Dynamics and storage capacity of neural networks with sign-constrained weights

2008 ◽  
pp. 237-252 ◽  
Author(s):  
C. Campbell ◽  
K. Y. M. Wong
Keyword(s):  
Neural Networks ◽  
Storage Capacity ◽  
And Storage

Fault prognostics using dynamic wavelet neural networks

2001 ◽  
Vol 15 (4) ◽  
pp. 349-365 ◽  
Author(s):  
PENG WANG ◽  
GEORGE VACHTSEVANOS
Keyword(s):  
Neural Networks ◽  
Storage Capacity ◽  
Structure Learning ◽  
Condition Based Maintenance ◽  
Wavelet Neural Networks ◽  
And Performance ◽  
Fault Prognostics ◽  
And Storage ◽  
Useful Lifetime ◽  
Inner Race

Modern industry is concerned about extending the lifetime of its critical processes and maintaining them only when required. Significant aspects of these trends include the ability to diagnose impending failures, prognosticate the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to condition-based maintenance (CBM). This paper attempts to address this challenging problem with intelligence-oriented techniques, specifically dynamic wavelet neural networks (DWNNs). DWNNs incorporate temporal information and storage capacity into their functionality so that they can predict into the future, carrying out fault prognostic tasks. Such fundamental issues as the network structure, learning algorithms, stability analysis, uncertainty management, and performance assessment are studied in a theoretical framework. An example is presented in which a trained DWNN successfully prognoses a defective bearing with a crack in its inner race.

ASSOCIATIVE MEMORY USING SYNCHRONIZATION IN A CHAOTIC NEURAL NETWORK

2001 ◽  
Vol 12 (01) ◽  
pp. 19-29 ◽  
Author(s):  
Z. TAN ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Storage Capacity ◽  
Initial Conditions ◽  
Neural Systems ◽  
Chaotic Neural Network ◽  
Relative Time ◽  
Chaotic Neural Networks ◽  
And Storage

Synchronization is introduced into a chaotic neural network model to discuss its associative memory. The relative time of synchronization of trajectories is used as a measure of pattern recognition by chaotic neural networks. The retrievability of memory is shown to be connected to synapses, initial conditions and storage capacity. The technique is simple and easy to apply to neural systems.

Sign in / Sign up

Export Citation Format

Share Document