| Title |
Introducing Artificial/Computational Intelligence-Derived Non-Parametric Transfer Functions for the Implementation of Dynamic Circular Economy Decision-Making Systems |
| ID_Doc |
2961 |
| Authors |
Yu, MH; Zheng, JJ |
| Title |
Introducing Artificial/Computational Intelligence-Derived Non-Parametric Transfer Functions for the Implementation of Dynamic Circular Economy Decision-Making Systems |
| Year |
2023 |
| Published |
Electronics, 12, 4 |
| DOI |
10.3390/electronics12041050 |
| Abstract |
With the development of science and technology, resource consumption has increasingly become a social problem. The circular economy is an economic form of a comprehensive utilization of resources. At present, China's circular economy industry has begun to develop towards intensification, scale and specialization. With the deepening of research on the value of a comprehensive utilization of resources, the traditional economic field has undergone tremendous changes. According to the current situation of circular economy industry, an intelligent decision-making system is designed using artificial intelligence technology. Empirically, using the circular economy model and combining the sequence parameters to determine the sequence parameter indicators of the scientific and technological innovation subsystem and the energy saving and emission reduction subsystem, the correlation analysis of each indicator is carried out, and the order degree of the two subsystems is obtained respectively, so as to obtain for the enterprise the degree of synergy of the evolution system of the circular economy synergy of industries and industrial clusters. After repeated exploratory tests, it is found that the calculation has achieved over 98% of the precision of an astute direction, and has specific use and value because of the enormous number of preparing modes and trial dependability. Making a specific commitment to the circular economy is a trusted and logical solution. |
| Author Keywords |
circular economy; open dynamics; input-output; intelligent decision-making; sequential parameter method and intelligent decision-making method; deep learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000939041700001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied |
| Research Area |
Computer Science; Engineering; Physics |
| PDF |
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