1 and 0.008 7,respectively,which were lower than those of the SSA-BPNN model,SSA-LSTM model,and SSA-RF model;the fitness of SSA-RF model and SSA-LSTM model was relatively high,and they were trapped in local optima,making it impossible to achieve a global optimal solution.SSA-GRNN model had the lowest fitness and achieved the best fitness with the least number of iterations.
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