Research On BP Neural Network Theory And Its Application In Agricultural Mechanization

BP neural network is one of the most mature and widely used artificial neural network models. As it has the advantages of simple structure, easy to operate, good self-learning capability, effectively solve the approximation problems of nonlinear objective function, etc., which has been widely used in pattern recognition, signal processing, automatic control, prediction, image recognition, function approximation, system simulation and other disciplines and fields. However, BP algorithm also has many deficiencies. For example, the selection of initial learning rate is difficult, the rate of convergence is slow, the volatile appears when close to the optimal solution, and sometimes there is oscillation. It is ineffective extrapolated with growth trend of time series prediction problems. Therefore, it has not only of theoretical significance, but also important application value in further BP neural network systematic study of these issues.It is proved that as long as the hidden nodes of three-layer BP neural network are enough, it has the capacity to simulate any complex nonlinear mapping, so the BP neural network has strong ability of the fitting ability. However, in practice, sometimes people not only care about the fitting effect of neural networks, but also very concerned about the value of the input, which can lead the output to achieve maximum or minimum. This problem is actually based on the optimization problem of BP neural network, by now, the research on this issue has not yet be reported. Although some literatures are referred to as BP neural network optimization, but they are focus on the weights, learning rate and network structure optimization of BP neural network, according to the relationship between BP neural network input and output, and to choose a better output value, which is actually not really optimization, but a simulation, it is to choose an optimal solution from simulation results. Therefore, it has not only of theoretical significance, but also important application value in exploration of real BP neural network optimization method.This thesis aimed to analysis the reasons of BP neural network appeared shortages, and then proposed improved algorithm of BP neural network and a new method for time series prediction. On this basis, BP neural network optimization problems were discussed. Finally, the theoretical research production of BP neural network for prediction of Heilongjiang province agriculture machinery total power and processing parameters optimization of inertia separation chamber of stripper combine harvester with air suction.The results achieved during the research were below:(1) Analysis indicated the reasons of BP neural network algorithm appeared problems and poor extrapolation results of time series forecasting when use BP neural network.(2) This study put forward an improved BP neural network algorithm.It was proposed that each weight corresponds to an improved learning rate of BP algorithm. This algorithm made the negative gradient direction information was more fully utilized, while the learning rate achieved necessary changes. It overcame the fluctuation and oscillation when BP neural network close to the optimal solution, and significantly improved the calculated accuracy; also the latter iterative calculation continued the learning rate of previous iterative calculation, which can improve the learning rate. In addition, the improved BP algorithm was independent of the initial learning rate, which avoided the difficulties of learning rate selection.(3) A new prediction method of BP neural network based time series was presented in this research.First, the prediction shortcomings of BP neural network were indicated, according to the structural features of BP neural network time series prediction, based on Z transform theory, a new activate function was given. And in the BP neural network, as activation function was y=x, y=a+bx was the reason of equivalent to the activation function. Secondly, y=x was derived as the activation function of BP algorithm and the model formula. Finally, through examples calculation, it was showed that with the growth trend for time series prediction, the extrapolation results were not good when unipolar S-function as the activation function, but the extrapolation results were better when y=x as the activation function. In addition, the extrapolation results were not affected by data processing interval when y=x as the activation function, while the extrapolation results were influenced by the processing interval a lot when unipolar S-function as the activation function. And the y=x as the activation function can overcome the shortcomings of prediction problems of unipolar Sigmoid function as the activation.(4) An optimization method was given based on BP neural network. The optimization method was according to unipolar Sigmoid function as the activation function, take the network maximize output for example, the general mathematical model of unconstrained and constrained optimization problem was given based on BP neural network, on this basis, the basic ideas of unconstrained and constrained optimization methods were given based on BP network, the partial derivative of BP neural network output to input was derived, and then the optimization calculation of unconstrained and constrained method was given.(5) The standard BP algorithm was written, BP algorithm was improved, the optimization method of the computer program based on time series forecasting was improved.(6) The application of BP neural network in agricultural mechanization was discussed.First, the total power of Heilongjiang Province was predicted by improved BP algorithm based on programmed time series forecasting procedure, the results showed that the total power value of the next 5 years was given. Predicted results showed high prediction accuracy. Secondly, the BP neural network optimization program was used for suction stripping inertial separation chamber associated receiver to optimize the process parameters, the best technology parameters of separation chamber pressure loss minima was given, the results can provide a theoretical basis for design and optimization of this type inertial separator chamber.