Simultaneous perturbation stochastic approximation (SPSA) belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri...
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Simultaneous perturbation stochastic approximation (SPSA) belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation (FASPA) algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks (NNs). Accordingly, a multilayer perceptron (MLP) network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
作者:
Arian AziziMona GhassemiZero Emission
Realization of Optimized Energy Systems(ZEROES)LaboratoryDepartment of Electrical and Computer EngineeringThe University of Texas at DallasRichardsonTexasUSA
The next generation of aircraft,including more electric aircraft and all-electric aircraft(AEA),requires electric power systems with high power density and low system mass *** the voltage of the system to the range of...
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The next generation of aircraft,including more electric aircraft and all-electric aircraft(AEA),requires electric power systems with high power density and low system mass *** the voltage of the system to the range of a few kV,medium voltage(MV),is a reasonable approach to achieving high-power-density and low-system-mass EPSs for aircraft *** voltages,however,pose many challenges for aviation MV power cables such as arcs and arc tracking,partial discharges(PDs),and thermal *** this regard,thermal management is more challenging since heat transfer by convection is greatly reduced at wide-body aircraft's cruising altitudes due to the reduced air *** this paper,a finite element method(FEM)model is devel-oped in COMSOL Multiphysics for an aircraft bipolar MVDC(5 kV)power *** the model,the maximum permissible cable current at a low pressure of 18.8 kPa(at an altitude of 12.2 km from sea level,the usual cruising altitude for wide-body aircraft)is ***,an analytical model is developed based on analytical and proven empirical correlations governing conductive,radiative,and convective heat transfers at the steady state to estimate the ampacity of the bipolar cable system at reduced *** was shown that the proposed analytical model can be used for atmospheric pressure and systems with a larger number of poles,expanding its range of *** results of the FEM and analytical models correlate at wide ranges of parameters such as ambient temperature,duct size,distance between the positive and negative pole cables,and the overall diameter of the *** influence of horizontal and vertical arrangement of poles is included in the analytical *** results of this study can be used to design bipolar MVDC power cable systems for the envisaged wide-body AEA.
We consider a class of supervisory control problems that require infinite state supervisors Petri nets with inhibitor arcs (PN's) to model the supervisors. We compare this PN-based approach to supervisory control ...
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We consider a class of supervisory control problems that require infinite state supervisors Petri nets with inhibitor arcs (PN's) to model the supervisors. We compare this PN-based approach to supervisory control to automata-based approaches. The primary advantage of a PN-based supervisory controller is that a PN-based controller provides a finite representation of an infinite state supervisor. For verification, implementation, and testing reasons, a finite PN-based representation of an infinite state supervisor is preferred over an automata-based supervisor. We show that this modeling advantage is accompanied by a decision disadvantage, in that in general the controllability of a language that can be generated by the closed-loop system is undecidable.
The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusi...
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The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.
With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stoch...
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With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stochastic variables obeying specific distributions by neglecting the temporal correlations. Conventional approaches to hedge the negative effects caused by such uncertainties are thus hard to pursue a trade-off between computation efficiency and optimality. As an alternative, the theory of stochastic process can naturally model temporal correlation in closed forms. Attracted by this feature, our research group has been conducting thorough researches in the past decade to introduce stochastic processes within renewable power systems. This paper summarizes our works from the perspective of both the frequency domain and the time domain, provides the tools for the analysis and control of power systems under a unified framework of stochastic processes, and discusses the underlying reasons that stochastic process-based approaches can perform better than conventional approaches on both computational efficiency and optimality. These work may shed a new light on the research of analysis, control and operation of renewable power ***, this paper outlooks the theoretic developments of stochastic processes in future’s renewable power systems.
The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without a...
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We analyze the scheduling aspects of database queries submitted to an abstract model of a very large distributed system. The essential elements of this model are: (a) a finite number of identical processing nodes with...
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Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks ...
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作者:
CLARK, MBOVIK, ACLaboratory for Vision Systems
Computer and Vision Research Center Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712-1084 U.S.A
In this paper we apply a previously developed model of texture segmentation using multiple spatially and spectrally localized filters, known as Gabor filters, to the analysis of textures composed of elementary image f...
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In this paper we apply a previously developed model of texture segmentation using multiple spatially and spectrally localized filters, known as Gabor filters, to the analysis of textures composed of elementary image features known as textons. It is found that for regularly- or irregularly-spaced texton patterns, the segmentation approach works well, in the sense that it is in accordance with visual segmentation. Differences in texton spacing, size, orientation, and phase are all found to lead to successful segmentations.
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