Optimization of ANN input parameters used in electric field level prediction model

This paper presents optimization of existing ANN (Artificial Neural Network) electric field level prediction model in a microcellular environment. Optimization is achieved using feedforward neural networks. The analysis showed that choosing optimal input parameters gives a better quality prediction than when considering all input parameters used by existing model. Optimization has been achieved in terms of increasing the accuracy of prediction and reducing the complexity of the model itself.