Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based o...

The paper describes the application of a combined neuro-fuzzy model for indoor temperature dynamic and automatic regulation. The neural module of the model, an auto-regressive neural network with external inputs (NNARX), produces indoor temperature forecasts that are used to feed a fuzzy logic control unit that simulates switching the heating, ventilation and air conditioning (HVAC) system on a...

An NNARX system is proposed for modelling the internal greenhouse temperature as a function of outside air temperature and humidity, global solar radiation and sky cloudiness. The models show a good performance over a complete year using only two training periods, 1 week in winter and one in September. Finding the balance between the number of neurons in the hidden layer of the NNARX system and...

evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. this paper explores evaporation estimation methods based o...

Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based o...

In this paper, an artificial intelligent tool is proposed using fuzzy logic (FL) and recurrent neural networks (RNN) for definition and forecast of patient’s clinical condition. The fuzzy logic-based proposed first phase of the tool permits the analysis of the current state of the patient, which allows the training of the artificial neural network. In the second phase, two Elman networks Multi ...

In this paper, the hydro power plant model (with penstock-wall elasticity and compressible water column effect) is simulated at random load disturbance variation with output as turbine speed for random gate position as input. The multilayer perceptron neural network (i.e. NNARX) and fused neural network and fuzzy inference system (i.e. ANFIS) for identification of turbine speed as output variab...

This paper studies the neural network nonlinear autoregressive with exogenous signal (NNARX) model identification of elastic and inelastic hydro power plant. A nonlinear relationship between the turbine deviated power and random gate position on random load variation and water disturbance is assessed. The identified elastic NNARX hydro plant model is simulated with predictive controller to trac...

The paper deals with the PAM manipulator modeling and identification based on autoregressive recurrent neural networks. For the first time, the most powerful types of neural-network-based nonlinear autoregressive models, namely, NNARMAX, NNOE and NNARX models, will be applied comparatively to the PAM manipulator identification. Furthermore, the evaluation of different nonlinear neural network a...

Context: This work presents different models based on artificial neural networks, among them NNARX, for estimating global solar radiation from UV index measurements. The objective is to determine the efficiency of studied estimate in terms coefficient determination (R2), root-mean-square error (RMSE), and mean absolute (MAE). Methodology: It divided into four stages: i) conformation training da...