BP artificial neural network model is used to predict developing modern agriculture demands for the agricultural scientific research institutions services. Starting from the brief introduction of the usages of BP neural network, we analyzed the demand factors of the agricultural scientific research institutions services and the affective elements of the demands, use the BP neural network model to predict, and then run the BP neural network model on the MATLAB platform, and finally carry out the case studies of Heilongjiang Province.
BP Neural NetworkAmong many different types of artificial neural networks, the error back propagation algorithm -BP algorithm is the most widely used and popular model. BP algorithm, by definition, is calculated from backward to forward. Usually, it has a multi-layer neural network, and the BP network information flows from the input layer to the output layer. Therefore, it is a multi-layer forward neural network. The difference between BP neurons and other neurons is that the transfer function of BP neuron is nonlinear, and the most commonly used function is logsig function and tansig function. Some of the output layer is linear function-purelin with the output A = logsig (W • P + b). After determining the BP network structure, the network can be trained with the input and output sample sets, that is to learn and correct the network thresholds and weights, so that the network can achieve a given output mapping.
The basic idea of BP algorithmThe basic idea of BP algorithm is to assign the initial network weights and threshold, and calculate forward. And then, based on the error between the achieving results and the expected results, network weights and thresholds are modified repeatedly and reversely until the minimum error is achieved. See Figure 1 and 2.
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