# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 5 Conclusions  
This paper presents a DE technology based on the real-time database and multi-agent system and discusses the concept and operation mechanism in detail. Developed advanced control strategy components based on this technology have the same data structure and the configuration mode with conventional control algorithm components; it can be directly used in PLC, IPC, and another machine for visual operation and debugging, with no need to configure a separate advanced control software to perform advanced control algorithm, improving the real-time performance of the online operation of the advanced control strategy to some extent. More importantly, DE technology: (1) enables the algorithm model to have a configuration programming environment that can be migrated and shared among different types of computing platforms; (2) realizes the interconnection and interworking of data between heterogeneous equipment control devices in engineering applications; (3) significantly reduces the development, operation, and maintenance costs of industrial applications for advanced control strategies; and (4) derived an advanced control technology in actual industrial control system. A novel DRNN hybrid controller was made in this paper to demonstrate the feasibility and superiority of the combination of PLC, IPC, and DE technology. In Section 3, the topology structure of the diagonal recurrent neural network algorithm, the architecture of the controller, and the realization method were described in detail. Also, a numerical simulation of nonlinear equipment with known reference model was made for the DRNN controller proposed in this work. The results showed that the off-line training with extensive input data was not necessary, the system has a good self-regulation, adaptive ability, can quickly return to a stable state, and has strong anti-interference ability with the real-time dynamic nonlinear learning law of the weight vector. The practical application research with motor speed measurement and control platform were made in this work, presented in Section 4. The results showed the good performance of the DRNN hybrid controller and the validity of the procedure to calculate the motor speed signal with motor parameter uncertainties. Also, the transient response from the steady state provided by the DRNN control has been best compared to conventional PID control. The control system based on DE technology realizes the graphical configuration of the neural network control algorithm by way of dynamic reconfiguration, providing an innovative application method for neural network intelligent control theory in engineering implementation, which is a major contribution of this paper. The experimental results in Section 4.3 show that dynamically changing the topological structure of the control strategy will not cause much disturbance to the steady-state system. Therefore, the dynamic reconfiguration technology is no longer limited to the exception of the control system and the change of process conditions; it extends to the field of the dynamic evolution technology of intelligent control domain control algorithm, including the long-term continuous online optimization of the control system. Despite all the achievements that have been made for DRNN control systems in the past decades, more efforts are still needed in the future, especially in the field of industrial control. In future studies,the DE architecture is planned to be deployed in the cloud of the Internet of Things. It will also be used to build the industrial cloud platform application of a neural network control algorithm, and research its close relationship with other systems such as the Internet of Things and multi-agent systems. This applied research will then reveal the values of control systems based on DE with an advanced control algorithm in broader perspectives. Conceptualization, S.Z. ; methodology, S.Z., C.B. and Y.L.S. ; software, C.B. and Y.L.S. ; validation, C.B. and Y.L.S. ; formal analysis, C.B. ; investigation, S.Z., C.B. and Y.L.S. ; resources, C.B.