The issue of power system voltage stability, which is one the key issues in process planning and operation system, considered paper. It noted that neural networks have now been tested a number technological incidents form instability various countries. problem possible risk failure due to loss its stability energy during investigated. this regard, conducting analysis an important procedure orde...

Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of prediction, in this study, a hybrid model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, data were decomposed into...

The Chaotic Baseband Wireless Communication System (CBWCS) is expected to eliminate the Inter-Symbol Interference (ISI) caused by multipath propagation using optimal decoding threshold that sum of ISI past decoded bits and future transmitting bits. However, current technique only capable removing partial effects ISI, because are available for sub-optimal calculation. unavailability information ...

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...

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based ...

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.

Bionics plays an important role in the treatment of Epilepsy using memristors. Bionics is the branch of science in which we study nature and find solutions tohuman problems. Echo state networks (ESN) or reservoirs, are random, recurrent neural network topologies that integrate temporal data over short time windows by operating on the edge of chaos. Recently, there is a significant effort on the...

System identification of highly nonlinear dynamical systems, important for reducing time complexity in long simulations, is not trivial using more traditional methods such as recurrent neural networks (RNNs) trained with back-propagation through time. The recently introduced Reservoir Computing (RC) ∗ approach to training RNNs is a viable and powerful alternative which renders fast training and...

Recent studies have emphasized the potential of Enterprise Social Networks (ESN) for companies. However, so far, researchers have paid little attention to usages and affordances of ESN. To investigate this research issue, we anchor our analysis on Leonardi’s Affordances Theory. A case study has been conducted in a large French company, where we conducted thirty-five interviews with users of an ...