| Revue: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000383502 |
| ISSN: | 1405-5546 |
| Autores: | Pérez, Joel1 Pérez, José P1 |
| Instituciones: | 1Universidad Autónoma de Nuevo León, Facultad de Ciencias Físico Matemáticas, Monterrey, Nuevo León. México |
| Año: | 2015 |
| Periodo: | Abr-Jun |
| Volumen: | 19 |
| Número: | 2 |
| Paginación: | 399-405 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | This paper presents an application of Time-Delay adaptive neural networks based on a dynamic neural network for trajectory tracking of unknown nonlinear plants. Our approach is based on two main methodologies: the first one employs Time-Delay neural networks and Lyapunov-Krasovskii functions and the second one is Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID approach. The new control scheme is applied via simulations to Chaos Synchronization. Experimental results have shown the usefulness of the proposed approach for Chaos Production. To verify the analytical results, an example of a dynamical network is simulated and a theorem is proposed to ensure the tracking of the nonlinear system |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Redes, Redes neuronales, Sincronización del caos, Seguimiento de trayectoria, Tiempo de retardo |
| Keyword: | Computer science, Networks, Neural networks, Chaos synchronization, Trajectory tracking, Time delay |
| Texte intégral: | Texto completo (Ver HTML) |
