Research on the Design of Computer Algorithms for Intelligent Decision-Making
Abstract
Against the backdrop of the continuous expansion of intelligent applications, decision-making tasks have put forward higher requirements for the real-time performance, accuracy, and adaptive capacity of computer algorithms. Focusing on the key links in intelligent
decision-making scenarios, such as data perception, feature modeling, model training, rule generation, and feedback optimization, this paper
conducts research on the design of computer algorithms for intelligent decision-making, with emphasis on analyzing the structural composition, core implementation paths, and performance evaluation methods of the algorithm system. The research shows that the design idea combining hierarchical architecture, parameter optimization, and closed-loop feedback can enhance the adaptability of algorithms to complex data
environments and improve the stability and effectiveness of decision-making output.
decision-making scenarios, such as data perception, feature modeling, model training, rule generation, and feedback optimization, this paper
conducts research on the design of computer algorithms for intelligent decision-making, with emphasis on analyzing the structural composition, core implementation paths, and performance evaluation methods of the algorithm system. The research shows that the design idea combining hierarchical architecture, parameter optimization, and closed-loop feedback can enhance the adaptability of algorithms to complex data
environments and improve the stability and effectiveness of decision-making output.
Keywords
Intelligent Decision-Making; Computer Algorithms; Model Training; Parameter Optimization; Performance Evaluation
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PDFReferences
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[3] Yan, Z. F., Shen, Y., Liu, J. F., et al. (2024). Computer network switch decision-making algorithm based on artificial intelligence. Science & Technology Information, 22(11), 74-76.
DOI: http://dx.doi.org/10.70711/frim.v4i5.9379
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