# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 1 Introduction  
In the complicated industrial systems, the control object model of the system is often unpredictable because of the frequent interference of the external environment, the input condition variable, and the high coupling degree and time variation. Conventional control, such as PID control method, is difficult to achieve the desired control performance or even cannot meet the control requirements of complex industrial systems [1]. The use of advanced control can overcome the shortcomings of conventional control, and bring significant economic benefits to industrial applications when applied properly. Advanced control refers to those uses the intelligent control and optimal control theory to evolve into a type of control method that project implementation is more difficult and the control structure is more complicated than the traditional control method [2], ensuring the efficiency of industrial control systems, improving productivity, and reducing energy consumption. Due to the changing production conditions and the change of product technical standards, it is often necessary to continuously optimize and update the relevant control strategies in the control system. However, the existing advanced control strategy is very difficult to coordination with thecontrol system, the implementation and update efficiency of the control strategy is low, and has brought many challenges to industrial production [3; 4]. The main reason for this phenomenon is that the current advanced control technology mostly uses an independent software system through the OPC way and DCS system for data communication, and communicates with the DCS system through OPC mode. In [5], with OPC technology, the advanced control strategy implemented in MATLAB/LabVIEW is integrated with the network control system. Paper [6] studies a kind of boiler system based on OPC application and uses OPC technology to communicate the control algorithm developed by Simulink with WICC of Siemens industrial control configuration software. Paper [7] developed an OPC client user connection based on.Net advanced control software and DCS system. Moreover, the traditional advanced control algorithm software package is usually written in a variety of high-level language integrated into the controller, its execution process often uses the 'black box' feature, which makes it difficult for users to perform monitoring and online debugging of advanced control algorithms, and the application effect is not ideal. At present, it is precisely because the algorithm execution mostly adopts the method of integration and plug-in. The independent algorithm model operating environment does not have the ability to transplant and share with each other in many different types of computing platforms, resulting in the high cost of development, operation, and maintenance of advanced control strategy in industrial applications. So, how to effectively implement and apply the advanced control strategy in the industrial control system and improve the stability and real-time performance of the whole system are the problems that need to be solved by the whole control engineering community. Neural network control technology, as a kind of advanced intelligent control algorithm, can fully approximate the unknown nonlinear dynamic behavior of an object, you can make up for the limitation of the conventional PID method, and is used to solve some nonlinear system control problem that is difficult to modeling [8; 9; 10; 11; 12]. However, the commonly used multi-layer forward neural network is a static network, which must be used in the control system to determine the object model. For systems with little or no prior knowledge, this method is inaccurate and affects the identification and control. Diagonal recurrent neural network has the internal feedback mechanism that can be used for the dynamic response of the memory system, and it does not need to have detailed knowledge of objects when being applied to the identification and control of a system.