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Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big challenges today around the world. The fault detection and isolation (FDI) play a significant role in the monitoring, repairing and maintaining of technical systems for the final destination of cost reduction. FDI makes it possible to reduce total cost effective of maintenance and thus increase the capacity utilization...
Finanicial market is characterized with complex, stochastic, nonstationary process and the development of effective models for prediction of a stock price is one of the important problems in finance. For analyzing nonlinear time-series, the importance of nonlinear models, such as neural networks (NNs) and fuzzy systems (FSs), has been increasing in recent years. Combining NNs, FSs and wavelets, FuzzyWavelet...
This paper reports the development of a software suite to be accessed in future with any General Packet Radio Service (GPRS) or High Speed Packet Access (HSPA) enabled mobile phone or Personal Digital Assistant (PDA) for the extraction and analysis of disease-related features from the photograph of paper based ECG records. In India and other developing countries, the cheaper paper based ECG machines...
This paper considers a continuous control design for second-order control affine nonlinear systems with time-varying state delays. A neural network is augmented with a robust integral of the sign of the error (RISE) control structure to achieve semi-global asymptotic tracking in the presence of unknown, arbitrarily large, time-varying delays, not linear-in-the-parameters uncertainty and additive bounded...
An asymptotic tracking control law is proposed for a class of strict-feedback nonlinear systems with unknown nonlinearities. A Barrier Lyapunov function in combination with backstepping is proposed to guarantee that the output trajectory is contained in a predefined set. A single neural network (NN), whose weights are tuned online, is utilized in our design to approximate the unknown functions in...
Stereo matching is widely using for 3D reconstruction, which aims to obtain corresponding locations between pairs of stereo images. In this paper we present a robust neural aggregation method for matching correspondences in stereoscopic color image. A data structure disparity space image (DSI) was firstly introduced for development of a local-based matching algorithm. To make good use of color information,...
In this paper, a novel approach is proposed to discover community membership in complex networks with node topology potential, along with the experiment on complex biological networks. The concept of physical field is brought into networks. Nodes will have a certain topology potential since they can affect and be affected by the others nearby. And this topology potential is an index to measure the...
In this paper, we present a robust adaptive neural network control design approach for strict-feedback nonlinear systems with uncertainties. In the controller design process, all unknown terms at intermediate steps are passed down and approximated by a single neural network at the last step. By this way, the structure of the designed controller is much simpler, and the control law and the adaptive...
While it is widely recognised that, on the one hand, reductionistic approaches are inadequate to deal with the multifactorial and complex nature of health and disease, and on the other hand, a system level understanding of the normal and pathological functioning of biological systems is sorely needed; no clear procedure have been put forth about the actual implementation of such a program. In this...
Hitherto, different efforts have been held for the recognition of emotional state of speakers. Most of these works are performed in clean environments. But, in the real world, there are different noise parameters such as cross-talk, car noise, awgn (especially in the transmission of sounds) and etc., which decrease the performance of classifiers. In this paper we look for features which have the best...
In this paper, in order to improve the Global Navigation Satellite System (GNSS) code tracking performance, a novel adaptive proportional-integral-derivative (PID) controlling strategy based on Radial Basis Function (RBF) neural network (NN) online identification is proposed in the loop filter design of the code tracking loop for a GNSS receiver. This proposed technique combines conventional PID controlling...
Intermarket analysis studies interrelationships between various related markets. Standard correlations between markets are not useful if our goal is to either predict future prices or generate profitable signals because current correlation does not tell us anything about future prices. A methodology we originally developed in the mid 1990's called intermarket divergence allows us to gauge the predictive...
The objectives of this paper are to demonstrate the use of a combination of artificial neural network and robust principal components (RPCs-ANN) to predict the soluble solid content of intact pineapple, non-invasively, based on near infrared spectral data, and to compare the performance of RPCs-ANN with artificial neural network based on classical principal components (PCs-ANN). First, we implemented...
Recently, sparse representation based classification [4] was successfully applied to face recognition. In SRC, the testing samples are represented as a sparse linear combination of the training samples and then classified according to the reconstruction error. The key of SRC is solving a Lasso problem. However, Lasso tends to select only one sample from a group of correlated training samples. Indeed,...
Distance relays are widely used for protection of transmission lines. Traditionally used electromechanical distance relays for protection of transmission lines are prone to effects of fault resistance. Each fault condition corresponds to a particular pattern. So use of a pattern recognizer can improve the relay performance. This paper presents a new approach, known as artificial neural network (ANN)...
Control systems for safety critical applications, including the ones relying on adaptive elements have to be certified against strict performance and safety requirements. This paper presents an approach for verifying worst-case tracking performance of neuro-adaptive systems in presence of bounded uncertainties. In this approach the boundedness of the tracking error vector is quantitatively investigated...
This paper presents a novel state and output feedback control law for the tracking control of a class of multi-input-multi-output (MIMO) continuous time nonlinear systems with unknown dynamics and disturbance input. First the state feedback based control law is designed which consists of the robust integral of a neural network (NN) output plus the sign of the tracking error signal multiplied with...
While the popularity of recommender systems is growing rapidly in e-commerce services, profile injection attacks are a great threat to their robustness and trustworthiness. Such attacks can be easily produced and inserted in recommender systems to alter the recommendation results. In such systems, attackers intentionally insert attack profiles to change the systems output to their advantage. This...
A dynamic neural network (DNN) based robust observer for second-order uncertain nonlinear systems is developed. The observer structure consists of a DNN to estimate the system dynamics on-line, a dynamic filter to estimate the unmeasurable state and a sliding mode feedback term to account for modeling errors and exogenous disturbances. The observed states are proven to asymptotically converge to the...
One of the most important issues of the digital watermarking is the watermark's robustness. That's why the error correcting codes (ECC) techniques were proposed. In this article, the performance of ECC in audio watermarking system is researched. The conclusion is that BCH encoders and turbo codes are the most important encoders. They have the best experimental robustness results against several audio...
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