In many systems, the objectives of operation aren’t always straight-forward. Achieving a unique balance of multiple aspects across a system, while configuring standard control options to realize this unique behavior is usually difficult, if not impossible. The Griffin AI Toolkit makes this process easy, by allowing custom cost and constraint functions to be written into its pre-packaged optimization component. Using the system described in the previous tutorial (Industrial AI – Part 3: Fine-Tuning Optimizer Performance in the Griffin AI Toolkit), this video shares an example situation of enforcing the desired movement of selected input parameters using custom constraint and cost functions.
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