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...

Virtual machines (VMs) dynamic consolidation is effective to improve the utilization of resources and energy efficiency in cloud environment. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy performance trade-off, as aggressive consolidation may lead to performance degradation. Current solutions to the problem of host lo...

in this work, a hybrid gmdh–neural network model was developed in order to predict the moisture content of papaya slices during hot air drying in a cabinet dryer. for this purpose, parameters including drying time, slices thickness and drying temperature were considered as the inputs and the amount of moisture ratio (mr) was estimated as the output. exactly 50% of the data points were used for ...

Proper performance of water distribution networks (WDNs) plays a vital role in customer satisfaction. The aim this study is to conduct sensitivity analysis evaluate the behavior WDNs analyzed by pressure-driven (PDA) approach and classification technique using an appropriate artificial neural network, namely Group Method Data Handling (GMDH). For purpose, divided into four distinct steps. In fi...

Item ranking and selection plays a key role in constructing concise and informative educational tests. Traditional techniques based on the item response theory (IRT) have been used to automate this task, but they require model parameters to be determined a priori of each item and their application becomes more tedious with larger item banks. Machine learning techniques can be used to build data...

In this paper, the forecasting problem of share prices at New York Stock Exchange (NYSE) was considered and investigated. For its solution alternative methods computational intelligence were suggested investigated: LSTM networks, GRU, simple recurrent neural networks (RNN) Group Method Data Handling (GMDH). The experimental investigations intelligent for CISCO carried out efficiency estimated c...