This paper proposes a trajectory model which is based on a mixture density network trained with target features augmented with dynamic features together with an algorithm for estimating maximum likelihood trajectories which respects constraints between the static and derived dynamic features. This model was evaluated on an inversion mapping task. We found the introduction of the trajectory mode...

The wavelet transform-domain least-mean square (WTDLMS) algorithm uses the self-orthogonalizing technique to improve the convergence performance of LMS. In WTDLMS algorithm, the trade-off between the steady-state error and the convergence rate is obtained by the fixed step-size. In this paper, the WTDLMS adaptive algorithm with variable step-size (VSS) is established. The step-size in each subf...

The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input auto-correlation matrix is ill-conditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the ad...

Abstract. Adaptive inverse control of linear system with fixed learning rate least mean square (LMS) algorithm is improved by varying the learning rate. This variable learning rate LMS algorithm is proved to be convergent by using Lyapunov method. It has better performance especially when there is noise in command input signal. And it is simpler than the Variable Step-size Normalized LMS algori...

This paper investigates the properties of the performance surface for the problem of nonlinear mean-square estimation of a random sequence. The problem studied has direct application to the study of active noise control (ANC) systems when the transducers are driven into a nonlinear behavior. A deterministic expression is derived for the mean-square error (MSE) surface as a function of the syste...

Adaptive filters updated by the least mean square (LMS) algorithm are succesfully implemented in digital control systems. They are utilized for both plant identification and control purposes. In control appications (e.g. in active noise control) the output of the adaptive filter drives the plant input, and the error signal is derived only at the output of the plant. In such cases the filtered r...

A standard algorithm for LMS-filter simulation, tested with several convergence criteria is presented in this paper. We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when random functions are used as reference inputs. In this paper, we make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix...

This paper proposes an adaptive algorithm, which can be pipelined, as an extension of the delayed least mean square (DLMS) adaptive algorithm. The proposed algorithm provides a capability to achieve high throughput with less degradation of the convergence characteristic than the DLMS algorithm. An architecture for pipelined implementation of the proposed algorithm is considered, and based on th...

The distributed system is a paradigm in which the components can communicate by means of the interface connected between them. The interaction between the components takes place in order to achieve a common goal. The reliability metrics is used to evaluate the performance of the components which is connected by the distributed systems. The Mean Time Between Failure (MTBF), Mean Time to Repair (...