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.

Improved GWO and its application in parameter optimization of Elman neural network

“…Chaotic mapping strategy and opposition learning strategy were widely used in initial population. In the chaotic mapping strategy, Luo et al [21] have proposed tentline coupled chaotic mapping to initialize the population, which ensured that the GWO algorithm generated diverse populations; Another improved GWO algorithm used a two-dimensional chaotic map to initialize the population [22]; Zhao et al have generated GWO initial population through Chebyshev chaotic mapping, ensuring the diversity of the initial population and enhancing global search ability of GWO [36]; In addition, some studies have integrated chaotic maps; Xu et al have applied integrated mapping systems (CLS) to GWO to increase its population diversity and accelerate the convergence of the algorithm [37]. Besides chaotic mapping, the pseudo-antithesis number generation method based on opposition learning strategy was used to improve the distribution of population [38]; Another improved GWO also generate its opposition wolf by lens imaging learning strategy [39].…”

“…However, different chaotic maps have different effects. To find the optimal value quickly, we analyzed and compared Sobol, Logistic, Iterative, and Circle maps [21,22] in Fig 1.…”

“…GWO is inspired by the wolves hierarchy and the hunting process. GWO has three leaders who are responsible for guiding the wolves to attack, delivering attack information and leading the pack to encircle [21]. During the iterative, the three wolves continuously update their positions and thus search for the global optimum.…”

Evolutionary game and simulation analysis on quality supervision of low-carbon renovation of high-carbon emission enterprises under the reward and punishment mechanism

“…Through the literature review and existing policy review, it is found that most of the low-carbon renovation policy documents on existing facilities are issued in 2022. However, the existing research mainly focuses on the evaluation of low-carbon renovation and the research on low-carbon emission reduction strategies of enterprises (Qu et al, 2021;Bai and Zhang, 2022;Jiang et al, 2020b;Dong et al, 2021). The research on the lowcarbon renovation of high-carbon emission enterprises has only begun to receive attention recently, and related research has begun to expand.…”