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Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs by Schoenberg, William is a scholarly article available to read on EtoBox.

What is Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs about?

This paper presents a new causal network learning algorithm (FSNN, Feedback System Neural Network) based on the construction and analysis of a non-linear system of Ordinary Differential Equations (ODEs). The constructed system provides insight into the mechanisms responsible for generating the past and potential future behavior of dynamic systems. It is also interpretable in terms of real system variables, providing a wholistic, causally accurate, and systemic understanding of the real-life interactions governing observed phenomena. This paper demonstrates the generation of an n-dimensional ordinary differential equation model that can be parameterized to fit measured data using standard numerical optimization techniques. The model makes use of feed forward artificial neural nets to capture nonlinearity, but is a parsimonious and interpretable representation of the network of causal relationships in complex systems. The generated model can easily and rapidly be experimented with and analyzed to determine the origins of behavior using the loops that matter method (Schoenberg et. al 2019). A demonstration of the utility and applicability of the method is given, showing that it produc

Author

Schoenberg, William

Published

2019

Language

EN