Application bifurcation theory (6 résultats)

Taschenbuch. Etat : Neu. Bifurcation and Nonlinear Dynamics in Neural Network | Theory and Application | Moayed Daneshyari | Taschenbuch | 96 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783838365886 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

paperback. Etat : New. Language:Chinese.Paperback. Publisher: University Press. book nonlinear system described the basic concepts of bifurcation and chaos. the main method and the machine.

Couverture rigide. Etat : Très bon. Précision sur l'exemplaire mis en vente : Exemplaire de bibliothèque du CEA, avec cachets, intérieur très propre. | Description : Edited by Okan Gurel and Otto E. Rössler.

paperback. Etat : New. Paperback. Pub Date: 2012 Jun Pages: 219 in Publisher: Science Press Delay Differential bifurcation theory and application brief introduction to the basic theory and focuses on the main branch Research Delay Differential. The basic theory introduced including the initial value problem of existence and uniqueness of solutions. Existence of global solutions. linear autonomous system spectral decomposition theory and linear stability theory. semi-dynamic system stability theory; around the branc.…

paperback. Etat : New. Language:Chinese.Bifurcation pulse power system chaos theory and its application.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nonlinear dynamics play an important role on biological livings, including biological neural systems. Researchers have shown that nonlinear dynamics and chaos exist in several levels of neural systems from micro to macro levels. This is a strong motivation for the addition of nonlinear dynamics and chaos into neural networks. In this book, a new type of neural network is introduced based on the nonlinear dynamics and chaos. Bifurcation behavior of the neural network shows that the state of the neurons will move chaotically among the stored states of the network but finally, by decreasing the degree of chaos, settles down into a proper state. Therefore this new type of neural network with chaotic dynamics is able to search the state space and seeks the points far away from equilibrium points. We also find that increasing the memory capacity of the neural network is one of the consequences of adding the nonlinearity into it. This network can also be used for pattern recognition, classification, and optimization.…