This paper presents the application of Hilbert transform and artificial neural network (ANN) for power quality (PQ) disturbance classification. The input features of the ANN are extracted from the envelope of the disturbance signals by applying Hilbert transform (HT). The features obtained from the Hilbert transform are distinct, understandable and immune to noise. These features after normaliz...

Many researchers and organizations are interested in creating a mechanism capable of automatically predicting software defects. In the last years, machine learning techniques have been used in several researches with this goal. Many recent researches use data originated from NASA (National Aeronautics and Space Administration) IV&V (Independent Verification & Validation) Facility Metrics Data P...

In this paper we propose a new method for image retrieval with relevance feedback based on eliciting preferences from the decision-maker acquiring visual information from an image database. The proposed extension of the common approach to image retrieval with relevance feedback allows it to be applied to objects with non-homogenous colour and texture. This has been accomplished by the algorithm...

We propose a genetic algorithm for generating adversarial examples for machine learning models. Such approach is able to find adversarial examples without the access to model’s parameters. Different models are tested, including both deep and shallow neural networks architectures. We show that RBF networks and SVMs with Gaussian kernels tend to be rather robust and not prone to misclassification...

It is very important to evaluate water quality in environment protection. Water environment is a complicated system, traditional methods cannot meet the demands of water environment protection. In view of the deficiency of the traditional methods, a BP neural network model and a RBF neural network model are proposed to evaluate water quality. The proposed model was applied to evaluate the water...

Wind speed forecast is essential in wind energy conversion system and may fail to operate power plant at non optimal region if not properly forecasted. This paper focuses the short term wind speed forecasting using conventional statistical method and artificial neural networks such as back propagation network (BPN), generalized regression neural network (GRNN) and radial basis function networks...

Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. An approach to this problem uses time series forecasting by neural networks. However, time series forecasting is a difficult problem, thus, the use of this technique for time series novelty detection is sometimes criticized. Alternativel...

In this paper the popular PD controller of robot manipulator is modified. RBF neural networks are used to compensate the gravity and fi-iction. No exact knowledge of the robot dynamics is required. The euggeated learning law of neuro compensator is similar to the well-known backpropagation algorithm but wit h addit ional robust terms. Lyapuuov-liie analysis is used to derive the stability of le...