HAMILTONIAN NEURAL NETWORK-BASED ORTHOGONAL FILTERS - A Basis for Artificial Intelligence

Wieslaw Citko, Wieslaw Sienko

Abstract

The purpose of the paper is to present how very large scale networks for learning can be designed by using Hamiltonian Neural Network-based orthogonal filters and in particular by using octonionic modules. We claim here that octonionic modules are basic building blocks to implement AI compatible processors.

References

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Paper Citation


in Harvard Style

Citko W. and Sienko W. (2011). HAMILTONIAN NEURAL NETWORK-BASED ORTHOGONAL FILTERS - A Basis for Artificial Intelligence . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 124-127. DOI: 10.5220/0003671501240127


in Bibtex Style

@conference{ncta11,
author={Wieslaw Citko and Wieslaw Sienko},
title={HAMILTONIAN NEURAL NETWORK-BASED ORTHOGONAL FILTERS - A Basis for Artificial Intelligence},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={124-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003671501240127},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - HAMILTONIAN NEURAL NETWORK-BASED ORTHOGONAL FILTERS - A Basis for Artificial Intelligence
SN - 978-989-8425-84-3
AU - Citko W.
AU - Sienko W.
PY - 2011
SP - 124
EP - 127
DO - 10.5220/0003671501240127