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...

        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...

In, th,is paper, a gen,etic fu,zzy n,eu,ro,l n,etwork f o r pattern, recognition, is proposed by applyin,g genwtic algorith,ms to th,e Kuian,-Cai f v z z y n,eural network. A genekic-guided self-organizinq learn,in,g a1gorith.m i s CO,pable of redncinuj th,e nmm'ber of f m z y neurons an,d in,creasinq recognxikion rates f o r th,e fixed n,um,ber of outpu,t neurons. Th.e simulataon,s h,ave indic...

This paper presents a soft computing based bank performance prediction system. It is an ensemble system whose constituent models are a multilayered feed forward neural network trained with backpropagation (MLFF-BP), a probabilistic neural network (PNN) and a radial basis function neural network (RBFN), support vector machine (SVM), classification and regression trees (CART) and a fuzzy rule bas...

This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all referen...

Prediction of highly non linear behavior of suspended sediment flow in rivers has prime importance in the field of water resources engineering. In this study the predictive performance of two Artificial Neural Networks (ANNs) namely, the Radial Basis Function (RBF) Network and the Multi Layer Feed Forward (MLFF) Network have been compared. Time series data of daily suspended sediment discharge ...

“Happy New Year!” As you open this January 2021 issue of IEEE T ransactions on N eural etworks and L earning S ystems (IEEE TNNLS), I hope everyone enjoyed a healthy happy holiday season!

Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and pre...

In the present research era, high accuracy methods as a statistical analysis tool are increasing. Therefore, researchers more focused to produce reliable and accurate results. Hence, use of data modeling techniques is meet needs current trend. On other hand, Design Experiment (DOE) extensively used among various scientific fields; however, its limitations do not allow these study designs for pu...

BACKGROUND Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. METHODS Two hybrid models, one composed of...