in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

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...

This paper aims to enhance the performance of a cascade-forward neural network (CFNN) model predict output power photovoltaic (PV) module. improvement is conducted by optimizing number hidden neurons using genetic algorithm (GA). The optimization carried out minimize value root mean square error (RMSE) between actual and predicted PV power. CFNN-based GA evaluated five statistical term terms; n...

Feed forward neural networks (FFNN) with an unconstrained random number of hidden neurons deene exible non-parametric regression models. In M uller and Rios Insua (1998) we have argued that variable architecture models with random size hidden layer signiicantly reduce posterior mul-timodality typical for posterior distributions in neural network models. In this chapter we review the model propo...

Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between...

EEG analysis aims to help scientists better understand the brain, physicians diagnose and treatment choices of brain-computer interface. Artificial neural networks are among most effective learning algorithms perform computing tasks similar biological neurons in human brain. In some problems, network model's performance might significantly degrade overfit due irrelevant features that negatively...

This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...

In an islanded microgrid, while considering the complex nature of line impedance, the generalized droop control fails to share the actual real/reactive power between the distributed generation (DG) units. To overcome this power sharing issue, in this paper a new approach based on feed forward neural network (FFNN) is proposed. Also, the proposed FFNN based droop control method simultaneously co...