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Regularization plays an important role in machine learning systems. We propose a novel methodology for model regularization using random projection. We demonstrate the technique on neural networks, since such models usually comprise a very large number of parameters, calling for strong regularizers. It has been shown recently that neural networks are sensitive to two kinds of samples: (i) adversarial...
Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition...
This paper considers the identification problem of nonlinear systems based on single-hidden-layer neural networks (SHLNNs) and Lyapunov theory. A nonlinearly parameterized neural model, whose weights are adjusted by robust adaptive laws, which are designed via Lyapunov theory, is proposed for ensuring the convergence of the residual state error to an arbitrary neighborhood of zero. In addition, a...
A PD neural network (NN)-based adaptive controller design is presented in this paper for trajectory tracking of robotic manipulators subject to external disturbances and noise measurement. The neural networks are employed to approximate the nonlinearities in dynamic model of the robot to improve the performance of the classical PD controller based on the filtered error approach. The augmented Lyapunov...
A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify...
This paper deals with an adaptive robust tracking control using a multilayer neural network (NN) for a class of nonlinear dynamic systems with unknown time varying state delays. Typical adaptive NN backstepping controllers for uncertain nonlinear systems with time-delay give rise to computation complexity caused by the the repeated derivatives of virtual controllers and nonlinear functions. Moreover,...
This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data-driven approach is utilized to develop a...
The naval gun weapon systems are the complex and large mechatronic systems which consist of mechanical system, electrical system and hydraulic system and so on. The systems have a wider working range, a worse working environment and a higher failure rate. Whether ship borne gun weapon systems work normally or not, they directly affect the performance indexes of the weapon systems, even do the entire...
A neural network empowered dynamic surface control (DSC) technique is addressed for robot manipulators system with unknown dynamics. In comparison to the conventional adaptive neural control algorithms, which could guarantee semi-globally uniformly ultimate boundedness (SGUUB) only when neural approximation keeps effective, the scheme designed in this paper ensures globally uniformly ultimately bounded...
To meet practical high-performance motion requirements under complicated disturbances, this paper proposes a neural network learning adaptive robust control (NNLARC) strategy for precision motion stages to achieve not only good tracking accuracy but also excellent disturbance rejection capability. Specifically, the NNLARC strategy contains parameter adaption term, robust feedback term, and radial...
In this paper, an adaptive neural network fast terminal sliding mode(ANNFTSM) control method is proposed, which is applied for a class of SISO uncertain nonlinear systems output tracking issue. Because neural network system has the ability to identify the uncertainty online, we combine RBF neural network with fast terminal sliding mode controller. Not only will this successfully solve the problem...
In this paper, a class of second-order nonlinear time-delayed multiagent systems with disturbance is investigated. In order to improve the adaptivity, neural networks are used to learn the unknown dynamics. Then, by utilizing Lyapunov-Krasovskii functional, time delays can be eliminated. Moreover, a robustifying term is introduced to constrain external disturbance. With divide-and-conquer idea, the...
The capability of artificial Neural Networks to forecast time series with trends has been a topic of dispute. While selected research following Zhang and Qi has indicated that prior removal of trends is required for a Multilayer Perceptron (MLP), others provide evidence that Neural Networks are capable of forecasting trends without data preprocessing, either by choosing input-nodes employing an adequate...
Roadside vegetation classification has recently attracted increasing attention, due to its significance in applications such as vegetation growth management and fire hazard identification. Existing studies primarily focus on learning visible feature based classifiers or invisible feature based thresholds, which often suffer from a generalization problem to new data. This paper proposes an approach...
Contrary to traditional evolutionary models of complex networks a novel consideration with two-formation & degradation — phases has been proposed. To clarify which of the stages of the phases are more sensible the evolving network has been put to simulated attacks. A novel integral vulnerability metrics has been proposed which demonstrated strong dependence on the network growth rate of the stage...
In this work, a systematical and methodological ANN optimization process known as robust design of artificial neural networks methodology, based on Taguchi method and Design of Experiments methodology, was applied to the design, training and testing of feed forward artificial neural networks trained with back-propagation training algorithm applied in the neutron spectrometry research area. The methodology...
This paper considers a strategy for energy coordination including flow control and generation in the distributed grid networks. The distributed energy coordination scheme proposed in [5] seeks to achieve the supply-demand balance on the basis of interactions between neighbors. First, details of the algorithm for the distributed energy coordination are described. Then, we provide general solutions...
Handwriting in data entry forms/documents usually indicates user's filled information that should be treated differently from the printed text. In Arab world, these filled information are normally in English or Arabic. Secondly, classification approaches are quite different for machine printed and script. Therefore, prior to segmentation & classification, text distinction into Printed & script...
In this paper, new approach is proposed for stabilization of a cart inverted pendulum system using Linear Quadratic Regulator (LQR) based PID controller and Artificial Neural Network (ANN). The proposed approach is compared with the recently published approach on designing of PID controller using LQR. It is observed that the proposed design shows better performance and disturbance rejection.
Detection of lung abnormalities by characterizing lung sounds has been a primary step for clinical examination for a pulmonologist. This work focuses on utilization of cepstral features for lung sound analysis and classification. The proposed method incorporates statistical properties of cepstral features along with artificial neural network (ANN) based classification. Experimental results indicate...
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