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Thursday, September 21, 2017

Dynamical Machine Learning

Good piece, which combines process and sensors and IoT. Much more detail at the link. Reminds me comments made here before, about how we used classic process control in similar ways, and tried to link it to AI.  As long as it was a closed loop system, which few systems are, but if you can control the degree to which it is.  ..... There is the challenge and often the value in trying.

IoT Data Science & “DML” – match made in heaven?
By PG Madhavan  in DSC

DML stands for “Dynamical Machine Learning” (more in the book, “SYSTEMS Analytics for IoT Data Science”, 2017). This match is not surprising once you realize that DML & IoT are both based on the venerable Systems Theory. Let us dig deeper . . .

Consider IoT for industrial applications. A machine is instrumented with sensors, data are collected in real-time (or at intervals), communicated to the cloud where IoT Data Science techniques predict machine condition which results in an action, if necessary, such as repair action on the machine. This is a classic “closed-loop” system. The theory that abstracts and governs this closed-loop system is the subject matter of Systems Theory, an undergraduate engineering topic.

Systems Theory is broad and deep – in the past 70 or so years, a great body of work has been developed from deep theory to day-to-day applications such as GPS in your mobile phones, controlling massive chemical plants or Dreamliner airplanes. Systems Theory’s state-space model based methods allow you to describe, estimate/predict and control all parts of a closed-loop system. ... "

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