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Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Machine Learning Optimizers
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AI
DOI：10.1007/978-3-032-02003-1_11.png)
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Abstract
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This work explores an extension of Machine Learning (ML)-optimized Piecewise Polynomial (PP) approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers within TensorFlow, we introduce a framework that minimizes elastic strain energy in cam profiles, leading to smoother motion. Experimental results confirm the effectiveness of this approach, demonstrating its potential to Pareto-efficiently trade approximation quality against energy consumption.
Keywords:
Piecewise Polynomials
Gradient Descent
Approximation
TensorFlow
Electronic Cams
Energy Optimization
Journal
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Papers:
11
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