rainfall-runoff relationship is very important in many fields of hydrology such as water supply and water resourcemanagement and there are many models in this field. among these models, the artificial neural network (ann) wasfound suitable for processing rainfall-runoff and opened various approaches in hydrological modeling. in addition,anns are quick and flexible approaches which provide very ...

To predict the variability of dam water levels, parametric Multivariate Linear Regression (MLR), stochastic Vector AutoRegressive (VAR), Random Forest (RFR) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) models were compared based on influences climate factors (rainfall temperature), indices (DSLP, Aridity Index (AI), SOI Niño 3.4) land-use land-cover (LULC) as predictor variab...

Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft fraud. NTLs can be characterized as outliers historical data series. The use computational tools to identify is subject research around world, this context, artificial neural networks (ANN) applicable. ANNs machine learning models that learn through experience, their performance associated ...

The aim of the current research was to develop models predict severity accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 2020, machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function (RBFNN) models, as well statistical methods, Kolmogorov–Smirnov test, Friedman fac...

This work analyzes the advantages of Artificial Neural Networks applied to sensorless control for Switched Reluctance Motors. Due to the non-linear electrical characteristics of Switched Reluctance Motors, Artificial Neural Networks perform a good tool and for torque ripple minimization in Switched Reluctance Motors. The simulation results to prove the efficiency of a multilayer perceptron in o...

In this contribution is investigated the use of multilayer perceptron type neural networks in the characterization of images by texture content. The paper is focused on the effects of textural feature extraction methods on the network architecture, training performance and generalization capability when applied in indexing of images contained within multimedia image databases. An in depth exper...

This paper presents a method of recognition of isolated offline handwritten Devanagari numerals using wavelets and neural network classifier. This method of optical character recognition takes the handwritten numeral image as input. After pre-processing, it is subjected to single level wavelet decomposition using Daubechies-4 wavelet filter. This wavelet decomposition allows viewing the input n...

An implementation of an optical perceptron with a soft optical threshold trained with an adapted BP algorithm is described as a precursor to an optical multilayer perceptron (MLP). It has 64 inputs and ten outputs. The soft threshold is implemented by a liquid crystal light valve. Experimental results on perceptron recall are also reported. The eeect of a modiied grey-scale to weight mapping fo...