<![CDATA[The manufacturing industry faces great challenges in improving the efficiency and effectiveness of automated control systems. This research aims to develop a neural network-based optimization formula that can overcome the limitations of conventional control methods. The method used in this research is gradient descent optimization applied to an objective function with certain constraints. The results show that this optimization method is effective in achieving the optimal value of ? that is close to the target with high precision, while the control variable ? remains stable throughout the iterations. The implication of this research is the improvement of the reliability and stability of automatic control systems in the manufacturing industry, which has the potential to significantly increase productivity and operational efficiency. Thus, this research makes an important contribution to the field of control system optimization and opens up opportunities for further development with the integration of more sophisticated optimization techniques.]]>
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