Erdem Turker Senalp

An attempt to model the influence of the trough on HF communication by using neural networks

Trough is an interesting phenomenon in characterizing the behavior of the ionosphere, especially during disturbed conditions. The subject, which was introduced around the 1970s, is still attracting attention, especially during recent years. In HF communication, in particular, over the midlatitude ionospheric regions the electron density trough exhibits a phenomenon of abrupt gradients of electron densities in space and time which are directly reflected to foF2. Thus the performances of HF communications are directly affected. In this work an attempt has been made for the modeling to quantify the influence of the ionospheric midlatitude electron density trough on the ionospheric critical frequency foF2 by using neural networks. Data sets are used from the ground stations that include observations in the trough region. It has been demonstrated that the neural-net based approaches are promising in modeling of the ionospheric processes. Data generated by using statistical relationships are used to train the neural network. Then the trained neural network is used to forecast the ionospheric critical frequency, foF2, values 1 hour in advance for the cases when the probability of influence of the trough is high. Preliminary results will be presented to discuss the suitability of the neural-network-based approach in the modeling of complex processes such as the influence of the trough on foF2. The basic contributions of this work are 1) generation and organization of significant data for teaching complex processes, 2) neural-network-based modeling of a highly complex nonlinear process such as the influence of the trough on foF2 forecasting, and 3) general demonstration of learning capability by calculating cross correlations and general demonstration of reaching a proper operating point by calculating errors (that is, during the optimization process the neural network reaches the global minimum by using the gradient descent method).

Neural networks and cascade modeling technique in system identification

The use of the Middle East Technical University Neural Network and Cascade Modeling (METU-NN-C) technique in system identification to forecast complex nonlinear processes has been examined. Special cascade models based on Hammerstein system modeling have been developed. The total electron content (TEC) data evaluated from GPS measurements are vital in telecommunications and satellite navigation systems. Using the model, forecast of the TEC data in 10 minute intervals 1 hour ahead, during disturbed conditions have been made. In performance analysis an operation has been performed on a new validation data set by producing the forecast values. Forecast of GPS-TEC values have been achieved with high sensitivity and accuracy before, during and after the disturbed conditions. The performance results of the cascade modeling of the near Earth space process have been discussed in terms of system identification.

Two possible approaches for ionospheric forecasting to be employed along with the IRI model

Ionospheric forecasting is a popular research area required by telecommunication and navigation system planners and operators. The problem is challenging because ionospheric processes are nonlinear. Data-driven techniques are of particular interest since they overcome most of these difficulties. In this work, two possible ionospheric forecasting approaches have been considered to be employed along with the IRI model. The authors reported these approaches previously. Ionospheric critical frequency values have been forecast using Fuzzy inference and Neural Networks considering the two possible approaches, METU-FNN and METU-NN. In parallel, the foF2 values have been calculated based on the IRI model.