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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Veröffentlicht in:	IEEE transactions on signal processing Jg. 53; H. 2; S. 672 - 685
Hauptverfasser:	Youshen Xia, Leung, H., Nan Xie, Bosse, E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.02.2005 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Bosse, E. Youshen Xia Leung, H. Nan Xie
Author_xml	– sequence: 1 surname: Youshen Xia fullname: Youshen Xia organization: Dept. of Electr. & Comput. Eng., Univ. of Calgary, Alta., Canada – sequence: 2 givenname: H. surname: Leung fullname: Leung, H. organization: Dept. of Electr. & Comput. Eng., Univ. of Calgary, Alta., Canada – sequence: 3 surname: Nan Xie fullname: Nan Xie organization: Dept. of Electr. & Comput. Eng., Univ. of Calgary, Alta., Canada – sequence: 4 givenname: E. surname: Bosse fullname: Bosse, E.
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Keywords	Performance evaluation recurrent neural network Simulation Estimation global convergence regression Information extraction Regression analysis Neural network Data mining Quadratic programming Optimization
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References	ref12 ref15 ref14 ref10 kinderlehrer (ref17) 1980 cottle (ref20) 1992 zaˇk (ref11) 1995; 6 ref16 ref19 ref18 ref8 cichocki (ref7) 1993 bradley (ref6) 1977 ref9 ref4 ref5 haykin (ref3) 1991 cherkassky (ref1) 1998 xia (ref13) 2000 gruber (ref21) 1990 bradley (ref2) 1998
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SubjectTerms	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
Title	A new regression estimator with neural network realization
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