Performance of four types of functionally different artificial neural network (ANN) models, namely Feed forward neural network, Elman type recurrent neural network, Input delay neural network and Radial basis function network and fourteen types of algorithms, namely Batch gradient descent (traingd), Batch gradient descent with momentum (traingdm), Adaptive learning rate (traingda), Adaptive lea...

The objective of this paper is to compare the performance of different Artificial Neural Network (ANN) training algorithms regarding the prediction of the hourly load demand of the next day in intercontinental Greek power system. These techniques are: (a) stochastic training process and (b) batch process with (i) constant learning rate, (ii) decreasing functions of learning rate and momentum te...

Article history: Received 20 February 2013 Received in revised form 5 August 2013 Accepted 16 September 2013 Available online 21 September 2013 This manuscript was handled by Andras Bardossy, Editor-in-Chief, with the assistance of Purna Chandra Nayak, Associate Editor

Comments Welcome 1 We are grateful to David Laitin, who collaborated with Kanchan Chandra on early versions of this chapter and has contributed to many of the insights here; to

The calibration of micro inertial measurement units is important in ensuring the precision of navigation systems, which are equipped with microelectromechanical system sensors that suffer from various errors. However, traditional calibration methods cannot meet the demand for fast field calibration. This paper presents a fast field calibration method based on the Powell algorithm. As the key po...