Abstract The study aims at forecasting the return volatility of cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method data handling (GMDH-NN) algorithm. used in this is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict four cryptocurrencies, namely Bitcoin, E...

With the increasing level of intelligence marine engines, there is an demand for online monitoring and NOx emissions have been great concern. In this paper, a simulation model developed based on virtual measurement technology, which can calculate predict current operating state parameters low-speed two-stroke diesel engines. First, calibrated 3D used to design experiments obtain experimental sa...

During an earthquake, significant damage can result due to instability of the soil in the area affected by internal seismic waves. A liquefaction-induced lateral ground displacement has been a very damaging type of ground failure during past strong earthquakes. In this study, neuro-fuzzy group method of data handling (NF-GMDH) is utilized for assessment of lateral displacement in both ground sl...

This paper proposes a new passive robust fault detection scheme using non-linear models that include parameter uncertainty. The nonlinear model considered here is described by a group method of data handling (GMDH) neural network. The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed by checking if the measured behaviour is inside t...

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on hydrological variable, which reliable. It essential flood risk evaluation project, hydropower development developing efficient water resource management. Presently, approach of Group Method Data Handling (GMDH) has been widely applied in modelling sector. Yet, comparatively, same tool not vas...

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the “conventional” SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials...