An adaptive nonlinear autoregressive approach for modeling biological signals

2016 ◽  
Author(s):  
Ursula C. Eid ◽  
Fadi N. Karameh
Keyword(s):  
Biological Signals ◽  
Nonlinear Autoregressive

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics

2021 ◽  
Vol 1149 ◽  
pp. 338210
Author(s):  
Kevin Cho ◽  
Michaela Schwaiger-Haber ◽  
Fuad J. Naser ◽  
Ethan Stancliffe ◽  
Miriam Sindelar ◽  
...  
Keyword(s):  
Biological Signals
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