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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2007.
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| Edición: | 1st ed. 2007. |
| Colección: | Studies in Computational Intelligence,
53 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.