DocumentCode :
850254
Title :
Lagrange programming neural networks
Author :
Zhang, Shengwei ; Constantinides, A.G.
Author_Institution :
Expervision, San Jose, CA, USA
Volume :
39
Issue :
7
fYear :
1992
fDate :
7/1/1992 12:00:00 AM
Firstpage :
441
Lastpage :
452
Abstract :
A class of neural networks appropriate for general nonlinear programming, i.e., problems including both equality and inequality constraints, is analyzed in detail. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. No explicit restriction is imposed on the form of the cost function apart from some general regularity and convexity conditions. The stability of the neural networks is analyzed in detail. The transient behavior of the network is simulated and the validity of the approach is verified for a practical problem, maximum entropy image restoration
Keywords :
image reconstruction; neural nets; nonlinear programming; stability; Kuhn-Tucker condition; Lagrange multiplier theory; Lagrange programming; convexity; cost function; equality constraints; equilibrium point; inequality constraints; maximum entropy image restoration; neural networks; nonlinear programming; optimization; regularity; stability; transient behavior; Analog computers; Circuits; Computational modeling; Computer networks; Constraint optimization; Cost function; Lagrangian functions; Neural networks; Optimization methods; Stability analysis;
fLanguage :
English
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7130
Type :
jour
DOI :
10.1109/82.160169
Filename :
160169
Link To Document :
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