Highly Accurate Multi-layer Perceptron Neural Network for Air Data System

  • H. S. Krishna Aeronautical Development Agency, Bangalore
DOI: https://doi.org/10.14429/dsj.59.1574
Keywords: Back propagation, calibration, curve-fitting, error, inner product, logistic function, neuron, perceptron, pressure probe, training network, synaptic weights

Abstract

The error backpropagation multi-layer perceptron algorithm is revisited. This algorithm is used to train and validate two models of three-layer neural networks that can be used to calibrate a 5-hole pressure probe. This paper addresses Occam's Razor problem as it describes the adhoc training methodology applied to improve accuracy and sensitivity. The trained outputs from 5-4-3 feed-forward network architecture with jump connection are comparable to second decimal digit (~0.05) accuracy, hitherto unreported in literature.

Defence Science Journal, 2009, 59(6), pp.670-674, DOI:http://dx.doi.org/10.14429/dsj.59.1574

Published
2009-11-01
How to Cite
Krishna, H. (2009). Highly Accurate Multi-layer Perceptron Neural Network for Air Data System. Defence Science Journal, 59(6), 670-674. https://doi.org/10.14429/dsj.59.1574
Issue
Vol 59 No 6: Advances in Microelectromechanical Systems
Section
Short Communication
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