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...
| Main Authors: | , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2007, 2007
|
| Edition: | 1st ed. 2007 |
| Series: | Studies in Computational Intelligence
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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