an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. thisstudy made us of adaptive neuro-fuzzy ...

quantitative prediction of municipal solid waste generation has an important role in the optimization and programming of municipal solid waste management system. but, this concept was companied with many problems, because of the non homogenous nature and the effect of various factors out of the control on solid waste generation. in this study, the combination of artificial neural network and wa...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...

the purpose of this research is to detect manipulation of stock prices in tehran stock exchange that it has been done through hybrid genetic algorithm-artificial neural network (ann-ga) model and the simplified quadratic discriminant function (sqdf) model. in this study, the variables of price, trading volume and free float stock to match the results of the model and the actual data of price ma...

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...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...

drought forecasting in khash city by using neural network model hossein negaresh associate professor of geography and environmental planningfaculty, university of sistan & baluchestan mohsen armesh holding master degree in climatology in environmental planning extended abstract 1- introduction drought is condition of lack of rainfall and increase in temperature occurring in any climatic condit...