Robustness analysis of a class sparsely interconnected neural networks with applications to design problem

2005 ◽  
pp. 154-176
Keyword(s):  
Neural Networks ◽  
Design Problem ◽  
Robustness Analysis

Perturbations, Accuracy and Robustness in Neural Networks

2005 ◽  
pp. 2282-2287
Author(s):  
Cesare Alippi ◽  
Giovanni Vanini
Keyword(s):  
Neural Networks ◽  
Robustness Analysis ◽  
Theoretical Aspect ◽  
Neural Model ◽  
Knowledge Space

A robustness analysis for neural networks, namely the evaluation of the effects induced by perturbations affecting the network weights, is a relevant theoretical aspect since weights characterise the “knowledge space” of the neural model and, hence, its inner nature.

CHAOS, BIFURCATION AND ROBUSTNESS OF A CLASS OF HOPFIELD NEURAL NETWORKS

2011 ◽  
Vol 21 (03) ◽  
pp. 885-895 ◽  
Author(s):  
WEN-ZHI HUANG ◽  
YAN HUANG
Keyword(s):  
Neural Networks ◽  
Dynamical Systems ◽  
Chaotic Behavior ◽  
Robustness Analysis ◽  
Original System ◽  
Hopfield Neural Networks ◽  
Phase Portraits ◽  
Chaotic Attractors ◽  
Computer Assisted ◽  
New Class

Chaos, bifurcation and robustness of a new class of Hopfield neural networks are investigated. Numerical simulations show that the simple Hopfield neural networks can display chaotic attractors and limit cycles for different parameters. The Lyapunov exponents are calculated, the bifurcation plot and several important phase portraits are presented as well. By virtue of horseshoes theory in dynamical systems, rigorous computer-assisted verifications for chaotic behavior of the system with certain parameters are given, and here also presents a discussion on the robustness of the original system. Besides this, quantitative descriptions of the complexity of these systems are also given, and a robustness analysis of the system is presented too.

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