Generalized neural network for nonsmooth nonlinear programming problems

In 1988 Kennedy and Chua introduced the dynamical canonical nonlinear programming circuit (NPC) to solve in real time nonlinear programming problems where the objective function and the constraints are smooth (twice continuously differentiable) functions. In this paper, a generalized circuit is intr...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Vol. 51; no. 9; pp. 1741 - 1754
Main Authors: Forti, M., Nistri, P., Quincampoix, M.
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Analog circuits
Circuit simulation
Computational modeling
Diodes
Dynamic programming
Functional programming
Neural networks
Neurons
Nonlinear programming
Quadratic programming
Studies
ISSN:1549-8328, 1558-0806
Online Access:Get full text
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Summary:In 1988 Kennedy and Chua introduced the dynamical canonical nonlinear programming circuit (NPC) to solve in real time nonlinear programming problems where the objective function and the constraints are smooth (twice continuously differentiable) functions. In this paper, a generalized circuit is introduced (G-NPC), which is aimed at solving in real time a much wider class of nonsmooth nonlinear programming problems where the objective function and the constraints are assumed to satisfy only the weak condition of being regular functions. G-NPC, which derives from a natural extension of NPC, has a neural-like architecture and also features the presence of constraint neurons modeled by ideal diodes with infinite slope in the conducting region. By using the Clarke's generalized gradient of the involved functions, G-NPC is shown to obey a gradient system of differential inclusions, and its dynamical behavior and optimization capabilities, both for convex and nonconvex problems, are rigorously analyzed in the framework of nonsmooth analysis and the theory of differential inclusions. In the special important case of linear and quadratic programming problems, salient dynamical features of G-NPC, namely the presence of sliding modes , trajectory convergence in finite time, and the ability to compute the exact optimal solution of the problem being modeled, are uncovered and explained in the developed analytical framework.
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ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2004.834493