Abstract—This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identificati...

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political instability and lack of mechanism for enforcement of contracts that can affe...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

‎linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints‎. ‎in this paper‎, ‎to solve this problem‎, ‎we combine a discretization method and a neural network method‎. ‎by a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem‎. ‎then‎, ‎we use...

In our work, we compared two approaches for predicting changes in the concentration of one main greenhouse gases - methane. The study is based on surface methane data obtained by monitoring dynamics major Arctic Island Belyy, Russia. We used a nonlinear autoregressive neural network with an external input (NARX), and vector regression model. An artificial type NARX was more accurate changes.

The aim of this study was to assess the aptitude recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions process dynamics in vertical-gradient-freeze growth gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time temperatures solid–liquid interface position GaAs are crucial control applications visualization, ...

In recent years, solar radiation forecasting has become highly important worldwide as energy increases its contribution to electricity grids. However, due the intermittent nature of caused by meteorological parameters, errors arise, and fluctuations in power output photovoltaic (PV) systems a severe issue. This paper aims introduce hybrid model daily global time series. Meteorological data samp...

Intelligent assessment of information gathered from industrial-grade data loggers for preemptive maintenance is one of the foremost areas of research in conditional monitoring. Due to the general operating environment, there exists a non-linear relationship between the input and output data gathered from these sensors. Moreover, the transmission of data from such dynamic environments is general...