In this paper the problem of the adaptive Parameter Estimation of 2-D systems is defined, and a solution method based on identification model is presented for it. In this solution method, the system Parameters are decomposed into two blocks, namely the horizontal and vertical blocks. The Estimations of the horizontal and vertical blocks, which are the identification model adjustable Parameters, are adjusted along the horizontal and vertical directions respectively. Thus a 2-D adaptive algorithm is obtained for estimating the 2-D systems Parameters. The convergence (stability) of the 2-D algorithm is analyzed via 2-D layponov approach, and also the algorithm asymptotic stability conditions, that are the 2-D persistent excitation conditions, are obtained in terms of the system locally controllability and the system input generality. In the end of paper the effectiveness of the presented procedure is illustrated by computer simulation.

The Estimation of unknown Parameters of two-Parameter Rayleigh distribution based on Type-II progressive censoring with binomial removals is studied. Maximum likelihood estimators of the Parameters and their confidence intervals are derived. By applying Markov Chain Monte Carlo techniques, Bayes estimators, and corresponding highest posterior density confidence intervals of Parameters are obtained. The expected time required to complete the life test under this censoring scheme is investigated. Monte Carlo simulations are performed to compare the performances of the different methods, and one data set is analyzed for illustrative purposes.

This paper studies the problem of binary RS code Parameters Estimation in noisy scenario. This problem comes to military applications as well as cognitive radio receivers design. Despite the widespread application of the RS codes, so far just a few methods have been proposed to solve it. Most of these have a high complexity and are effective only in the very low noise. This paper proposes a new, effective and low-complexity method to identify the RS code Parameters. In this method, the code length and primitive polynomial and then message length is determined. The Estimation of Parameters is based on the identification of a special set. The main feature of this set is the presence of transmitted codewords in the all codes belonging to it. In this paper, a test is proposed to identify the set codes. In this test, the parity check bits are generated again by the received message bits, and then compared with the received parity check bits. If the number of bit differences is greater than the threshold, presence of RS code is verified in the set. In this paper, two appropriate thresholds are proposed. The first threshold is designed based on Minimax decision rule and is depended on the channel error. However, the second threshold is experimental and is independent of channel error. The simulation verifies the high performance of this method. For example, the method can perfectly estimate the Parameters of RS code with length of 63 up to 4×10-3 error rate.

This paper presents the application of recently introduced water cycle algorithm (WCA) to optimize the Parameters of exact and approximate induction motor from the nameplate data. Considering that induction motors are widely used in industrial applications, these Parameters have a significant effect on the accuracy and efficiency of the motors and, ultimately, the overall system performance. Therefore, it is essential to develop algorithms for the Parameter Estimation of the induction motor. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. The objective function is defined as the minimization of the real values of the relative error between the measured and estimated torques of the machine in different slip points. The proposed WCA approach has been applied on two different sample motors. Results of the proposed method have been compared with other previously applied Meta heuristic methods on the problem, which show the feasibility and the fast convergence of the proposed approach.

Fuzzy regression represents the relation between variables. Fuzzy regression analysis is one of the most widely used statistical techniques. In this study, fuzzy regression is considered with the crisp input and the fuzzy output. A hybrid algorithm based on fuzzy weights and linear programming is designed for the fuzzy nonparametric regression model prediction that function form is assumed unknown and in cases low data. In proposed method, the objective function minimizes the spread of outputs. Finally, the performance of the suggested method is compared with linear programming (LP) and quadratic programming (QP) methods using the numerical examples. The results demonstrate that the proposed method has more accurate than the LP and QP methods. Also, it is verified in cases of low data.

This study introduce a new frequency Parameter calledsfcwt, which can be used to estimate earthquake magnitude on the basis of the first few seconds of P-waves, using the waveforms of earthquakes occurring in Japan. This new Parameter is introduced using continuous wavelet transform as a tool for extracting the frequency contents carried by the first few seconds of P-wave.The empirical relationship between the logarithm of tfcwt within the initial 4 s of a waveformand magnitude was obtained. To evaluate the precision of tfcwt, we also calculated Parameters tpmax and tc. The average absolute values of observed and estimated magnitude differences (½Mest -Mobs½) were 0.43, 0.49, and 0.66 units of magnitude, as determined using tpmax, tc, and tfcwt, respectively. For earthquakes with magnitudes greater than 6, these values were 0.34, 0.56, and 0.44 units of magnitude, as derived using tpmax, tc, and tfcwt, respectively. The tfcwt Parameter exhibited more precision in determining the magnitude of moderate- and small-scale earthquakes than did the tc-based approach. For a general range of magnitudes, however, the tpmax -based method showed more acceptable precision than did the other two Parameters.

This paper implements a derivative free optimization method called "Genetic Algorithm" to estimate the Parameters of a four-wheel, three degrees of freedom vehicle handling model. At first the model is developed containing a non-linear tire model called "Fiala". Then, an error function is defined and the "Genetic Algorithm" optimization method is introduced and applied to minimize the error. Finally, verification of Parameter Estimation is checked.

Recently, studies of squirrel-cage induction machines validate the idea that these machines follow the double-cage induction model; however, the manufacturers categorize these machines as single-cage machines. There are many references that use the squirrel-model for squirrel-cage machines, then machine Parameters increase from 5 to 7. In the laboratory, Parameter determination is performed to estimate 5 Parameters; however, 7 Parameters have to be determined. Also, the DC test is not an exact test and is used for approximate Estimation. Therefore, locked rotor test equations are changed and an overload test is added. After measurements in the laboratory according to the equations of this paper, a MATLAB program is prepared and 7 Parameters are determined in a loop until reaching the desired error. This paper demonstrates that laboratory squirrel-cage machines have to be corrected using the 7-Parameter model. Furthermore, this paper shows the necessity of new laboratory test equations for squirrel-cage machines. Then a numerical method is used to determine the machine Parameters.