Abstract Accurate network traffic classification is an essential and challenging issue for wireless management survivability. Existing algorithms, on the other hand, cannot meet required specifications of real networks' in terms user privacy control overhead, latency, above all, speed. For classification, machine learning‐based hybrid optimization techniques have been deployed. This paper takes...

Evapotranspiration (ET) is a significant aspect of the hydrologic cycle, notably in irrigated agriculture. Direct approaches for estimating reference evapotranspiration (ET0) are either difficult or need large number inputs that not always available from meteorological stations. Over 6-year period (2006–2011), this study compares Feed Forward Neural Network (FFNN), Radial Basis Function (RBFNN)...

The quest for an intelligence compliance system to solve power stability problems in real-time with high predictive accuracy, and efficiency has led the discovery of deep learning (DL) techniques. This paper investigates potency several artificial neural network (ANN) techniques assessing steady-state a system. new voltage pointer (NVSP) was employed parameterize reduce input data algorithms pr...

The present paper estimates for the first time State of Charge (SoC) a high capacity grid-scale lithium-ion battery storage system used to improve power profile in distribution network. proposed long short-term memory (LSTM) neural network model can overcome problems associated with nonlinear and adapt complexity uncertainty estimation process. accuracy developed was compared results obtained f...

artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

Agricultural sector area plays major role in Indian economy. This paper shows research comparison in between MLP Feed Forward Neural Network, Generalized Regression Neural Network and Radial-Basis Function Neural Network in the field of Wheat yield prediction using Z-score Normalization method. The outcome represents that GRNN present better prediction results as compared to FFNN and RBNN. Eigh...

Unlike feedforward neural networks (FFNN) which can act as universal function approximaters, recursive neural networks have the potential to act as both universal function approximaters and universal system approximaters. In this paper, a globally recursive neural network least mean square (GRNNLMS) gradient descent or a real time recursive backpropagation (RTRBP) algorithm is developed for a s...

A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions," Received Unlike feedforward neural networks (FFNN) which can act as universal function ap-proximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTR...

In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT2016 evaluation campaign are described. This evaluation campaign focuses on transcribing spontaneous speech from Skype recordings. State-of-the-art bidirectional long shortterm memory (LSTM) and deep, multilingually boosted feed-forward neural network (FFNN) acoustic models are trained a...