Abstract
This paper introduces a novel integration of Müntz polynomials into the Least Squares Support Vector Regression framework for addressing fractional optimal control problems. By utilizing Müntz basis functions as the mapping mechanism to project the problem into a higher-dimensional space, the proposed method reformulates the optimization challenge and resolves it efficiently through Maple's optimization tools. The effectiveness of this technique is validated via numerical experiments on benchmark fractional optimal control cases. Outcomes reveal that the approach delivers high precision in solving these problems, surpassing existing techniques in terms of accuracy and efficiency.
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