In this article, the LS-SVM model was established against the working stability control for the vacuum feeding device. Based on the experimental sample data, the learning and training was performed. The actual operating results of the control system proved its efficiency and reliance.

The recursive compensatory fuzzy neural network model was established against the characteristics of temperature control for film laminating machine. The neural network can be used to construct the fuzzy system, and the self-adaptive and self-learning capability of neural networks was used to automatically adjust fuzzy system parameters, BP network could be learned and trained by the gradient descent algorithm. Based on the test data for the study and testing of network, system error is less than the national standard error requirements, the results proved the effectiveness and feasibility of the algorithm.

As a new product of integration of mechanics and electrics, electric boiler is acomparatively environment-friendly and energy-saving heating equipment at the present time. For the electric boiler, the performance of intelligent temperature control will directly affect the effect of energy saving, satisfaction of consumers andthe competitive ability of products in the market. Temperature control with large timelag and disturbances is widely used in the production of industry and daily necessities.It is affected by many factors such as the control plant and production condition, so itis very difficult to build the accurate mathematical model. Thus, the conventionalcontrol method such as PID control can’t meet requirements. Fuzzy control instead of establishing a precise mathematical model by simulation comparison of electric boiler control objects have very good results

The Neural Network Toolbox in MATLAB is a powerful instrument of analyzing and designing a neural network system. RBF Neural Network has small computational burden and fast learning rate and is not liable to be trapped by local minimal points etc. So it is an effective means to identify and model a system. In this paper, the Neural Network Toolbox in MATLAB and RBF Neural Network are combined to solve the problem of modeling the pressure in oilfield test well systems and the result is excellent.