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RTDGPS Implementation by Online Prediction of GPS Position Components Error Using GA-ANN Model | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقاله 6، دوره 1، شماره 1، فروردین 2013، صفحه 43-50 اصل مقاله (758.14 K) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2013.20 | ||
| نویسندگان | ||
| M. H. Refan* 1؛ A. Dameshghi2 | ||
| 1GPS Research Lab., Faculty of ECE, Shahid Rajaee Teacher Training University, Tehran, Iran | ||
| 2Electrical and Computer Engineering Faculty, Shahid Rajaee Teacher Training University, Tehran, Iran | ||
| تاریخ دریافت: 28 فروردین 1392، تاریخ بازنگری: 07 اردیبهشت 1392، تاریخ پذیرش: 24 اردیبهشت 1392 | ||
| چکیده | ||
| If both Reference Station (RS) and navigational device in Differential Global Positioning System (DGPS) receive signals from the same satellite, RS Position Components Error (RPCE) can be used to compensate for navigational device error. This research used hybrid method for RPCE prediction which was collected by a low-cost GPS receiver. It is a combination of Genetic Algorithm (GA) computing and Artificial Neural Network (ANN). GA was used for weight optimization and RS and Mobile Station (MS) were implemented by the software. The experimental results demonstrated which GA-ANN had great approximation ability and suitability in prediction; GA-ANNs prediction' RMS errors were less than 0.12 m. The simulation results with real data showed that position components' RMS errors in MS were less than 0.51 m after RPCE prediction. | ||
| کلیدواژهها | ||
| DGPS؛ ANN؛ RPCE Prediction؛ GA | ||
| مراجع | ||
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[1] K. Kawamura, and T. Tanaka, “Study on the Improvement of Measurement Accuracy in GPS,” 2006 SICE-ICASE International Joint Conf., pp. 1372-1375.
[2] A. Indriyatmoko, T. Kang, Y.J. Lee, G.I. Jee, Y.B. Cho and J. Kim, “Artificial Neural Networks for Predicting DGPS Carrier Phase and Pseudo-Range Correction,” Journal of GPS Solutions, vol. 12, no. 4, pp. 237-247, 2008.
[3] M. R. Mosavi and H. Nabavi, “Improving DGPS Accuracy using Neural Network Modelling,” Australian Journal of Basic and Applied Sciences, vol. 5, no. 5, pp. 848-856, 2011.
[4] M. R. Mosavi, “An Adaptive Correction Technique for DGPS using Recurrent Wavelet Neural Network,” 2007 IEEE Spectrum, pp. 3029-3033.
[5] M. H. Refan, K. Mohammadi and M.R. Mosavi, “Improvement on low cost positioning sensor accuracy,” 2003 IEEE conf. on sensors, Kuala Lumpur, Malaysia, pp. 9–14.
[6] M. R. Mosavi, “Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network, and Kalman Filter,” Journal of GPS Solutions, vol. 10, no. 2 ,pp.97-107, 2006.
[7] R. Duvigneau and M. Visonneau, “Hybrid Genetic Algorithms and Arti_cial Neural Networks for Complex Design Optimization in CFD,” International journal for numerical methods in fluids, vol. 44, no. 11, 2004.
[8] P. K. Enge, R. M Kalafus and M. F. Ruane, “Differential Operation of the Global Positioning System,” IEEE Communications Magazine, vol. 26, no. 7, pp. 48-60, July 1988.
[9] DJ. Jwo, CC. Lai, “Neural network-based GPS GDOP approximation and classification,” Journal of GPS Solutions, vol. 11, no. 1, pp. 51–60. 2007.
[10] Ch. T. Chiang, J. Sh. Hsu, and Ch. Y. Hsieh, “Improvement in DGPS Accuracy Using Recurrent S_CMAC_GBF,” World Academy of Science, Engineering and Technology, Vol. 55, pp. 422-427, July 2009.
[11] B.H.M. Sadeghi, “ABP-neural network predictor model for plastic injection molding process,” J. Mater. Process. Technol. Vol. 103, no. 3, pp.411–416, 2000.
[12] C.R. Chen, H.S. Ramaswamy, “Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and genetic algorithms,” J. Food Eng. Vol. 53, no. 3, pp. 209–220, 2002.
[13] T.T. Chow, G.Q. Zhang, Z. Lin, C.L. Song, “Global optimization of absorption chiller system by genetic algorithm and neural network,” Energy Build. Vol. 34, no. 1, pp. 103–109, 2002.
[14] D.F. Cook, C.T. Ragsdale, R.L. Major, “Combining a neural network with a genetic algorithm for process parameter optimization,” Eng. Appl. Artif. Intell. Vol. 13, no. 4. pp. 391– 396, 2000.
[15] L. Rigal and L. Truffet, “A new genetic algorithm specifically Referenced on mutation and selection,” Advances in Applied Probability, Vol 39, 141-161, 2007.
[16] K. Okyay, E Alpaydin. E. Xu, L. (Eds.) Springer, “Artificial Neural Networks and Neural Information,” Vol. 2714 ISBN 3540404082, 2003.
[17] M. NirmalaDevi, N. Mohankumar and M. Karthick, “Design of Genetically Evolved Artificial Neural Network Using Enhanced Genetic Algorithm,” International Journal of Recent Trends in Engineering, Vol. 1, no. 2, May 2009.
[18] S.L.B. Woll, D.J. Cooper, “Pattern-based closed-loop quality control for the injection molding process,” Polym. Eng. Sci. vol. 37, no. 5, pp. 801–812 1997.
[19] R. H. Christopher, A. J. Jeffery, G. K. Michael. The Intranet Architecture: A Genetic Algorithm for Function Optimization: A MATLAB Implementation. Available: www.ie.ncsu.edu/mirage
[20] S. Alla, A. Aulia, S. Kumar and R. Garg, “Using Hybrid GA-ANN to Predict Biological Activity of HIV Protease Inhibitors,” 2008 IEEE CIMCB.
[21] Ms. Dharmistha, D. Vishwakarma, “Genetic Algorithm based Weights Optimization of Artificial Neural Network,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 1, no. 3, August 2012.
[22] Sh. Changyu, W.Lixia, L. Qian, “Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method,” Journal of Materials Processing Technology 183, pp. 412–418, 2007. | ||
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