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!

A new proton transfer compound, formulated as (Hamp-6-pic)(Hpyzd) ∙H2O (1), has been synthesized from the reaction of pyrazine-2,3-dicarboxylic acid (H2pyzd)  and 2-amino-6-methyl pyridine (amp-6-pic), in 1:1 molar ratio. Extensive O−H×××O, N−H×××N and O−H×××O hydrogen bonds involving (Hamp-6-pic)+ cation, (Hpyzd)- anion and co-crystal water molecule٫ static electronic٫ and π…π stacking interac...

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...

The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient’s medical files cannot be revealed. The goal is to make an inference using the model, without jeopa...