The Internal Model Control of Greenhouse System Based on LS_SVM
The greenhouse environment has the characteristics of nonlinear, time-varying, large time delay, so it is difficult to obtain satisfactory result using conventional control method. The internal model control system method and the algorithm based on LS_SVM is proposed for the problem. The simulation experiment was performed on the greenhouse environment control system. Results shows that the controlled object internal mode and inverse model have very high precision and generalization ability and the control system has not only good control performance but also strong anti-interference ability and robust performance which has the great value to application and extension
Abstract Machine learning techniques have been widely applied to production processes with the aim of improving product quality, supporting decision-making, or implementing process diagnostics. These techniques proved particularly useful in the investment casting manufacturing industry, where huge variety of heterogeneous data, related to different production processes, can be gathered and recorded but where traditional models fail due to the complexity of the production process. In this study, we apply Support Vector Representation Machine to production data from a manufacturing plant producing turbine blades through investment casting. We obtain an instance ranking that may be used to infer proper values of process parameter set-points.