Neural Programming: Towards adaptive control in Cyber-Physical Systems

K. Selyunin, D. Ratasich, E. Bartocci, M. A. Islam, S. A. Smolka, R. Grosu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We introduce Neural Programming (NP), a novel paradigm for writing adaptive controllers for Cyber-Physical Systems (CPSs). In NP, if and while statements, whose discontinuity is responsible for frailness in CPS design and implementation, are replaced with their smooth (probabilistic) neural nif and nwhile counterparts. This allows one to write robust and adaptive CPS controllers as dynamic neural networks (DNN). Moreover, with NP, one can relate the thresholds occurring in soft decisions with a Gaussian Bayesian network (GBN). We provide a technique for learning these GBNs using available domain knowledge. We demonstrate the utility of NP on three case studies: an adaptive controller for the parallel parking of a Pioneer rover; the neural circuit for tap withdrawal in C. elegans; and a neural-circuit encoding of parallel parking which corresponds to a proportional controller. To the best of our knowledge, NP is the first programming paradigm linking neural networks (artificial or biological) to programs in a way that explicitly highlights a program's neural-network structure.

Original languageEnglish
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6978-6985
Number of pages8
ISBN (Electronic)9781479978861
DOIs
StatePublished - Feb 8 2015
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)0743-1546

Conference

Conference54th IEEE Conference on Decision and Control, CDC 2015
CountryJapan
CityOsaka
Period12/15/1512/18/15

Keywords

  • Bayes methods
  • Neurons
  • Probabilistic logic
  • Probability density function
  • Programming
  • Robustness
  • Trajectory

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  • Cite this

    Selyunin, K., Ratasich, D., Bartocci, E., Islam, M. A., Smolka, S. A., & Grosu, R. (2015). Neural Programming: Towards adaptive control in Cyber-Physical Systems. In 54rd IEEE Conference on Decision and Control,CDC 2015 (pp. 6978-6985). [7403319] (Proceedings of the IEEE Conference on Decision and Control; Vol. 54rd IEEE Conference on Decision and Control,CDC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2015.7403319