Impact of bank research and development on total factor productivity and performance evaluation by RBF network

The research aims to improve the competitiveness of Chinese banking enterprises and China's banking industry in an increasingly competitive economic globalization (EG) environment. This paper takes research and development (R&D) investment as a practical method to improve the competitiveness of banks. Firstly, it introduces the enterprise sustainable development (SD) theory and total factor productivity (TFP) theory and analyzes the importance of R&D innovation to the development of banks. Then, radial basis function (RBF) network is proposed to test the impact of bank R&D investment on enterprise performance. Therefore, a bank performance evaluation (PE) system based on the Internet of Things (IoT) is established. Secondly, block matrix (BM) and the incremental learning algorithm are used to optimize the RBF network. The RBF network model is further improved, and the RBF network model based on the IoT cloud platform (CC-RBF) is proposed, which improves model convergence speed and accuracy. The results show that (I) BM and incremental learning algorithm can greatly simplify the calculation and improve the efficiency of RBF network model. (II) Bank R&D investment will significantly improve TFP. (III) The proposed CC-RBF network model can improve prediction accuracy and reduce the model training time. The research content provides a reference for analyzing the impact of bank R&D investment on bank performance.