Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniq...

This paper constructs fully parallel NN hardware realization of Artificial Neural Network (ANN) depends on the efficient execution of single neuron. Field Programmable Gate Array (FPGA) reconfigurable computing architecture is appropriated for hardware achievement of ANN. Numerous implementation of ANNs have been reported in scientific documents, trying to reduce Neural Networks NNs hardware ci...

The purpose of this study was to optimize the concentration of a fatty alcohol, in addition to internal phase, for formulating a stable O/W emulsion, by using artificial neural networks (ANNs). Predictions from ANNs are accurate and allow quantification of the relative importance of the inputs. Furthermore, by varying the network topology and parameters it was possible to obtain output values t...

?‘lie paper presents an artificial neural network(ANN) approach using ARTS(Adaptive Resonance Theory 2) to a diagnostic system for a Gas Insulated Switchgear(G1S). To begin with, we will show the background of abnormality diagnosis of GISs from the view point of predictive maintenance of them. Then, we will discuss the necessity of ART-t,ype ANNs, as an unsupervised learning method, in which ne...

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of the ANN paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problem...

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Multilayer perceptrons neural network and Radial basis function neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modeling. Meth...

Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in geotechnical engineering and p...

The IP+GMPLS over DWDM model has been considered a trend for the evolution of optical networks. However, a challenge that has been investigated in this model is how to achieve fast rerouting in case of DWDM failure. Artificial Neural Networks (ANNs) can be used to generate proactive intelligent agents, which are able to detect failure trends in optical network links early and to approximate opt...

The results presented are related to the prediction of the specific grinding force components. The main problems associated with the prediction capability of empirical models developed using the design of experiment (DOE) method are given. In this study an approach suggesting the combination of DOE method and artificial neural network (ANN) is developed. The inputs of the developed ANNs were se...