Neural networks : computational models and applications
Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their ap...
Tallennettuna:
| Päätekijät: | , , |
|---|---|
| Aineistotyyppi: | Livre numérique |
| Kieli: | Anglais |
| Julkaistu: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Painos: | 1st ed. 2007. |
| Sarja: | Studies in Computational Intelligence
53 |
| Linkit: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Huomautus: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Neural Networks: Computational Models and Applications, Texte imprimé, 9783540692256 • Neural Networks: Computational Models and Applications, Texte imprimé, 9783540834465 • Neural Networks: Computational Models and Applications, Texte imprimé, 9783642088711 • Neural Networks: Computational Models and Applications, Texte imprimé, 9783540692256 |
Sisällysluettelo:
- Feedforward Neural Networks and Training Methods New Dynamical Optimal Learning for Linear Multilayer FNN Fundamentals of Dynamic Systems Various Computational Models and Applications Convergence Analysis of Discrete Time RNNs for Linear Variational Inequality Problem Parameter Settings of Hopfield Networks Applied to Traveling Salesman Problems Competitive Model for Combinatorial Optimization Problems Competitive Neural Networks for Image Segmentation Columnar Competitive Model for Solving Multi-Traveling Salesman Problem Improving Local Minima of Columnar Competitive Model for TSPs A New Algorithm for Finding the Shortest Paths Using PCNN Qualitative Analysis for Neural Networks with LT Transfer Functions Analysis of Cyclic Dynamics for Networks of Linear Threshold Neurons LT Network Dynamics and Analog Associative Memory Output Convergence Analysis for Delayed RNN with Time Varying Inputs Background Neural Networks with Uniform Firing Rate and Background Input