2010
Hernández-Romero, N., Seck-Tuoh-Mora, J. C., González-Hernández, M., Medina-Marin, J. (2010). Modeling a Nonlinear Liquid Level System by Cellular Neural Networks. International Journal of Modern Physics C, 21(4), 489-501.
Abstract
This paper presents the analogue simulation of a nonlinear liquid level system composed by two tanks; the system is controlled using the methodology of exact linearization via state feedback by cellular neural networks (CNNs). The relevance of this manuscript is to show how a block diagram representing the analogue modeling and control of a nonlinear dynamical system, can be implemented and regulated by CNNs, whose cells may contain numerical values or arithmetic and control operations. In this way the dynamical system is modeled by a set of local-interacting elements without need of a central supervisor.
Modeling a Nonlinear Liquid Level System by Cellular Neural Networks
Elementary cellular automaton Rule 110 explained as a block substitution system
Pair Diagram and Cyclic Properties Characterizing the Inverse of Reversible Automata
Complex Dynamics Emerging in Rule 30 with Majority Memory
How to Make Dull Cellular Automata Complex by Adding Memory: Rule 126 Case Study
Unconventional invertible behaviors in reversible one-dimensional cellular automata.
Reproducing the Cyclic Tag System Developed by Matthew Cook with Rule 110 Using the Phases f(i-)1.