A new regression estimator with neural network realization

A new regression estimator viewed as the solution of a strictly convex quadratic programming problem is introduced in this paper. Two recurrent neural networks in continuous-time and discrete-time respectively are proposed to solve the quadratic programming problem in real time. The continuous-time...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 53; no. 2; pp. 672 - 685
Main Authors: Youshen Xia, Leung, H., Nan Xie, Bosse, E.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.02.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Artificial intelligence
Asymptotic properties
Asymptotic stability
Computer science; control theory; systems
Computer simulation
Connectionism. Neural networks
Constraint optimization
Convergence
Cost function
Data mining
Estimation
Estimators
Exact sciences and technology
Gaussian noise
global convergence
Neural networks
Optimization methods
Quadratic programming
recurrent neural network
Recurrent neural networks
Regression
Stability
Performance evaluation
recurrent neural network
Simulation
Estimation
global convergence
regression
Information extraction
Regression analysis
Neural network
Data mining
Quadratic programming
Optimization
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:A new regression estimator viewed as the solution of a strictly convex quadratic programming problem is introduced in this paper. Two recurrent neural networks in continuous-time and discrete-time respectively are proposed to solve the quadratic programming problem in real time. The continuous-time neural network is shown to have a global stability, including the global asymptotic and exponential stability. The discrete-time neural network is shown to have a global convergence with a fixed step length. This fixed step length can be independent of the regression problem size by scaling a design parameter. Since the sizes of the proposed neural networks depend only on the constraints of the optimization problems, the proposed new regression estimator can obtained by two novel neural networks with lower implementation costs than the conventional methods. Our simulation results confirm that the proposed neural networks are effective in solving various kind of regression problems.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2004.838929