Evaluation for Development Effect of Enterprise Innovation with Neural Network from Low-Carbon Economy

Abstract

China has resolved to pay more and more attention to the sustainability of ecological construction and social development. The country has proposed a low-carbon economy policy; development and management of enterprises are also actively responding to the call for a low-carbon economy. Through a series of innovations and changes, it intends to realize its own low-carbon production and business development, thereby promoting transformation and optimization of industrial institutions. Innovation is a significant driving force of national economic development. In order to build China into an innovative country, there is a need of national innovation strategy and innovation system. The state vigorously promotes the implementation of innovation-driven development strategies. Enterprises are not only an important carrier for the state to implement mass entrepreneurship and innovation but also a powerful driving force for development of economy. There is a dire need and importance to identify techniques to evaluate impact of corporate innovation on development effects with low-carbon economy. Considering this aforementioned problem, random initialization parameters in back propagation network are used. This evaluation process can easily lead to the functioning of the model falling in the local optimum. The work designs an impact evaluation model (IGWO-BP) with improved gray wolf algorithm (IGWO) in order to optimize BP network. To improve optimization ability for GWO, the study uses the chaotic map to initialize the population. The nonlinear convergence factor strategy as well as dynamic weight strategy is used to promote GWO, so as to optimize initial weight as well as threshold point for the BP network. The IGWO-BP is applied to perform evaluation of the impact in case of enterprise innovation relevant to development effects with low-carbon economy.