Online Predicative Optimum Control of the Temperature of a Cold Storage Based on the Two-Stage RBF Neural Network

The predicative optimum control of the temperature of a cold storage has a wide application in agricultural engineering especially in fruit and vegetable cold storage. In recent years, the advanced control technology was used for the cold storage. But there is still a lot of shortcomings. One of the main problems is that the traditional methods can't realize the online predicative optimum control of a cooling system with simple and valid methods. A RBF neural network has a strong ability in nonlinear function and a good inter value performance, and it has a higher training speed. Therefore a two-stage RBF neural network was proposed. Combining the measured values and the predicated values, the two-stage RBF neural network was used for the online predicative optimum control of the temperature of a cold storage. The application result of the new methods in a real cold storage showed a great success.