-The Generalised Mapping Regressor (GMR) is an incremental self-organizing neural network with adaptive chains (linking) among neurons. These chains yield supplementary information to the network. GMR is capable to approximate every function or relation (general mapping) and, simultaneously, its inverse function, if it exists, or the inverse relation. It also outputs all the solutions (even inf...

This article studies the determination of the rate of convergence to the unit of each of three newly introduced here multivariate fuzzy perturbed normalized neural network operators of one hidden layer. These are given through the multivariate fuzzy modulus of continuity of the involved multivariate fuzzy number valued function or its high order fuzzy partial derivatives and that appears in the...

Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks. In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a “smaller” network architecture that “approximates” the operation of the target network?...

The new idea of this research is application of a new fault detection and isolation (FDI) technique for supervision of sensor networks in transportation system. In measurement systems, it is necessary to detect all types of faults and failures, based on predefined algorithm. Last improvements in artificial neural network studies (ANN) led to using them for some FDI purposes. In this paper, appl...

We combine two notable streams of neural networks research: Adaptive Resonance Theory (ART) and Growing Neural Gas (GNG) networks. In particular we modify the AppART neural network formulation by introducing GNG based training features. The resulting neural network outperforms its original version as well as other neural models while maintaining the functional approximation properties and hybri...

In an attempt to enhance the neural network technique so that it can evolve from a ‘‘black box” tool into a semi-analytical one, we propose a novel modeling approach of imposing ‘‘generalized constraints” on a standard neural network. We redefine approximation problems by use of a new formalization with the aim of embedding prior knowledge explicitly into the model to the maximum extent. A gene...

In this paper, an analytic neural network model is introduced for the modeling of the wind turbine behavior. The proposed hybrid method is the combination of the analytic and neural network models. The neural network model is used as a compensator to improve the approximation of the analytic model. It is guaranteed that the error of the analytic neural network model is smaller than the error of...

information on nitrate in groundwater resources requires periodic measurements are accurate. despite the measure in some areas due to sensitive social and health community are not reported. therefore, be informed of the status of each area of water quality, modeling is essential. the purpose of this study was the application of artificial neural network method for estimating nitrate and compare...

in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.