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Application of artificial intelligence based on the fuzzy control algorithm in enterprise innovation
1School of Business, Macau University of Science and Technology, Macau, 999078, China.
Heliyon
|March 28, 2024
Summary
This study introduces an intelligent strategy using Q-learning and Takagi Sugeno Fuzzy Control (Q-TSFC) for enterprise innovation. The AI approach enhances decision-making, boosting customer satisfaction and performance efficiency.
Area of Science:
- Computer Science
- Business Administration
Background:
- Artificial Intelligence (AI) is increasingly vital for enterprise innovation (EI) and competitiveness.
- Dynamic markets necessitate AI for effective decision-making in challenging business environments.
Purpose of the Study:
- To propose an intelligent strategy combining Q-learning and Takagi Sugeno Fuzzy Control (Q-TSFC) for enhanced enterprise innovation decision-making.
- To develop a framework that integrates adaptive learning with fuzzy logic for handling market uncertainty.
Main Methods:
- Implemented Q-learning to maximize enterprise profit through adaptive learning and exploration.
- Utilized Takagi Sugeno Fuzzy Control (Q-TSFC) to process learned Q-values and linguistic conventions for decision-making.
- Developed a decision-making framework to manage imprecise market trend data.
Main Results:
- Achieved 96.5% customer satisfaction ratio.
- Reached 96% enterprise performance efficiency.
- Generated 48% cost savings and an R² of 0.83.
Conclusions:
- The Q-TSFC algorithm effectively improves decision-making in enterprise innovation.
- The proposed AI strategy enhances customer satisfaction, performance efficiency, and cost savings.
- The approach successfully handles linguistic uncertainty in market trends for better business outcomes.
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