원문정보
WLAN 환경에서 효율적인 실내측위 결정을 위한 혼합 SVM/ANN 알고리즘
초록
영어
For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.
목차
1. 서론
2. SVM/ANN Hybrid Algorithm
2.1. Fingerprinting 방식
2.2. Support Vector Machine
2.3. 인공 신경망(ANN)
3. 실험 및 결과
3.1. 실험환경
3.2. 실험장비
3.3. 성능비교와 분석
4. 결론
참고문헌