Error estimation of artificial neural network with varying inputs

In case of processing time series analysis, learning becomes complex and time consuming, hence training algorithms are required that are robust and accurate. The proposed paper compares performance in minimizing the error function using training method using a modified version of Rprop (Resilient Propagation) for regression analysis performed on three datasets from UCI repository-Forest Fire, Concrete Compressive Strength and Energy Efficiency dataset.