The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Rational approximations in terms of infinite impulse response filter for the full band digital differentiator (DD) based on a metaheuristic optimization technique called Adaptive G-best Guided Gravitational Search Algorithm (GGSA) is proposed in this paper. The use of adaptive acceleration coefficients in GGSA provides a better balance in the intensification and the diversification of GGSA as compared...
In this paper, an adaptive adjustment method for the kernel parameter used in the kernel adaptive filters (KAFs) is proposed. The KAF is one of the linear-in-the-parameters (LIP) nonlinear filters, and is based on the kernel method used in machine learning. Typically, the Gaussian kernel function is used, but there is no effective method for automatically adjusting its parameter that influences the...
A novel adaptation strategy is proposed for Acoustic Echo Cancellation (AEC). The new algorithm firstly partitions the adaptive filter into several blocks and the successive blocks with the maximum h norm are considered to be the active blocks. Then the coefficients of the active blocks are adapted with large update probability to ensure the identification accuracy while the zero coefficients are...
The distributed incremental affine projection algorithm (DIAPA) with constant step size inevitably encounters the conflict between the fast convergence rate and low steady-state misalignment. Thus, this paper proposes an adaptive combination of step sizes for distributed incremental affine projection algorithm (ACSS-DIAPA), which is updated by effectively employing a mixing parameter to combine the...
In this paper, the convergence and the stability analysis of two Induction Motor (IM) rotor time constant (Tr) estimators are performed. Both estimators are Model Reference Adaptive System (MRAS) based, first considering the rotor flux vector, second the reactive power as the state variable. The stability analysis is based on Lyapunov and Popov criteria, passivity formalism and positive real function...
Predicting the desirability and number of student applications to universities is a challenging and dynamic undertaking. Student applications are affected by a variety of factors ranging from those related to the university itself and its surrounding community, to factors up to the national level, including perceptions of international relations. This paper proposes a way of modeling this complex...
Due to the rapid growth in scale and complexity of information networks, self-organizing systems have been focused on for realizing new network control architectures that have high scalability, adaptability, and robustness. However, in self-organizing systems, the uncertainty (incompleteness, ambiguity, and dynamicity) of information observable for components in the system can lead to the slow adaptation...
This paper proposes a new method for continuous acoustic adaptive feedback cancelation (AFC) in digital hearing aids. The proposed method employs two adaptive filters working in tandem. The first adaptive filter is excited by the receiver (output) signal of the hearing aid, and uses microphone signal as its desired response. The lattice-predictor based adaptive algorithm is used to updated the coefficients...
In the paper a modification enabling acceleration of the rate of convergence for LMS-like on-line identification and adaptation algorithms is proposed. This is based on an artificial decaying of initial conditions in recursive identification as well as adaptation algorithms. The decaying is done using a set of the most recent measurements. Properties of the algorithms with the proposed modification...
Parametrization of process models is an important task and often the first step in process control and monitoring. For continuous fluidized bed spray granulation, being often described by population balance models parameter estimation is particularly challenging due to the infinite-dimensional state space. In this contribution a Lyapunov-based approach is used to derive the appropriate online parameter...
In this paper we present the co-simulation of a PID class power converter controller and an electrical circuit by means of the waveform relaxation technique. The simulation of the controller model is characterized by a fixed-time stepping scheme reflecting its digital implementation, whereas a circuit simulation usually employs an adaptive time stepping scheme in order to account for a wide range...
In this paper, we propose sparsity-aware data-selective adaptive filtering algorithms with adjustable penalties. Prior work incorporates a penalty function into the cost function used in the optimization that originates the algorithms to improve their performance by exploiting sparsity. However, the strength of the penalty function is controlled by a scalar that is often a fixed parameter. In contrast...
Digital predistortion (DPD) is an effective power amplifier (PA) linearization technique improving the system energy efficiency. At this point, real-time DPD adaptation is still an open issue due to the high computational complexity during the coefficients estimation procedure. Online censoring approach, which is effective in reducing the redundant data samples, can be applied in the DPD coefficients...
Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly...
In this paper we study rendezvous control of multiple mobile robots. We propose a control law that merely requires each robot to measure the relative bearings of their neighbors in their local coordinate frames. Distance measurement or relative position estimation is not required. In theory, the proposed control law verifies that distance information is redundant in rendezvous control tasks though...
Convolutional dictionary learning (CDL) has great potential to “learn” rich sparse representations from training datasets, by training translation-invariant filters. However, the performance of applying learned filters from CDL to inverse problems has not yet been fully maximized because training data preprocessing in training stage is not fully compensated in testing stage. We propose CDL using Adaptive...
Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are capable of tracking changes in underlying distributions of input features. This intrinsically...
Single-integrator models have been widely used to model robot kinematics in multi-robot coordination control problems. However, it is also widely believed that this model is too simple to lead to practically useful control laws. In this paper, we prove that if a gradient-descent distributed control law designed for single integrators has been proved to be convergent for a given coordination task,...
This paper considers the problem of interpolating signals defined on graphs. A major presumption considered by many previous approaches to this problem has been low-pass/band-limitedness of the underlying graph signal. However, inspired by the findings on sparse signal reconstruction, we consider the graph signal to be rather sparse/compressible in the Graph Fourier Transform (GFT) domain and propose...
This paper deals with the real-time steady-state optimization of slow dynamic processes under plant-model mismatch. A novel scheme that iteratively adapts the process set-points (or operating parameters) to attain an economic optimum is proposed. Based on computing the next steady state from the process transient response to the current set-point change, this scheme can significantly reduce the time...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.