Imperial College London
Portrait Picture

ProfessorThomasParisini

Faculty of Engineering, Department of Electrical and Electronic Engineering

Chair in Industrial Control, Head of Group for CAP
 
 
 
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Contact

 

+44 (0)20 7594 6240t.parisini Website

 
 
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Location

 

1114Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

399 results found

Bolla R, Davoli F, Maryni P, Parisini Tet al., 1998, An adaptive neural network admission controller for dynamic bandwidth allocation, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, Vol: 28, Pages: 592-601, ISSN: 1083-4419

Journal article

Lewis FL, Parisini T, 1998, Neural network feedback control with guaranteed stability, INTERNATIONAL JOURNAL OF CONTROL, Vol: 70, Pages: 337-339, ISSN: 0020-7179

Journal article

Parisini T, Sanguineti M, Zoppoli R, 1998, Nonlinear stabilization by receding-horizon neural regulators, INTERNATIONAL JOURNAL OF CONTROL, Vol: 70, Pages: 341-362, ISSN: 0020-7179

Journal article

Fenu G, Parisini T, 1998, Model-free fault diagnosis for nonlinear systems: A combined Kernel-Regression and neural networks approach, American Control Conference, Publisher: IEEE, Pages: 2470-2471, ISSN: 0743-1619

Conference paper

Alessandri A, Parisini T, Zoppoli R, 1998, A convergent neural state estimator for nonlinear stochastic systems, 37th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 1076-1081, ISSN: 0191-2216

Conference paper

Alessandri A, Baglietto M, Parisini T, 1998, Robust model-based fault diagnosis using neural nonlinear estimators, 37th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 72-77, ISSN: 0743-1546

Conference paper

Alessandri A, Parisini T, 1998, Neural state estimators for direct model-based fault diagnosis, American Control Conference, Publisher: IEEE, Pages: 2874-2878, ISSN: 0743-1619

Conference paper

Alessandri A, Parisini T, 1998, Direct model-based fault diagnosis using neural filters, 3rd IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 1997 (SAFEPROCESS 97), Publisher: PERGAMON PRESS LTD, Pages: 343-348

Conference paper

Fenu G, Parisini T, 1998, Kernel regression and neural networks for model-free fault diagnosis, 3rd IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries 1998, Publisher: PERGAMON PRESS LTD, Pages: 149-154

Conference paper

Parisini T, Sacone S, 1998, Fault diagnosis and controller re-configuration: An hybrid approach, Joint Conference on the Science and Technology of Intelligent Systems ISIC/CIRA/ISAS, Publisher: IEEE, Pages: 163-168

Conference paper

Alessandri A, Parisini T, 1997, Nonlinear modeling of complex large-scale plants using neural networks and stochastic approximation, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, Vol: 27, Pages: 750-757, ISSN: 1083-4427

Journal article

Alessandri A, Parisini T, Zoppoli R, 1997, Neural approximators for nonlinear finite-memory state estimation, INTERNATIONAL JOURNAL OF CONTROL, Vol: 67, Pages: 275-301, ISSN: 0020-7179

Journal article

Zoppoli R, Parisini T, 1997, Optimization problems and neural networks, AEI AUTOMAZIONE ENERGIA INFORMAZIONE, Vol: 84, Pages: 62-71, ISSN: 0013-6131

Journal article

Alessandri A, Parisini T, Zoppoli R, 1997, Neural approximations for state-space parametric identification of nonlinear systems, Pages: 1409-1414

In the paper, the problem of designing a nonlinear parametric identifier for nonlinear discrete-time systems and noisy measurement channels is addressed. By generalizing the classical least-squares method we compute the estimation law off line by solving a functional optimization problem. Convergence results of the estimation errors are stated and the approximate solution of the above problem is addressed by means of a feedforward neural network. A min-max technique is proposed to determine the weight coefficients of the "neural" identifier so as to estimate the system parameters to any given degree of accuracy, thus guaranteeing the boundedness of the estimation error.

Conference paper

Parisini T, 1997, Physically accurate nonlinear models for fault detection and diagnosis: The case of a power plant, 13th International-Federation-of-Automatic-Control World Congress, Publisher: ELSEVIER SCI LTD, Pages: 97-109, ISSN: 0959-1524

Conference paper

Alessandri A, Parisini T, 1997, Model-based fault diagnosis using nonlinear estimators: A neural approach, 1997 American Control Conference, Publisher: I E E E, Pages: 903-907, ISSN: 0743-1619

Conference paper

Parisini T, Polycarpou M, Sanguineti M, Vemuri Aet al., 1997, Robust parametric and non-parametric fault diagnosis in nonlinear input-output systems, 36th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 4481-4482, ISSN: 0191-2216

Conference paper

Parisini T, Alessandri A, Maggiore M, Zoppoli Ret al., 1997, On convergence of neural approximate nonlinear state estimators, 1997 American Control Conference, Publisher: I E E E, Pages: 1819-1822, ISSN: 0743-1619

Conference paper

Ellis R, Simpson R, Culverhouse PF, Parisini Tet al., 1997, Committees, collectives and individuals: Expert visual classification by neural network, NEURAL COMPUTING & APPLICATIONS, Vol: 5, Pages: 99-105, ISSN: 0941-0643

Journal article

Parisini T, Sacone S, 1997, A stable two-level hybrid controller for nonlinear discrete-time systems, 36th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 1234-1236, ISSN: 0191-2216

Conference paper

Baglietto M, Parisini T, Zoppoli R, 1997, Nonlinear approximations for the solution of team optimal control problems, 36th IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 4592-4594, ISSN: 0191-2216

Conference paper

Zoppoli R, Parisini T, Sanguineti M, 1996, Neural approximators for functional optimization, Pages: 2355-3592, ISSN: 0191-2216

Functional optimization problems can be solved analytically only if special assumptions are verified. Otherwise, approximations are needed. This paper proposes approximation method for the general case. As test-beds for the solving technique, a stochastic optical control problem and an estimation problem, whose solution are traditionally regarded as difficult tasks, are addressed.

Conference paper

Parisini T, Zoppoli R, 1996, Infinite-horizon optimal control of nonlinear stochastic systems: a neural approach, Pages: 2355-3592, ISSN: 0191-2216

A feedback control law is proposed that drives the controlled vector vt of a dynamic system (in general, nonlinear) to track a reference vt* over an infinite time horizon, while minimizing a given cost function (in general, nonquadratic). The behavior of vt* over time is completely unpredictable. Random noises (in general, non-Gaussian) act on both the dynamic system and the state observation channel, which may be non-linear, too. As is well known, so general a non-LQG optimal control problem is very difficult to solve. The proposed solution is based on three main approximating assumptions: 1) the optimal control problem is stated in a receding-horizon framework where vt* is assumed to remain constant within a shifting-time window, 2) the control law is assigned a given structure (the one of a multilayer feedforward neural network) in which a finite number of parameters have to be determined in order to minimize the cost function (this makes it possible to approximate the original functional optimization problem by a nonlinear programming one), and 3) the control law is given a 'limited memory', which prevents the amount of data to be stored from increasing over time. The errors resulting from the second and third assumptions are discussed. Simulation results show that the proposed method may constitute an effective tool for solving, to a sufficient degree of accuracy, a wide class of control problems traditionally regarded as difficult ones.

Conference paper

Culverhouse PF, Simpson RG, Ellis R, Lindley JA, Williams R, Parisini T, Reguera B, Bravo I, Zoppoli R, Earnshaw G, McCall H, Smith Get al., 1996, Automatic classification of field-collected dinoflagellates by artificial neural network, MARINE ECOLOGY PROGRESS SERIES, Vol: 139, Pages: 281-287, ISSN: 0171-8630

Journal article

Parisini T, Zoppoli R, 1996, Neural approximations for multistage optimal control of nonlinear stochastic systems, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 41, Pages: 889-895, ISSN: 0018-9286

Journal article

Casalino G, Ferrara A, Minciardi R, Parisini Tet al., 1996, Implicit model techniques and their application to LQ adaptive control, Control and Dynamic Systems, Pages: 347-383

Book chapter

Contin A, Fenu GF, Parisini T, 1996, Diagnosis of HV stator bars insulation in the presence of multi partial-discharge phenomena, Conference on Electrical Insulation and Dielectric Phenomena, Publisher: I E E E, Pages: 488-491

Conference paper

Alessandri A, Maggiore M, Parisini T, Zoppoli Ret al., 1996, Neural approximators for nonlinear sliding-window state observers, 35th IEEE Conference on Decision and Control, Publisher: I E E E, Pages: 1461-1463, ISSN: 0191-2216

Conference paper

Parisini T, Zoppoli R, 1996, Infinite-horizon optimal control of nonlinear stochastic systems: A neural approach, 35th IEEE Conference on Decision and Control, Publisher: I E E E, Pages: 3294-3299, ISSN: 0191-2216

Conference paper

Zoppoli R, Parisini T, Sanguineti M, 1996, Neural approximators for functional optimization, 35th IEEE Conference on Decision and Control, Publisher: I E E E, Pages: 3290-3293, ISSN: 0191-2216

Conference paper

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