Pitfalls of Optimization and Learning Control
What data analysis and AI can and cannot do! We will propose accurate analysis methods.
When unexpected situations arise, the conditions for optimization and the training data for AI and machine learning may become disconnected from reality. At our company, if you provide us with your data and the desired outcomes, we will conduct experimental analyses to evaluate the potential for anomaly detection and rule discovery for predictive diagnostics. We will also propose visualization methods to effectively present the results of the analysis. [Background and Issues] - With the proliferation of various devices, collecting information related to the environment, device operations, and human behavior has become easier, enabling precise control based on that data. - However, when situations change rapidly, as they have in recent years, and the underlying assumptions collapse, there is a high risk that the results of optimization or learning may not reflect reality. *For more details, please refer to the PDF document or feel free to contact us.
basic information
【Possible Countermeasures】 ■ Identify the conditions under which the model's assumptions hold and limit the scope of use ■ Collect data under changed conditions → Rebuild optimization models/learning models ■ Accumulate data through daily activities → Incorporate optimization/learning and establish a system for continuously updating the model *For more details, please refer to the PDF document or feel free to contact us.
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Applications/Examples of results
For more details, please refer to the PDF document or feel free to contact us.
