In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

this paper investigates the islands formation in initial stages of restoration process in blackout condition and studies the impacts of islands number variation on the process execution. in addition, a method is proposed to determining each island optimal boundaries. then the effects of transmission lines thyistor controlled series compensators (tcsc) to facilitate restoration process and impro...

important parameters on apple drying process are investigated experimentally and modeled employing artificial neural network and neuro-taguchi's method. experimental results show that the apple drying curve stands in the falling rate period of drying. temperature is the most important parameter that has a more pronounced effect on drying rate than the other two parameters i.e. air velocity and ...

Prediction of traffic is very crucial for its management. Because of human involvement in the generation of this phenomenon, traffic signal is normally accompanied by noise and high levels of non-stationarity. Therefore, traffic signal prediction as one of the important subjects of study has attracted researchers’ interests. In this study, a combinatorial approach is proposed for traffic signal...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...