Search results for: M. Farrokhrooz
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In this paper, we present an orthonormal version of the new information criterion (NIC) algorithm for fast estimation and tracking of signal subspace using a two-layer linear neural network (NN). The fast orthonormal NIC referred as to FONIC algorithm guarantees the orthonormality of the weight matrix at each iteration. The proposed FONIC algorithm has a linear complexity which makes it efficient...
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In this paper, we present some new investigations on a robust adaptive beamforming via LMS-type procedure which has been proposed recently. This beamformer self-corrects and tracks desired source location errors or variations regardless of the eigenstructure of the input correlation matrix and array shape. This algorithm and MUSIC show an identical asymptotic variance in localization for immobile...
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The use of Probabilistic Neural Network (PNN) is very common in supervised pattern recognition applications. PNN is based on Bayes decision rule and it uses Gaussian Parzen windows for estimating the probability density functions (pdf) required in Bayes rule. The conventional PNN needs a single spread value for pdf estimation which is proportional to Gaussian window width. In this paper we will suggest...
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This paper discusses the constrained two stage least squares (CLS2) estimator of the parameters of ARCH models under known order. This estimator is a modified version of the two stage least squares (TSLS) estimation. The estimator is easy to obtain and fast since it involves only quadratic optimization. At the same time, the estimator has the same asymptotic efficiency as that of the TSLS estimator...
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The use of Probabilistic Neural Network (PNN) is very common in supervised pattern recognition applications. PNN is based on Bayes decision rule and it uses Gaussian Parzen windows for estimating the probability density functions (pdf) required in Bayes rule. The conventional PNN needs a single spread value for pdf estimation which is proportional to Gaussian window width. In this paper we will suggest...
chapter
The use of probabilistic neural network (PNN) is very common in supervised pattern recognition applications. PNN is based on Bayes decision rule and it uses Gaussian Parzen windows for estimating the probability density functions (pdf) required in Bayes rule. The conventional PNN needs a single spread value for pdf estimation which is proportional to Gaussian window width. In this paper we suggest...
