this paper presents a hybrid approach to developing a short-term traffic flow prediction model. in thisapproach a primary model is synthesized based on neural networks and then the model structure is optimized throughgenetic algorithm. the proposed approach is applied to a rural highway, ghazvin-rasht road in iran. the obtainedresults are acceptable and indicate that the proposed approach can i...

Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descripti...

through the last three decades different seismological and engineering approaches for the generation of artificial earthquakes have been proposed. selection of an appropriate method for the generation of applicable artificial earthquake accelerograms (aeas) has been a challenging subject in the time history analysis of the structures in the case of the absence of sufficient recorded accelerogra...

in this study, we focused on the gait of parkinson’s disease (pd) and presented a gray box model for it. we tried to present a model for basal ganglia structure in order to generate stride time interval signal in model output for healthy and pd states. because of feedback role of dopamine neurotransmitter in basal ganglia, this part is modelled by “elman network”, which is a neural network stru...

today, with the advanced statistical techniques and neural networks, predictive models of distribution have been rapidly developed in ecology. purpose of this research is to predict and map the distribution of tetranychus urticae koch (acari: tetranychidae) using mlp neural networks combined with genetic algorithm in surface of farm. population data of pest was obtained in 2016 by sampling in 1...