Construction of computer model for enterprise green innovation by PSO-BPNN algorithm and its impact on economic performance

Abstract

The present work aims to analyze the elements that affect corporate green technology innovation and investigate a method suitable for predicting and evaluating corporate performance. First, the elements of green technology innovation and their relationships are analyzed and explained. Then, the Complex Adaptive System (CAS) theory is introduced. On this basis, a computer model for the driving mechanism system of corporate green technology innovation is constructed on the Recursive Porus Agent Simulation (Repast) platform. Finally, the Backpropagation Neural Network (BPNN) model is optimized by Particle Swarm Optimization (PSO), constituting the PSO-BPNN algorithm to evaluate corporate performance. The results of network training and simulation demonstrate that compared with traditional BPNN, PSO-BPNN achieve a faster convergence speed and fewer errors. Besides, the actual output value has a tiny difference from the expected value, showing the application potential of this algorithm in corporate performance prediction. Moreover, the driving factors of green technology innovation greatly affect the profitability and performance of enterprises. Given insufficient corporate profit margin, continuous technological innovation activities can ensure the normal operation of enterprises. A smaller corporate tax rate can shorten the time for the system to reach equilibrium. When the corporate tax rate is above 0.2, the system takes longer to reach equilibrium. In addition, the public opinion coefficient directly affects the time needed for the system to attain equilibrium. When the public opinion coefficient is within 50,00 ~ 6,000 interval, the time that the system takes to reach equilibrium changes significantly. Furthermore, corporate internal and external driving factors have a direct effect on corporate green technology innovation and performance. The research findings indicate that the PSO-BPNN algorithm is of vital practical value to corporate performance evaluation.

The Impact of Green Innovation on Enterprise Performance: The Regulatory Role of Government Grants

“…On the other hand, they explored the impact of green product innovation on enterprise performance, arguing that green innovation brings cost not only efficiency but also profitability to firms and that the pursuit of green product innovation can better contribute to enterprise performance in terms of both cost efficiency and profitability even in a dynamic environment [21][22][23][24][25]. There is rich research literature exploring green innovation from different theoretical perspectives, and studies from a strategic flexibility perspective have found that green innovation can contribute to improved enterprise performance [26][27][28].…”

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

“…Particle Swarm Optimization (PSO) is a heuristic search technique with simple implementation, high global search capability, and superior performance [ 34 ]. PSO simulates the foraging behavior of birds and is used to solve optimization problems [ 35 ].…”

An Optimized BP Neural Network Model and Its Application in the Credit Evaluation of Venture Loans

“…The basic idea of PSO improving the BP algorithm is to analyze the fitness of each individual in the group to the environment, find the best location in the area where the individual is located, and use the cooperation and information sharing of each individual in the group to move to the best location in the area. The algorithm treats each individual in the swarm as a particle without volume and mass, and each particle flies in a given search space at a certain speed, thus realizing a dynamic iterative update [ 25 , 26 ].…”