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Two-stage recursive least squares method for modeling power signals
2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015This paper studies two-stage recursive least squares identification problems for power signals by the decomposition technique. The basic idea is to decompose a power signal model into two submodels and then to identify the parameters of each submodel, respectively.
null Xiangli Li +2 more
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Adaptive state observer development using recursive extended least-squares method
2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2017This paper presents a recursive algorithm for adaptive observation of linear single-input single-output (SISO) time-invariant discrete systems. The problem of state observation is of main importance in automatic control, especially for designing modal state controllers when the state variables of the system are unknown.
Nikola N. Nikolov +3 more
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Fast residual computation for sliding window recursive least squares methods
Signal Processing, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yoo, Kyeongah, Park, Haesun
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Identification of interval fuzzy models using recursive least square method
2010 IEEE International Conference on Systems, Man and Cybernetics, 2010In this paper, we present a new method of interval fuzzy model identification. Unlike the previously introduced methods, this method uses recursive least square methods to estimate the parameters. The idea behind interval fuzzy systems is to introduce optimal lower and upper bound fuzzy systems that define the band which contains all the measurement ...
Mojtaba Ahmadieh Khanesar +2 more
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Fixed memory least squares filters using recursion methods
IEEE Transactions on Information Theory, 1957Given a set of equally spaced measurements, it is possible to curve fit a "least squares" polynomial to the N observed data points and obtain estimates of the past, present, or future values of the data or its derivatives by appropriate manipulations of the curve fit.
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Recursive Least Squares Method for Identification of MEMS Orientation Sensors Parameters
2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 2019The main contribution to the error in determining the parameters of motion of moving objects make instrumental errors due to both the imperfection of the technology of manufacturing elements of MEMS accelerometers and gyroscopes, and the deviation of their characteristics when changing operating conditions.
Kseniya I. Goryanina +1 more
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Identification of asynchronous machine parameters by a recursive least-squares method
Proceedings of IEEE International Conference on Control and Applications CCA-94, 1994In this paper, an identification of asynchronous machine parameters by a recursive least-squares method with forgetting factor is presented. The authors perform an identification procedure based on stator voltage and current measurements. The authors use band-pass filters to stabilize the identification algorithm. For different time sampling, they have
null Guesbaoui, null Touhami, null Iung
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Hierarchical Fuzzy identification using gradient descent and recursive least square method
2013 3rd IEEE International Conference on Computer, Control and Communication (IC4), 2013In this paper, the parameters of hierarchical fuzzy systems are trained using the simultaneous use of Gradient Descent (GD) for nonlinear parameters and recursive least square (RLS) algorithm for linear parameters. One of the most effective ways to overcome the curse of dimensionality of fuzzy systems is the use of hierarchical fuzzy systems (HFS ...
Zeinab Fallah +2 more
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Fast Training of Recurrent Neural Networks by the Recursive Least Squares Method
1997In this work a novel approach to the training of recurrent neural nets is presented. The algorithm exploits the separability of each neuron into its linear and nonlinear part. Each Iteration of the learning consists of two steps: first the descent of the error functional in the space of the linear outputs of the neurons is performed (descent in the ...
PARISI, Raffaele +3 more
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Combining PCA and MCA by using recursive least square learning method
1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196), 2002By using the fact that the derivatives of the ith network output with respect to the weights connected to the jth output neuron (i/spl ne/j) are zero, a modified RLS method is proposed for principal and minor components analysis. After the extraction of significant components of the input vectors, the error covariance matrix obtained in the learning ...
A.S.Y. Wong, K.W. Wong, C.S. Leung
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