Polynomial discrete time Cellular Neural Networks to solve the XOR problem

Eduardo Gomez-Ramirez; Giovanni Egidio Pazienza; Xavier Vilasis-Cardona

doi:10.1109/CNNA.2006.341598

Polynomial discrete time Cellular Neural Networks to solve the XOR problem

Eduardo Gomez-Ramirez^*
, Giovanni Egidio Pazienza
, Xavier Vilasis-Cardona

^*Corresponding author for this work

La Salle International School of Commerce and Digital Economy

Research output: Book chapter › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

Some papers discuss different option to improve the capabilities of Cellular Neural Networks (CNN), The principal point is that a single layer CNN can not solve problems with linearly nonsepurable data. In this paper a new model called Polynomial Discrete Time Cellular Neural networks is presented. This model has a very simple nonlinear term that can improve the performance of the network. The results show how it is possible to solve the XOR problem. The templates of the entire network are computed using genetic algorithm,

Original language	English
Title of host publication	Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006
DOIs	https://doi.org/10.1109/CNNA.2006.341598
Publication status	Published - 2006
Event	2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006 - Istanbul, Turkey Duration: 28 Aug 2006 → 30 Aug 2006

Publication series

Name	Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Conference

Conference	2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006
Country/Territory	Turkey
City	Istanbul
Period	28/08/06 → 30/08/06

Keywords

Genetic algorithm
Polynomial discrete time Cellular Neural Networks
XOR problem

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10.1109/CNNA.2006.341598

Cite this

Gomez-Ramirez, E., Pazienza, G. E., & Vilasis-Cardona, X. (2006). Polynomial discrete time Cellular Neural Networks to solve the XOR problem. In Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006 Article 4145838 (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications). https://doi.org/10.1109/CNNA.2006.341598

Gomez-Ramirez, Eduardo ; Pazienza, Giovanni Egidio ; Vilasis-Cardona, Xavier. / Polynomial discrete time Cellular Neural Networks to solve the XOR problem. Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006. 2006. (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications).

@inproceedings{040006276efd4624ada1f51a71190799,

title = "Polynomial discrete time Cellular Neural Networks to solve the XOR problem",

abstract = "Some papers discuss different option to improve the capabilities of Cellular Neural Networks (CNN), The principal point is that a single layer CNN can not solve problems with linearly nonsepurable data. In this paper a new model called Polynomial Discrete Time Cellular Neural networks is presented. This model has a very simple nonlinear term that can improve the performance of the network. The results show how it is possible to solve the XOR problem. The templates of the entire network are computed using genetic algorithm,",

keywords = "Genetic algorithm, Polynomial discrete time Cellular Neural Networks, XOR problem",

author = "Eduardo Gomez-Ramirez and Pazienza, \{Giovanni Egidio\} and Xavier Vilasis-Cardona",

year = "2006",

doi = "10.1109/CNNA.2006.341598",

language = "English",

isbn = "1424406404",

series = "Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications",

booktitle = "Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006",

note = "2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006 ; Conference date: 28-08-2006 Through 30-08-2006",

}

Gomez-Ramirez, E, Pazienza, GE & Vilasis-Cardona, X 2006, Polynomial discrete time Cellular Neural Networks to solve the XOR problem. in Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006., 4145838, Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications, 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006, Istanbul, Turkey, 28/08/06. https://doi.org/10.1109/CNNA.2006.341598

Polynomial discrete time Cellular Neural Networks to solve the XOR problem. / Gomez-Ramirez, Eduardo; Pazienza, Giovanni Egidio; Vilasis-Cardona, Xavier.
Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006. 2006. 4145838 (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications).

Research output: Book chapter › Conference contribution › peer-review

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T1 - Polynomial discrete time Cellular Neural Networks to solve the XOR problem

AU - Gomez-Ramirez, Eduardo

AU - Pazienza, Giovanni Egidio

AU - Vilasis-Cardona, Xavier

PY - 2006

Y1 - 2006

N2 - Some papers discuss different option to improve the capabilities of Cellular Neural Networks (CNN), The principal point is that a single layer CNN can not solve problems with linearly nonsepurable data. In this paper a new model called Polynomial Discrete Time Cellular Neural networks is presented. This model has a very simple nonlinear term that can improve the performance of the network. The results show how it is possible to solve the XOR problem. The templates of the entire network are computed using genetic algorithm,

AB - Some papers discuss different option to improve the capabilities of Cellular Neural Networks (CNN), The principal point is that a single layer CNN can not solve problems with linearly nonsepurable data. In this paper a new model called Polynomial Discrete Time Cellular Neural networks is presented. This model has a very simple nonlinear term that can improve the performance of the network. The results show how it is possible to solve the XOR problem. The templates of the entire network are computed using genetic algorithm,

KW - Genetic algorithm

KW - Polynomial discrete time Cellular Neural Networks

KW - XOR problem

UR - https://www.scopus.com/pages/publications/47549106867

U2 - 10.1109/CNNA.2006.341598

DO - 10.1109/CNNA.2006.341598

M3 - Conference contribution

AN - SCOPUS:47549106867

SN - 1424406404

SN - 9781424406401

T3 - Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

BT - Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006

T2 - 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006

Y2 - 28 August 2006 through 30 August 2006

ER -

Gomez-Ramirez E, Pazienza GE, Vilasis-Cardona X. Polynomial discrete time Cellular Neural Networks to solve the XOR problem. In Proceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006. 2006. 4145838. (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications). doi: 10.1109/CNNA.2006.341598

Polynomial discrete time Cellular Neural Networks to solve the XOR problem

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