Neurocomputing

We present a novel local surface description technique for automatic three dimensional (3D) object recognition. In the proposed approach, highly repeatable keypoints are first detected by computing the divergence of the vector field at each point of the ...

We consider a variant of the multi-armed bandit model, which we call multi-armed bandit problem with known trend, where the gambler knows the shape of the reward function of each arm but not its distribution. This new problem is motivated by different ...

In most sparse coding based image restoration and image classification problems, using the non-convex źp-norm minimization (0źp<1) can often deliver better results than using the convex ź1-norm minimization. Also, the high computational costs of ź1-...

In this paper, we propose the architecture of a fault-tolerant unit in a modular neurocomputer that is based on decoding with computation of errors syndromes on redundant moduli and implemented using FPGA and a finite ring neural network. The ...

A radial Basis Function Network (RBFN) works well as a nonlinear approximator in direct adaptive control, as long as the number of inputs is low. A Cerebellar Model Arithmetic Computer (CMAC) indexes basis functions efficiently and can handle many ...

Point predictions of wind speed can hardly be reliable and accurate when the uncertainty level increases in data. Prediction intervals (PIs) provide a solution to quantify the uncertainty associated with point predictions. In this paper, we adopt radial ...

In this work several univariate approaches for short-term load forecasting based on neural networks are proposed and compared. They include: multilayer perceptron, radial basis function neural network, generalized regression neural network, fuzzy ...

This paper is mainly concerned on stability problem of Markovian jump neural networks with mode-dependent two additive time-varying delays based on quadratic convex combination approach. The jumping parameters are modeled as a continuous time, finite ...

Many existing methods for frame deletion detection attempt to detect abnormal periodical artifacts in video stream, however, due to a number of reasons, the periodical artifacts can not always be reliably detected. In this paper, we propose a new method ...

Recently, Local Fisher Discriminant Analysis (LFDA) has achieved impressive performance in person re-identification. However, the classic LFDA method pays little attention to the intrinsic geometrical structure of the complex person re-identification ...

Low-rank tensor completion (LRTC) has been applied in many real-world problems. But most of the existing LRTC methods recover a tensor on a single dataset with the low-rank assumption, suffering from a low accuracy due to the complicated structures of ...

This study proposes a metaheuristic-based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic-based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism ...

Based on popular active contours, this paper proposes a novel two-stage image segmentation method, which incorporates the global and local image region fitting energies. In the first stage, according to the global region active contour, we preliminarily ...

Contextual information has been proven to be valuable factor for building personalized Recommender Systems. However, most existing solutions based on probabilistic matrix factorization in recommender systems do not provide a straightforward way of ...

Missing values exist in many generated datasets in science. Therefore, utilizing missing data imputation methods is a common and important practice. These methods are a kind of treatment for uncertainty and vagueness existing in datasets. On the other ...

Nonnegative matrix factorization (NMF) is a promising algorithm for dimensionality reduction and local feature extraction. However, NMF is a linear and unsupervised method. The performance of NMF would be degraded when dealing with the complicated ...

The fast synchronization problem for a class of complex dynamical networks with time varying delay by means of periodically intermittent control is studied. Based on the finite-time stability theory and periodically intermittent control technique, some ...

This paper is concerned with the input-to-state stability problem of a class of neutral stochastic neural networks. The stochastic neural networks that we consider contain both neutral terms and mixed delays. By utilizing the Lyapunov-Krasovskii ...

This paper studies a robust fault tolerant control of a class of nonlinear switched systems with strong structural uncertainties and actuator faults. This paper presents a new robust fault tolerant state feedback method, by using the average dwell time ...

This paper investigates exponentially stable guaranteed cost control (GCC) for a class of nonlinear systems which is represented by Takagi-Sugeno (T-S) fuzzy systems. State feedback controllers of parallel distributed compensation (PDC) structure are ...

Matrices are appropriate for representing a wealth of data with complex structures such as images and electroencephalogram data (EEG). To learn a classifier dealing with these matrix data, the structure information of the feature matrix is useful. In ...

Differential Evolution (DE) has been widely researched because of its excellent performance and many differential evolution variants have been proposed. However, no variant was able to consistently perform over a wide range of test problems. This paper ...

This paper presents a fast algorithm and an accelerated toolbox11https://github.com/akusok/elmvis for data visualization. The visualization is stated as an assignment problem between data samples and the same number of given visualization points. The ...

Forecasting stock price is a hot issue for stock investors, dealers and brokers. However, it is difficult to find out the best time point to buy or sell stock, since many variables will affect the stock market, and stock dataset is time series data. ...

Among several palmprint recognition methods proposed recently, coding-based approaches using multi-spectral palmprint images are attractive owing to their high recognition rates. Aiming to further improve the performance of these approaches, this paper ...

The objective of this work is single image super-resolution (SR), in which the input is specified by a low-resolution image and a consistent higher-resolution image should be returned. We propose a novel post-processing procedure named iterative fine-...

A wide variety of methods have been developed to predict where people look in natural scenes focused on pixel-level image attributes. Most existing methods measure the saliency of a pixel or region based on its contrast within a local context or the ...

This paper has two objectives: (a) it describes the problem of finding a precise and uncomplicated model of a neutralisation process, (b) it details development of a nonlinear Model Predictive Control (MPC) algorithm for the plant. The model has a ...

To tackle the challenge of more and more stringent emission regulations, a selective catalytic reduction (SCR) system is widely used all over the world in Diesel-engine applications. In SCR system, input states may be indispensable for onboard ...