The fidelity of a signal formed by recalling samples of a sinusoid from a look-up table and converting these sample amplitudes to a waveform in a digital-to-analog converter (DAC) is affected by both the phase and amp...
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When modelling a time series from discrete-time data, a continuous-time parametrization is desirable in some situations. It can have good numerical properties and low computational burden, in particular for fast or no...
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When modelling a time series from discrete-time data, a continuous-time parametrization is desirable in some situations. It can have good numerical properties and low computational burden, in particular for fast or nonuniform sampling. In a direct estimation approach, the derivatives are approximated by appropriate differences, leading to a linear regression model. It is shown that standard approximations like Euler backward or Euler forward cannot be used. The precise conditions on the derivative approximation are derived and analysed. It is shown that if the highest order derivative is selected with care, a least-squares estimate will be accurate. The theoretical analysis is complemented by some numerical examples.
The paper introduces an idea of logical filtering viewed as a new tool for solving fuzzy relational equations. Considering the panoply of the existing methods, the proposed approach can be classified as a semi-analyti...
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The paper introduces an idea of logical filtering viewed as a new tool for solving fuzzy relational equations. Considering the panoply of the existing methods, the proposed approach can be classified as a semi-analytic method in the sense it departures from the individual analytical solutions to the individual equations in the system and combines them through an optimization process of logical filtering (masking). Several types of filtering are studied and provided with the detailed learning schemes.< >
Output tracking of implcitly defined reference trajectories is examined. A continuoustime nonlinear dynamical system is constructed that produces explicit estimates of time-varying implicit trajectories. We prove that...
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Output tracking of implcitly defined reference trajectories is examined. A continuoustime nonlinear dynamical system is constructed that produces explicit estimates of time-varying implicit trajectories. We prove that incorporation of this “dynamic inverter” into a tracking controller provides exponential output tracking of the implicitly defined trajectory for nonlinear controlsystems having vector relative degree and well-behaved internal dynamics.
In this paper, the authors describe their NSF sponsored research-curriculum program devoted to the topic of modeling and control of semiconductor manufacturing. The paper is focused for the most part on the curriculum...
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In this paper, the authors describe their NSF sponsored research-curriculum program devoted to the topic of modeling and control of semiconductor manufacturing. The paper is focused for the most part on the curriculum development under this program.
An algorithm for iterative learning control is proposed based on an optimization principle used by other authors to derive gradient type algorithms. The new algorithm is a descent algorithm and has potential benefits ...
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An algorithm for iterative learning control is proposed based on an optimization principle used by other authors to derive gradient type algorithms. The new algorithm is a descent algorithm and has potential benefits which include realization in terms of Riccati feedback and feed-forward components. This realization also has the advantage of implicitly ensuring automatic step size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm achieves a geometric rate of convergence for invertible plants which can be arbitrarily changed by design parameters.
This paper develops new 2D systems state space models for discrete linear repetitive processes. The overall aim is to use these models and well established 2D systems theory to address basic systems theoretic question...
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This paper develops new 2D systems state space models for discrete linear repetitive processes. The overall aim is to use these models and well established 2D systems theory to address basic systems theoretic questions for this subclass of repetitive processes. To this end, it is shown that the stability theories are equivalent and a state transition matrix is developed and used to present some basic results on reachability. Cet article developpe une nouvelle classe de modeles des processus lineaires, discrets et repetitifs. L'objectif est du'utiliser conjointement ces modeles et la theorie classique des systemes 2D afin der examiner, pour cette classe de systemes repetitifs, des notions de base la theorie des systemes. A cette fin, il est montreque les theories de la stabili te sont equivalentes. Une matrice de transition est definie, qui nous permet de presenter quelques resultats de base concemant latteignab ilite.
There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a...
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There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing techniques.< >
Kitano's approach to neural network design is extended in the sense that not just the neural network structure, but also the values of the weights are coded in the chromosome. Experimental results are presented de...
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Kitano's approach to neural network design is extended in the sense that not just the neural network structure, but also the values of the weights are coded in the chromosome. Experimental results are presented demonstrating the capability of the technique in the solution of a standard test problem.
This paper presents an efficient approach to short term power system resource scheduling based on the augmented Lagrangian relaxation method. The problem is divided into two stages, the commitment stage and the constr...
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This paper presents an efficient approach to short term power system resource scheduling based on the augmented Lagrangian relaxation method. The problem is divided into two stages, the commitment stage and the constrained economic dispatch stage. The proposed mathematical model incorporates optimal power flow (OPF) constraints in the unit commitment stage. By OPF constrains, the authors refer to the relevant active power constraints that are incorporated in the constrained economic dispatch stage (i.e. transmission capacity constraints, fuel and various regulated emission requirements). The inclusion of OPF constraints in the commitment stage will improve the feasibility of the constrained economic dispatch solution. Other unit commitment constraints such as spinning and operating reserve requirements, power balance as well as other relevant local constraints (i.e. unit ramping rates, upper and lower generation limits, minimum up and down times) are taken into account in the proposed model. As a larger number of constraints are dealt with, a more rigorous method is introduced for updating Lagrange multipliers to improve the solution convergence. A software package which addresses energy management systems requirements is developed and tested.
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