# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 1 Introduction
It can effectively improve the robustness and adaptability of the control system, improving the control performance of the system [13; 14; 15]. This study proposes a novel Diagonal Recurrent Neural Network (DRNN) hybrid controller based on real-time database structure of memory, using the DE technology to build the algorithm execution mechanism, innovatively put forward the concept of advanced control execution carrier transformation, and realize the implementation of new techniques of neural network control algorithm in low computing performance controllers. Here, we provide a brief tutorial on DN technology. The idea of DE technology is to construct a computing environment that can explain and operate the configuration files in the different control station. The prerequisite for establishing this technique is that all technical characteristics of the control configuration can be represented in a fixed data structure. DE technology includes a real-time database structure based on memory, and software organization technical specification based on a Multi-Agent System (MAS). The data model of the control configuration is installed in the real-time database, and the DE completes all the tasks of the control station through periodic data updates. The MAS software organization is used as the transaction processing mechanism of the real-time database, which can automatically motivate specific agents from the changes of specific data, and accept the related real-time data update request. From the working principle of the Data Engine, it can be seen that the configuration of the different control station in the DE has been transformed into a real-time database with unified memory data update calculation and management issues [16; 17; 18], thus solving the problem of programming difficulties of collaborative control of heterogeneous control devices. IAPlogic (IAP: Industry Automation Platform) [19], a control logic configuration software corresponding to the DE, uses a unified graphical configuration programming language to describe the controlled object, provides intuitive configuration and transparent data processing, and can significantly reduce development debugging costs. Built on such fundamental theoretical of DE, we have seen various innovations. With the help of this research, we can realize the complicated calculation and application of neural network control algorithms on various computing platforms such as PLC and PC, and support the neural network control algorithm to learn, train, and test on the lower computer, and break through the barriers of the PLC controller which can only carry on simple logic operation and control, greatly improving the system operation and control of real-time performance and stability, while supporting online configuration and debugging optimization, which helps to reduce the control system development and operation and maintenance costs [20; 21]. The main reason for using PLC as the execution equipment is that it has very high reliability, stability, and affordability [22]. However, for a long time, PLC has been identified as a class of equipment that is not suitable for process control and complex network control. Therefore, the significance of this study is very important. To demonstrate the feasibility and superiority of the combination of PLC, IPC, and DE technology, a novel diagonal recurrent neural network hybrid controller was made in this paper and a DC motor speed measurement and control system is built. The neural network control strategy can run directly in the PLC, IPC-based control station of the speed control system, the use of methods is no different in nature from the conventional control. There is no longer a need to configure a high-end computer running independent advanced control software to operate an advanced control algorithm. The real-time database structure based on memory makes the diagonal recursive neural network controller not need offline training to obtain its optimal parameter value so that it can track the actual speed signal in time.