On the Intrinsic Relation of Linear Dynamical Systems and Higher Order Neural Units

Conference paper

DOI: 10.1007/978-3-319-33389-2_23

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)
Cite this paper as:
Benes P., Bukovsky I. (2016) On the Intrinsic Relation of Linear Dynamical Systems and Higher Order Neural Units. In: Silhavy R., Senkerik R., Oplatkova Z., Silhavy P., Prokopova Z. (eds) Automation Control Theory Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 466. Springer, Cham


This paper summarizes the fundamental construction of higher-order-neural-units (HONU) as a class of polynomial function based neural units, which are though non-linear discrete time models, are linear in their parameters. From this a relation will be developed, ultimately leading to a new definition for analysing the global stability of a HONU, not only as a model itself, but further as a means of justifying the global dynamic stability of the whole control loop under HONU feedback control. This paper is organised to develop the fundamentals behind this intrinsic relation of linear dynamic systems and HONUs accompanied by a theoretical example to illustrate the functionality and principles of the concept.


Adaptive control Higher-order neural units (HONUs) Gradient descent (GD) Levenberg-Marquardt (LM) Batch propagation through time (BPTT) 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Instrumentation and Control EngineeringCzech Technical University in PraguePragueCzech Republic

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