Variable conjugate gradient algorithm and its application in prediction of the total yield of main agricultural products of China
变共轭梯度算法及其在农产品总产量预测中的应用

To improve the prediction accuracy of BP network, the variable conjugate gradient (VCG) algorithm was proposed. The new approach improved BP algorithm from two aspects: activation transfer function and learning rule. Its convergence was analyzed and briefly proved. The variable conjugate gradient algorithm was applied to train a multilayer neural network to predict the total yield of main agricultural products of China, which overcame the slow rate of convergence and poor generalization capability of the traditional BP algorithm.