Application of Simulated Annealing Neural Network in Performance Evaluation of Science and Technology Innovation Community

With the "The Belt and Road" strategy deeply rooted in people's hearts, it is an inevitable choice for China and countries (or regions) along the "The Belt and Road" strategy to build a science and technology innovation community. In this study, the high-tech industries of major provinces and cities in China are taken as the research object, and the collaborative innovation process and mechanism of each innovation subject are taken as the research purpose. By introducing the collaborative innovation concept of the innovation community, we construct the innovation performance evaluation index system to optimize the model of the innovation community performance evaluation via using neural networks. The neural network is one of the most widely used algorithms among deep learning algorithms, and this paper mainly analyzes the characteristics of Back Propagation (BP) neural network in the performance evaluation, which has strong local optimization but slow convergence speed. We build a BP neural network based on the genetic algorithm, and this neural network makes the performance evaluation model has the characteristics of strong global optimization and fast convergence speed. However, at the same time, it also has the risk of early convergence and lacks the ability of the global search. On this basis, we propose an improved genetic BP neural network based on simulated annealing in this paper. The improved simulated annealing algorithm not only can reduce error but also has a stronger local convergence and a faster evolution speed, which can obtain a satisfying global optimal solution. When we apply the improved simulated annealing genetic BP neural network in the performance evaluation of the science and technology innovation community, the simulation results show that this algorithm can be a reliable performance evaluation method and easier to obtain the optimal solution than other algorithms. Besides, the feasibility of the evaluation system and method is verified.