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

被引：1
|
作者：
Krishna, H. S. [1 ]
机构：
[1] Aeronaut Dev Agcy, Bangalore 560017, Karnataka, India
来源：
DEFENCE SCIENCE JOURNAL | 2009年 / 59卷 / 06期
关键词：
Back propagation; calibration; curve-fitting; error; inner product; logistic function; neuron; perceptron; pressure probe; training network; synaptic weights;
D O I：
10.14429/dsj.59.1574
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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 (similar to 0.05) accuracy, hitherto unreported in literature.
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页码：670 / 674
页数：5
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