Title :
Polynomial Discrete Time Cellular Neural Networks to solve the XOR problem
Author :
Gomez-Ramirez, Ediuardo ; Pazienza, Giovanni Egidio ; Vilasis-Cardona, Xavier
Author_Institution :
La Salle Univ., Mexico City
Abstract :
Some papers discuss different options to improve the capabilities of cellular neural networks (CNN). The principal point is that a single layer CNN can not solve problems with linearly nonseparable 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 :
Boolean algebra; cellular neural nets; genetic algorithms; polynomials; XOR problem; genetic algorithm; polynomial discrete time cellular neural networks; Artificial neural networks; Cellular networks; Cellular neural networks; Computer networks; Electronic mail; Genetic algorithms; Helium; Nonhomogeneous media; Nonlinear equations; Polynomials; Polynomial Discrete Time Cellular Neural Networks; XOR problem; genetic algorithm;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2006. CNNA '06. 10th International Workshop on
Conference_Location :
Istanbul
Print_ISBN :
1-4244-0640-4
Electronic_ISBN :
1-4244-0640-4
DOI :
10.1109/CNNA.2006.341598
