When is the stability of a nonlinear input-output system robust?

1997 ◽  
Vol 16 (4) ◽  
pp. 487-505
Author(s):  
Vaclav Dolezal
Keyword(s):  
Input Output ◽  
Nonlinear Input ◽  
Output System ◽  
The Stability

On a nonlinear input-output system

1987 ◽  
Vol 13 (3) ◽  
pp. 277-281 ◽  
Author(s):  
Koji Okuguchi ◽  
Ferenc Szidarovszky
Keyword(s):  
Input Output ◽  
Nonlinear Input ◽  
Output System

The Control of Leontief Model on Industry Manufacturing Process

2011 ◽  
Vol 187 ◽  
pp. 287-290
Author(s):  
Yong Liang Cui
Keyword(s):  
Linear System ◽  
Discrete Time ◽  
Stability Condition ◽  
Manufacturing Process ◽  
Input Output ◽  
Sufficient Stability Condition ◽  
Sufficient Stability ◽  
Output System ◽  
Leontief Model ◽  
The Stability

The classic Leontief model on industry manufacturing process is investigated. A kind of discrete-time singular dynamic input-output model of industry manufacturing process based on the classic Leontief Model is provided and the stability of this kind of model is researched. By the new mathematic method, the singular dynamic input-output system will not be converted into the general linear system. Finally, a sufficient stability condition under which the discrete-time singular Extended Leontief Model is admissible is proved.

SUPERVISED TRAINING OF DYNAMICAL NEURAL NETWORKS FOR ASSOCIATIVE MEMORY DESIGN AND IDENTIFICATION OF NONLINEAR MAPS

1994 ◽  
Vol 05 (03) ◽  
pp. 165-180 ◽  
Author(s):  
SUBRAMANIA I. SUDHARSANAN ◽  
MALUR K. SUNDARESHAN
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Equilibrium Points ◽  
Training Procedure ◽  
Input Output ◽  
Nonlinear Input ◽  
Dynamical Neural Networks ◽  
Hidden Layer ◽  
The Stability

Complexity of implementation has been a major difficulty in the development of gradient descent learning algorithms for dynamical neural networks with feedback and recurrent connections. Some insights from the stability properties of the equilibrium points of the network, which suggest an appropriate tailoring of the sigmoidal nonlinear functions, can however be utilized in obtaining simplified learning rules, as demonstrated in this paper. An analytical proof of convergence of the learning scheme under specific conditions is given and some upper bounds on the adaptation parameters for an efficient implementation of the training procedure are developed. The performance features of the learning algorithm are illustrated by applying it to two problems of importance, viz., design of associative memories and nonlinear input-output mapping. For the first application, a systematic procedure is given for training a network to store multiple memory vectors as its stable equilibrium points, whereas for the second application, specific training rules are developed for a three-layer network architecture comprising a dynamical hidden layer for the identification of nonlinear input-output maps. A comparison with the performance of a standard backpropagation network provides an illustration of the capabilities of the present network architecture and the learning algorithm.

scholarly journals An operational, nonlinear input–output system

2014 ◽  
Vol 41 ◽  
pp. 99-108 ◽  
Author(s):  
Ana-Isabel Guerra ◽  
Ferran Sancho
Keyword(s):  
Input Output ◽  
Nonlinear Input ◽  
Output System

A modular analog digital input output system (ADIOS) for on-line computers

1968 ◽  
Author(s):  
R. W. Kerr ◽  
H. P. Lie ◽  
G. L. Miller ◽  
D. A. H. Robinson
Keyword(s):  
Input Output ◽  
Digital Input ◽  
On Line ◽  
Output System
Sign in / Sign up

Export Citation Format

Share Document