top | item 34969663 (no title) thearn4 | 3 years ago In my area, every controls engineer says "we can just use a PID".99% of the time it seems they're not wrong, despite control engineering being a pretty large area of research. discuss order hn newest sitkack|3 years ago And a simple PID controller built with op-amps looks an awful lot like a simple neural network.http://www.ecircuitcenter.com/Circuits/op_pid/op_pid.htmhttps://www.nutsvolts.com/magazine/article/the_perceptron_ci... mordae|3 years ago Isn't gradient descent basically PID over parameters? And tricks like momentum basically a low-pass filter integrated in the PID loop? It's quite weird how not that many concepts from analog electronics domain have gotten carried over to ML. elevation|3 years ago I'm nominally familiar with PID loops but am not a control engineer. What other tools would you commonly bring to bear when a PID is not applicable?
sitkack|3 years ago And a simple PID controller built with op-amps looks an awful lot like a simple neural network.http://www.ecircuitcenter.com/Circuits/op_pid/op_pid.htmhttps://www.nutsvolts.com/magazine/article/the_perceptron_ci... mordae|3 years ago Isn't gradient descent basically PID over parameters? And tricks like momentum basically a low-pass filter integrated in the PID loop? It's quite weird how not that many concepts from analog electronics domain have gotten carried over to ML.
mordae|3 years ago Isn't gradient descent basically PID over parameters? And tricks like momentum basically a low-pass filter integrated in the PID loop? It's quite weird how not that many concepts from analog electronics domain have gotten carried over to ML.
elevation|3 years ago I'm nominally familiar with PID loops but am not a control engineer. What other tools would you commonly bring to bear when a PID is not applicable?
sitkack|3 years ago
http://www.ecircuitcenter.com/Circuits/op_pid/op_pid.htm
https://www.nutsvolts.com/magazine/article/the_perceptron_ci...
mordae|3 years ago
elevation|3 years ago