Prediction and Control of Input and Output for Industry–University–Research Collaboration Network in Construction Industry

Abstract

An unreasonable allocation of resources has led to a low rate of output in the industry–university–research collaboration network. A solution to this problem is to control and predict the input and output. However, the network has the characteristics of strong nonlinearity and insufficient samples. It is difficult for the existing control methods to migrate to collaboration networks because the traditional control methods, including Proportional–Integral–Derivative (PID) control and Model Predictive Control (MPC), are usually not applied to the system with strong nonlinearity and the controlled system needs to have specific parameters, while the modern control methods, including feedforward control and feedback control, have their limitations in both parameters and other aspects. In addition, there is a lack of research on the control and output prediction of collaboration networks, and there is no effective and applicable scheme for the control and prediction. Considering the nonlinearity and insufficient samples of the collaboration network, a Feedforward Control–Feedback Control Model based on the Multi-Layer Perceptron (FCFCM-MLP) is proposed in this paper. Adopting the controller structure of the Grid Search-Multilayer Perceptron (GS-MLP), a control block diagram, a feedforward controller, a feedback controller, and prediction methods such as Harris Hawk Optimization-Support Vector Regression (HHO-SVR) are designed for the FCFCM-MLP, which effectively realizes the feedforward control, feedback control, and prediction of inputs and outputs. In this paper, simulation tests on output-feedback tracking control are conducted with real statistics of papers jointly produced by the industry–university–research collaboration network in the construction industry. The results show that the proposed model has obvious effectiveness. Specifically, compared with the model composed of other controller structures and prediction methods, the optimal model Particle Dynamic Multiple Perturbation_Butterfly Optimization Algorithm-Support Vector Regression_Grid Search-Multi-Layer Perceptron (PDM_BOA-SVR_GS-MLP) obtained in this paper can minimize the predictive control error and effectively improve the control accuracy.