Thank you for posting this great video, Professor!
dr.alikhudhair
the best demonstrating way i have even seen
人類之信仰現代の精神
Excited to see how this can be used for IK in robotics, thanks for your time.
georgekerwood
Thank you for posting this great video, Professor! This technique seems quite simple, yet extremely powerful. I am curious what types of otherwise intractable systems might become tractable if one uses this technique with more powerful modern NNs.
mattkafker
Hi Professor Kutz,
I was wondering what the intuition was behind choosing your three activation functions (lines 25-27). I've seen logsig and tanh functions used a lot for the hidden layers of regression networks but I'm not sure what the radial basis function brings to the table and why it is placed in the middle.
In addition, I thought the output layer for matlab's regression networks is 'purelin' by default so is the third hidden layer a bit redundant? Or is there a reason you chose to have the last hidden layer to have a purelin activation function?
chrisprasanna
Great video Nathan, I recently got your other book "Data-Driven modeling &Scienfitif computation" I'm enjoying
Anorve
Hi, thanks for the interesting vedio. Is there a method that we could include the "b; sig; r;" as input paramaters for nerual network?
suningok
I was searching around for a neural network architecture that works by taking in an input multi-modal "image" at time T, and predicts multi-modal output at tiime T+1. But, how would you represent actuators in this scheme? The position of a motor as an input and predicted output (y_a1 - x_a1) ... would that diff trigger a motor to actuate the world? since LLMs are all based on next-frame prediction, it seems that you could make a generic box and hook up some inputs to pixels, other inputs to positions of actuators; but it's mysterious how the output would control the actuators. ie: if a neural network is trying to minimize surprise, then it can minimize surprise by actuating the world on its own.
rrrbb
What might be the advantage of training a NN to solve an IVP? It seems that to train a NN to solve for the IVP one must already have a way to generate trajectories.
TheSugarDealers
Does someone know how this approach changes when you also consider an input, u, in your system?
optimizacioneningenieria
Most complicated example I’ve seen in my entire life. And I’m very old.
