In identification of a finite impulse response (FIR) model using noise-corrupted input and output data, the least squares type of estimation schemes such as the ordinary least squares (LS), the corrected least squares (CLS) and the total least squares (TLS) method become often numerically unstable, when the true input signal to the system is strongly correlated. To overcome this ill-conditioned problem, we propose a regularized CLS estimation method by introducing multiple regularization parameters to minimize the mean squares error (MSE) of the regularized CLS estimate of the FIR model. The asymptotic MSE can be evaluated by considering the third and fourth order cross moments of the input and output measurement noises, and an analytical expression of the optimal regularization parameters minimizing the MSE is also clarified. Furthermore, an effective regularization algorithm is given by using the only accessible input-output data without using any true unknown parameters. The effectiveness of the proposed data-based regularization algorithm is demonstrated and compared with the ordinary LS, CLS and TLS estimates through numerical examples.

The main purpose of this paper is to give a new representation method of the convergence characteristics of the LMS algorithm using tap-input vectors. The described representation method is an extended version of the interpretation method based on the orthogonal projection. Using this new representation, we can express the convergence characteristics in terms of tap-input vectors instead of the eigenvalues of the input signal. From this representation, we consider a general method for improving the convergence speed.

We consider the identification of a class of systems which are both time-varying and nonlinear. Time-varying nonlinear systems are often encountered in practice, but tend to be avoided due to the difficulties that arise in modelling and estimation. We study a particular time-varying polynomial model, which is a member of the class of time-varying Wiener models. The model can characterise both time-variation and nonlinearity in a straightforward manner, without requiring an excessively large number of coefficients. We formulate a procedure to find least-squares estimates of the model coefficients. An advantage of the approach is that systems with rapidly changing dynamics can be characterised. In addition, we do not require that the input is stationary or Gaussian. The approach is validated with an application to an automobile modelling problem, where a time-varying nonlinear model is seen to more accurately characterise the system than a time-invariant nonlinear one.

A new variable step size Least Mean Square (LMS) FIR adaptive filter algorithm (VSS-CC) is proposed. In the VSS-CC algorithm the step size adjustment (α) is controlled by using the correlation between the output error (e(n)) and the adaptive filter output ((n)). At small times, e(n) and (n) are correlated which will cause a large α providing faster tracking. When the algorithm converges, the correlation will result in a small size α to yield smaller misadjustments. Computer simulations show that the proposed VSS-CC algori thm achieves a better Echo Return Loss Enhancemen (ERLE) than a conventional NLMS Algorithm. The VSS-CC algorithm was also compared with another variable step algorithm, achieving the VSS-CC a better ERLE when the additive noise is incremented.

An adaptive joint-process IIR filter with generalized lattice structure is constructed. This filter can borrow both FIR and IIR features and simultaneously holds the well-known merits of lattice structure.

In this report, we propose a robust block adaptive digital filter (BADF) which can improve the accuracy of the estimated weights by averaging the adaptive weight vectors. We show that the improvement of the estimated weights is independent of the input signal correlation.

This paper proposes a new approach to the management of large-scale communication networks. To manage large-scale communication networks effectively, it is essential to get a bird's-eye view of them when they are in their normal conditions. When an indication of faulty state is detected, the focus of the management is narrowed down to the faulty network elements until the appropriate granularity is reached. This management scheme is called multilevel network management in this paper, and it first explains the significance of this scheme for large-scale communication networks and presents some ideas on implementing this management scheme. It then proposes that system identification be used in multilevel network management. The system identification is used to measure transmission delays between two arbitrarily selected nodes in the networks, and multilevel network management is achieved by selecting those two nodes appropriately in accordance with the levels to be managed. Finally, it is demonstrated by computation simulation results that the proposed method is suitable for multilevel network management.

A novel total harmonic distortion (THD) measuring technique is proposed. A modified Volterra series using harmonics in place of powers of the sinusoidal input is used to identify the nonlinear models of the source and the device under test (DUT). The least-mean square (LMS) adaptive algorithm is applied for identification. While maintaining comparable speed and accuracy this technique provides a more flexible test procedure than conventional methods, in terms of the frequency resolution, the number of samples, and the sampling rate. It outperforms conventional methods when there is a bin energy leakage, which occurs in a non-coherent system. In addition, it is real-time computing while other conventional methods post process blocks of data. Simulation results collaborate the analytical results.

An alias free parallel structure for adaptive digital filters (ADF's) is considered. The method utilizes the properties of the Frequency-Sampling Filter (FSF) banks to obtain alias free points in the frequency domain. We propose a new cost function for parallel ADF's. The limiting value analysis of system identification using proposed cost function is given in stochastic sense. It is also shown by simulation examples that we can carry out precise system identification. The cost function is defined in each bin; accordingly, it enables the parallel processing of ADF's.

A new gradient type adaptive algorithm is proposed in this paper. It is formulated based on the least squares criteria while the conventional gradient algorithms are based on the least mean square criteria. The proposed algorithm has two variable parameters and by changing them we can adjust the characteristic of the algorithm from the RLS to the LMS depending on the environment. This capability of adjustment achieves the possibility of providing better solutions. However, not only it provides better solutions than the conventional algorithms under some conditions but also it provides a very interesting theoretical view point. It provides a unified view point of the adaptive algorithms including the conventional ones, i.e., the LMS or the RLS, as limited cases and it enables us to analyze the bounds for those algorithms.

In this paper, an ARMA order selection method is proposed with a fuzzy reasoning method. In order to identify the reference model with the ARMA model, we need to determine its ARMA order. A less or more ARMA order, other than a suitable order causes problems such as; lack of spectral information, increasing calculation cost, etc. Therefore, ARMA order selection is significant for a high accurate ARMA model identification. The proposed method attempts to select an ARMA order of a time-varying model with the following procedures: (1) Suppose the parameters of the reference model change slowly, by introducing recursive fuzzy reasoning method, the estimated order is selected. (2) By introducing a fuzzy c-mean clustering methed, the period of the time during which the reference model is changing is detected and the forgetting factor of the recursive fuzzy reasoning method is set. Further, membership functions used in our algorithm are original, which are realized by experiments. In this paper, experiments are documented in order to validate the performance of the proposed method.

In this paper, the relationship between the recursive least square (RLS) method with a U-D decomposition algorithm and ARMA lattice filter realization algorithm is presented. Both the RLS method and the lattice filter realization algorithm are used for the same applications, such as model identification, etc., therefore, it is expected that the lattice filter algorithm is in some ways related to the RLS. Though some of the proposed lattice filter algorithms have been derived by the RLS method, they do not express the relationship between RLS snd ARMA lattice filter realization algorithm. In order to describe the relation clearly, a new structure of ARMA lattice filter is proposed. Further, based on the relationship, a method of model identification with frequency weighting (MIFW), which is different from a previous method, is derived. The new MIFW method modifies the lattice parameters which are acquired without a frequency weighting and obtain the parameters of an ARMA model, which is identified with frequency weighting. The proposed MIFW method has the following restrictions: (1) The used frequency weighting is FIR filter with a low order. (2) By using the parameters of the ARMA lattice filter with ARMA (N,M) order and the frequency weighting with L order, the new ARMA parameter with the frequency weignting is with ARMA(N-L,M-L) order. By using the proposed MIFW method, the ARMA parameters estimated with the frequency weighting can be obtained without starting the computation again.

This paper describes a new method to estimate traffic load of communication nodes, such as switching systems. The new method uses the system identification, which is often used in designing control systems of real systems. First, this paper makes clear that, under certain conditions, the input and output relation of a communication system, which is composed of a number of communication nodes, is formulated into a dynamic state equation that is classed as a time-invariant, single-input single-output, discrete-time system. Next, it is explained that traffic load information is estimated by identifying the dynamic state equations of the communication system. Then, the traffic load estimator is synthesized using the system identification in it. Finally, it is clarified by computation simulations that the proposed method is very applicable in estimating the traffic load of each communication node.

An automatic tap assignment method in sub-band adaptive filter is proposed in this letter. The number of taps of the adaptive filter in each band is controlled by the mean-squared error. The numbers of taps increase in the bands which have large errors, while they decrease in the bands having small errors, until residual errors in all the bands become the same. In this way, the number of taps in a band is roughly proportional to the length of the impulse response of the unknown system in this band. The convergence rate and the residual error are improved, in comparison with existing uniform tap assignment. Effectiveness of the proposed method has been confirmed through computer simulation.

In case of the system identification problem, such as an echo canceller, estimated impulse response obtained by the frequency-domain adaptive filter based on the circular convolution has estimation error because the unknown system is based on the linear convolution in the time domain. In this correspondence, we consider a sufficient condition to reduce the estimation error.