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.
Patients with locked-in-syndrome (fully paralyzed but aware) struggle in their life and communication. Providing a level of communication offers these patients a chance to resume a meaningful life. Current brain-computer interface (BCI) communication requires users to build words from single letters selected on a screen, which is extremely inefficient. Faster approaches for their speech communication...
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and contains multiple fields, a typicalrepresentation is to transform it into a high-dimensional sparsebinary feature representation via one-hot encoding...
The popular i-vector approach to speaker recognition represents a speech segment as an i-vector in a low-dimensional space. It is well known that i-vectors involve both speaker and session variances, and therefore additional discriminative approaches are required to extract speaker information from the ‘total variance’ space. Among various methods, the probabilistic linear discriminant analysis (PLDA)...
Pair wise learning to rank algorithms (such as Rank SVM) teach a machine how to rank objects given a collection of ordered object pairs. However, their accuracy is highly dependent on the abundance of training data. To address this limitation and reduce annotation efforts, the framework of active pair wise learning to rank was introduced recently. However, in such a framework the number of possible...
In this paper, a motion control problem for underwater gilders in longitudinal plane is considered. A recurrent neural network based model predictive control approach is developed. The model predictive control of underwater gliders is formulated as a time-varying constrained quadratic programming problem, which is solved by using a recurrent neural network called the simplified dual network in real-time...
The neural networks based attribute hierarchy method (NN-AHM) is promoted in this study, it could be applied in cognitive diagnosis. AHM could classify examinees' responses into a set of attribute mastery patterns. Very large sample is needed for AHM, and it is hard to describe the examinee master degree for each attribution. The generalized regression neural network is used in this study to estimate...
In this paper, a one-layer recurrent neural network is presented for solving single-ration linear fractional programming problems subject to linear equality and box bound constraints. The convergence condition is derived to guarantee the solution optimality to the fractional programming problems if the design parameters in the neural network are larger than the derived lower bounds. Two numerical...
Recently, a continuous-time k-winners-take-all (kWTA) network with a single state variable and a hard-limiting activation function and its discrete-time counterpart were developed. These kWTA networks have proven properties of finite-time global convergence and simple architectures. In this paper, the kWTA networks are applied for information retrieval, such as web search. The weights or scores of...
This paper proposes an online clustering algorithm for wind speed forecasting. The algorithm combines the persistence method and the RBF neural network, and chooses an appropriate method according to different wind conditions. Computer simulations demonstrate that this algorithm can more accurately predict wind speed than either of the single methods and therefore is more effective for wind speed...
In this paper, we introduce the notion of “weight” to task's capability, and describe the use of case-based learning and reinforcement learning in a coalition formation model when games are repeated. Based on the the notion “weight” we introduce, a weight-based coalition formation algorithm is proposed, but this algorithm can't always generate good coalitions, to supplement this, an randomized weight-based...
A Dynamic Constitution Evaluation Model for supplier selection and evaluation was suggested in this paper. Subjective weights are given based on G1 method, and objective weights are obtained from entropy method. The combination of subjective and objective weights avoids the one-sided result, which is the limitation of single method. Using dynamic multi-stages linear constitution method on the evaluation...
Carrier Interferometry Orthogonal frequency division multiplexing (CI/OFDM) has gained a great deal of attention recently because of its good bit error rate (BER) and low peak-to-average power ratio (PAPR) performance. In this paper, we investigate the performance of CI/OFDM based on a new implementation model for both zero forcing (ZF) and minimum mean square error (MMSE) equalization. We further...
Memristor is a newly prototyped nonlinear circuit device. Its value is not unique and changes according to the value of the magnitude and polarity of the voltage applied to it. In this paper, a simplified mathematical model is proposed to characterize the pinched hysteretic feature of the memristor, a memristor-based recurrent neural network model is given, and its global stability is studied. Using...
In recent years, constrained sparsity maximization problems received tremendous attention in the context of compressive sensing. Because the formulated constrained L0 norm minimization problem is NP-hard, constrained L1 norm minimization is usually used to compute approximate sparse solutions. In this paper, we introduce several alternative objective functions, such as weighted L1 norm, Laplacian,...
Empirical Mode Decomposition (EMD) is a new way to process the non-stationary and nonlinear data. But the edge effect or boundary effect appears when spline interpolation is used to get two envelopes of the data. A novel method based on improved characteristic wave algorithm is proposed to prolong the data series and get extra-extremes to deal with the edge effect. Two simulated signals are decomposed...
Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. There exist supervised...
A method for representing gait feature based on frequency domain analysis is proposed. For a gait represented by a sequence of binarized silhouettes, its frequency domain feature is extracted following period detection, contour extraction and unwrapping, distance normalization, 2D Fourier transform, feature frequencies computation and some other procedures. Nearest neighbor classifier is used to test...
The fundamental of neural network and its application are introduced firstly. Then the main factors which affect the runoff and the sediment transport volume in the problems of the water and sediment fluxes in the Yellow River Mouth during the flood and non-flood period are analyzed. Furthermore, the BP model is set up by using the program in the toolbox of the neural network under the environment...
As the liquid position control system has the characteristic of a large time delay, instability and non-linear, a neural network self-adaptive PID controller based on self-adaptive PID and neural network is introduced in this paper, it can optimize and adjust the controller parameters on line. The system simulation is carried out in the end. The simulation results show that the control effect of this...
Inverse kinematic motion planning of redundant manipulators by using recurrent neural networks in the presence of obstacles and uncertainties is a real-time nonlinear optimization problem. To tackle this problem, two subproblems should be resolved in real time. One is the determination of critical points on a given manipulator closest to obstacles, and the other is the computation of joint velocities...
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.