Research on cost budget control strategy of biomechanics based on fuzzy logic and neural network
Abstract
This article proposes a biomechanical cost control strategy using fuzzy logic and neural networks. A cost model for the biomechanical system is established, and a fuzzy logic strategy addresses its uncertainty and complexity. By integrating neural networks with fuzzy logic, the accuracy and adaptability of budget control are enhanced. Experimental results show the proposed strategy outperforms traditional methods (GNN-GA, DP-PSO, A-DRL) in cost savings, system stability, and response time. The deviation between target and actual costs is minimal, confirming the strategy’s efficiency and accuracy. This integrated approach offers significant cost savings, strong adaptability, and real-time performance, providing new solutions for biomechanics budget control with practical applications and theoretical value.
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