Mahmood Karimi

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The case where the data sample size is finite and the least-squares-forward (LSF) method is used for autoregressive (AR) parameter estimation is considered. New formulas describing the residual variance and the prediction error behaviors in AR parameter estimation are derived, and the relation between the residual variance and the prediction error is(More)
One of the approaches that can be used in autoregressive (AR) model order selection is to choose the order that minimizes the prediction error. The final prediction error (FPE) criterion uses this approach in order selection. Unfortunately, this criterion has poor performance in the finite sample case. In this paper, new theoretical approximations are(More)
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(More)
Development of intelligent systems for classifying marine vessels based on their acoustic radiated noise is of major importance in the sonar systems. This paper focuses on three topics. The first topic is applying some modifications to the conventional Probabilistic Neural Network (PNN), as a common classifier in supervised pattern recognition, and(More)
The modified transpose Jacobian (MTJ) algorithm is a recently proposed algorithm used in manipulator control. Based on an approximated feedback linearization approach the MTJ does not need to a priori knowledge of the plant dynamics. In this paper, this scheme is extended to the complicated control problem of underactuated robots in Cartesian space. Based(More)