Optimum system modelling using recent gradient methods

Markettos, N. D.; Sinha, Ν.Κ.

doi:10.1080/00207727308919993

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…As is well known, considerable improvement in the rate of convergence is obtained in deterministic problems if more efficient gradient search techniques are used. As shown in a recent paper [13], the new Fletcher method is probably the most efficient. Hence, a stochastic approximation algorithm based on such a method will converge to the optimum much faster than the Kiefer-Wolfowitz algorithm.…”

Section: P(p) Y(t)mentioning

confidence: 94%

Adaptive Control Using Stochastic Approximation

Sinha¹,

Prasad²

1974

Journal of Cybernetics

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Adaptive control of systems with uncertainty can be implemented without complete identification of the plant if the cost function is measured continuously and the parameters of the controller are adjusted, using an efficient gradient method, to obtain optimum performance. As, in practice, only noisy measurements of the cost function are available, one has to use stochastic gradient methods. In this paper some efficient stochastic approximation algorithms are proposed for adaptive control.

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Section: P(p) Y(t)mentioning

confidence: 94%

Adaptive Control Using Stochastic Approximation

Sinha¹,

Prasad²

1974

Journal of Cybernetics

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“…The second proposed methods group of reduced order model is based on the optimization without regarding to important eigen-values of the original model [3] - [4].…”

Section: Introductionmentioning

confidence: 99%

Designing Controller by State Space Techniques Using Reduced Order Model Algorithm

Nguyen

Dao

Trung

2011

2011 Fourth International Conference on Intelligent Computation Technology and Automation

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In general telecommunication, control and signal processing, mathematical models of objects are often built from the physical process. In fact, some objects have complex physical models, therefore, accurate description of the object will results to large and high-order mathematical models. This causes difficulties for the computation and design. Hence, finding a lower-order model that still has the important characteristics of the high-order models with acceptable errors is essential. This paper presents the reduced order model problem and studies on balancing approach proposed by Moore. The comprehensive research includes: transient response h(t), frequency response and applications in control. Simulation results prove the proper algorism as well as capability of being applied into practice.

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Investigation and Implementation of Model Order Reduction Technique for Large Scale Dynamical Systems

Suman

Kumar

2022

Arch Computat Methods Eng

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Optimum system modelling using recent gradient methods

Cited by 13 publications

References 5 publications

Adaptive Control Using Stochastic Approximation

Adaptive Control Using Stochastic Approximation

Designing Controller by State Space Techniques Using Reduced Order Model Algorithm

Investigation and Implementation of Model Order Reduction Technique for Large Scale Dynamical Systems

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