System and method for determining harmonic contributions from non-linear loads using recurrent neural networks

2006 ◽  
Author(s):  
J. Mazumdar ◽  
R.G. Harley ◽  
F. Lambert
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Non Linear

scholarly journals Scalable Polyhedral Verification of Recurrent Neural Networks

2021 ◽  
pp. 225-248
Author(s):  
Wonryong Ryou ◽  
Jiayu Chen ◽  
Mislav Balunovic ◽  
Gagandeep Singh ◽  
Andrei Dan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Networks ◽  
Sensor Data ◽  
Motion Sensor ◽  
Use Case ◽  
Prior Work ◽  
Non Linear ◽  
Speech Classification ◽  
Sampling Optimization

AbstractWe present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.

Identification and control of class of non-linear systems with non-symmetric deadzone using recurrent neural networks

2014 ◽  
Vol 8 (3) ◽  
pp. 183-192 ◽  
Author(s):  
José Humberto Pérez-Cruz ◽  
Isaac Chairez ◽  
Jose de Jesús Rubio ◽  
Jaime Pacheco
Keyword(s):  
Neural Networks ◽  
Linear Systems ◽  
Recurrent Neural Networks ◽  
Non Linear ◽  
Non Linear Systems ◽  
And Control
Sign in / Sign up

Export Citation Format

Share Document