Outlier detection for geodetic nets using ADALINE learning algorithm

Developed by imitating the operation of human brain, artificial neural network applications are used in many fields such as engineering, industry, medicine, agriculture, finance, communication, meteorology, space and aeronautics. By the help of sophisticated computing technologies, the learning algorithms used in artificial neural networks allowed solving many problems that remained as undecided and defied any mathematical expression, particularly in the fields of engineering. In geodetic studies, threedimensional geodetic networks are used for all sorts of location-based engineering measurements on earth. Numerous measurements are performed to determine the position of the points in geodetic networks. Possible errors and inconsistencies in these measurements affect geodetic network precision. Therefore, the test for outliers is implemented to eliminate measurement errors and sort out outliers. In the present study, the test for outliers was performed on a computer program developed by using ADALINE learning algorithm and the results were compared with traditional methods (data snooping, Tau, t). This new method was observed to be superior to traditional methods with regards to calculations about outliers and decision-making on the results.

[1]  Michael T. Manry,et al.  LMS learning algorithms: misconceptions and new results on converence , 2000, IEEE Trans. Neural Networks Learn. Syst..

[2]  H. Metin Ertunç,et al.  Using adaline neural network for performance improvement of smart antennas in TDD wireless communications , 2005, IEEE Transactions on Neural Networks.

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[4]  S. Hekimoglu,et al.  Effect of heteroscedasticity and heterogeneousness on outlier detection for geodetic networks , 2007 .

[5]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[6]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[7]  Frank Hampel,et al.  Robust statistics: a brief introduction and overview , 2001 .

[8]  W. Kosek,et al.  El Niño Impact on Polar Motion Prediction Errors , 2001 .

[9]  Vladimír Čermák,et al.  Prediction of Surface Air Temperatures by Neural Network, Example Based on Three-Year Temperature Monitoring at Spořilov Station , 2003 .

[10]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[11]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[12]  Karl-Rudolf Koch,et al.  Parameter estimation and hypothesis testing in linear models , 1988 .

[13]  Vladimír Čermák,et al.  Neural Network Prediction of Monthly Precipitation: Application to Summer Flood Occurrence in Two Regions of Central Europe , 2001 .

[14]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[15]  Charles R. Schwarz,et al.  Blunder Detection and Data Snooping in LS and Robust Adjustments , 1993 .

[16]  W. Baarda,et al.  A testing procedure for use in geodetic networks. , 1968 .

[17]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[18]  E. J. Krakiwsky,et al.  Reliability Analysis of Phase Observations in GPS Baseline Estimation , 1990 .