The bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Given a sample of independent identically distributed real-valued random variables with ...
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The bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Given a sample of independent identically distributed real-valued random variables with a common unknown probability density function f, the considered problem here is to estimate the probability density function f from the sample set. In our work, we suppose that the density f is expressed as a finite linear combination of second order b-splines functions. The problem of estimating the density f leads to the estimation of the coefficients of b-splines. In order to solve this problem, we suppose that the prior distribution of the b-splines coefficients is a Dirichlet distribution. The estimation of these coefficients allowed us to introduce a new algorithm called bayesian expectation maximization. In fact, this algorithm, which is the combination of the bayesian approach and the expectation maximization algorithm, attempts to directly optimize the posterior bayesian distribution. This algorithm has been generalized to the case of mixing distributions. We have studied the asymptotic properties of the bayesian estimator. Then, the performance of our algorithm has been evaluated and compared by making a simulation study, followed by a real image segmentation. In both cases, our proposed bayesian algorithm is shown to give better results. (C) 2015 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier b.V. All rights reserved.
This article deals with the problem of trajectory generation and tracking for nonlinear systems. The principal problem considered concerns the generation of desired trajectories for differentially flat systems. In thi...
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ISBN:
(纸本)9781467363020;9781467363006
This article deals with the problem of trajectory generation and tracking for nonlinear systems. The principal problem considered concerns the generation of desired trajectories for differentially flat systems. In this work, two methods of trajectory generation are applied in case of induction machine control to solve the problem of trajectory tracking with good efficiencies. The first one is based on the polynomial functions and the second rests on the b-splines functions. The combined methods of PI controllers and trajectory planning give an improvement results in terms of trajectory tracking in the case of field oriented control strategy.
The paper proposes an improved b-spline networks in order to increase the ability of rejecting the disturbance when it is as a fuzzy-neural network controller in real applications, by pointing out the shortage existed...
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ISBN:
(纸本)7312012035
The paper proposes an improved b-spline networks in order to increase the ability of rejecting the disturbance when it is as a fuzzy-neural network controller in real applications, by pointing out the shortage existed in the conventional b-spline function as the fuzzy membership function, the paper proposes the relevant improved method in such way to both satisfies b-splines features, and also avoid system unstable factors. The actual experiments for a DC motor speed control system verify the effectiveness and practicability. The paper gives out the design procedure and the experiment results of the improved b-spline networks.
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