An Improved Neural Network Algorithm and its Application on Agricultural Information Degree Measurement in Hebei Province

The agricultural information level is on the initial stage in Hebei province, so we should pay more attention to its construction. And on this basis we can find out the influencing factors and corresponding countermeasures. To evaluate the agricultural information degree in Hebei province scientifically and accurately, this paper proposes the improved BP neural network model which imports the adjustable activation function and the Levenberg-Marquardt optimization algorithm. The improved model not only can simulate the expert in evaluating the agricultural information degree and avoiding the subjective mistakes in the evaluation process, but also enhance the learning accuracy and the algorithm convergence speed greatly. The evaluation of 11 cities in Hebei province shows that the results are reliable and the method to evaluate the agricultural information degree is feasible.