The difficulty in gas price forecasting has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting gas prices however, all of the existing models of prediction cannot meet practical needs. In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural n...

In the present study, the effective parameters of water-Al2O3 nanofluid flowing in flat tubes are investigated using the EFAST Sensitivity Analysis (SA) method. The SA is performed using GMDH type artificial neural networks (ANN) which are based on validated numerical data of two phase modeling of nanofluid flow in flat tubes. There are five design variables namely: tube flattening (H), flow ra...

Harmonic generation in power system networks presents significant issues that arise utilities. This paper describes a machine learning technique was used to conduct research study on the harmonic analysis of railway stations. The an investigation time series whose values represented total distortion (THD) for electric current. based information collected at station. In electrified substation, m...

Recently, a lot of attention has been devoted to advanced techniques of system modeling. PNN(polynomial neural network) is a GMDH-type algorithm (Group Method of Data Handling) which is one of the useful method for modeling nonlinear systems but PNN performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error. In this paper, w...

Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the market and efficient resource planning. This article dedicated two models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach time series forecasting, using measured historical data, (2) ANN (Artificial Neural Network) machine learning techniques. The m...

The ultimate axial bearing capacity (UABC) of a single pile is an important parameter in design. BP neural network (BPNN) has strong nonlinear mapping ability and can effectively predict the UABC pile. However, frequent immersion unstable search results with local vibration leads BPNN to less usable solution. weights biases model are optimized using improved radial movement optimization (IRMO) ...

Modeling and multi-objective optimization of centrifugal pumps is performed at three steps. At the first step, η and NPSHr in a set of centrifugal pump are numerically investigated using commercial software NUMECA. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks are obtained, at the second step, for modeling of η and NPSHr with respect to geometrica...

In the case of substantial noise, i.e., for inaccurate and incomplete data, the use of the Group Method of Data Handling (GMDH) algorithm leads to sharp and rather deep minimums of dependency of external criterion of accuracy measured on testing sample on the complexity of model structure. This minimum indicates the optimal model. In practice, however, if the noise is just noticeable, i.e., if ...

An Artificial Neural Network is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. Such Neural Networks have been characterized by passive neurons that are not able to select and estimate their own inputs. In a new approach, which corresponds in a better way to the actions of human nervous system, the connections...