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In this paper, we use an adaptive method for identifying of minimum phase channel parameters. We have selected two channels as the Proakis's ‘B’ channel and the Macchi's channel. We compared this method with those based on the Higher Order Statistics. The obtained simulation results in noisy environment and for different data input channel, demonstrate that the adaptive method is efficient and can...
This work concerns the problem of the supervised identification of the parameters of non linear models using 3rd order moments. The input sequence is assumed to be independent and identically distributed (i.i.d), zero mean and must be non-Gaussian. The developed algorithm is tested and compared with other method developed in literature. Simulation examples are provided to verify the performance of...
This work concerns the development of two approaches for the identification of diagonal parameters of quadratic systems from only the output observation. The systems considered are excited by an unobservable independent identically distributed (i.i.d), stationary zero mean, non-Gaussian process and corrupted by an additive Gaussian noise. The proposed approaches exploit higher order cumulants (HOC)...
In this paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of Internet traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision...
In this paper we develop an algorithm based on the 3th and 4th order cumulant techniques for supervising identification of non linear systems. This algorithm is compared with the recursive least square (RLS) algorithm for different signal to noise ratios (SNR) and different length of output sequences. The simulation results prove the performance of the developed algorithm. In the last part, we apply...
In this paper, we developed a model based on the adaptive neuro-fuzzy inference systems (ANFIS) for analyzing a real non Gaussian process. The obtained results show that the generated values using ANFIS techniques have similar statistical characteristics as real data. Additionally, the developed model fits well real data and can be used for predicting purpose. Compared with existing model obtained...
In this paper a non linear system identification problem is addressed. A support vector regressor is used to solve the Internet traffic identification problem. We give a basic idea underlying support vector (SV) machine for regression, which is a novel type of learning machine based on statistical learning theory. Furthermore, we describe how SV regressor can be applied for non linear system identification...
In this work we compare tow methods for the identification of non-linear systems. The first one uses a quadratic non linear model of which parameters are estimated using a new algorithm based on the fourth order cumulants. The second one is based on the Takagi-Sugeno fuzzy models. The simulation results show that the fuzzy models give the good results in noiseless and weak noise environment. However...
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