Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring makes it impractical in every location. This study’s primary objective was develop an artificial neural network (ANN) model for global horizontal (GHI) prediction using satellite data inputs. Three types ANN, namely, feed forward (FFNN), cascaded (CFNN), El...

This paper aims to determine suitable seasonal autoregressive integrated moving average (SARIMA) and feed-forward neural network (FFNN) models forecast the total non-coincidental monthly system peak demand in Philippines. To satisfy stationary requirement of SARIMA model, differencing, first-differencing were applied. The findings reveal that (0,1,1)(0,1,1)12 is appropriate model. All model par...

The static pull-in instability of beam-type micro-electromechanical systems is theoretically investigated. Two engineering cases including cantilever and double cantilever micro-beam are considered. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, c...

This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MB...

The static pull-in instability of beam-type micro-electromechanical systems is theoretically investigated. Two engineering cases including cantilever and double cantilever micro-beam are considered. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, c...

Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...

This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...