The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 LVQ(Learning Vector Quantization) 모델과 그 변형을 자연적 지진 사건(지진)과 인공 지진(핵폭발)을 구별하기 위한 클러스터링 도구로 간주합니다. 이 연구는 단기간 텔레지진 기록을 통해 계산된 P파 스펙트럼의 26가지 스펙트럼 특징을 기반으로 합니다. Kohenen이 제안한 기존 LVQ와 Sakuraba 및 Bezdek이 제안한 Fuzzy LVQ(FLVQ) 모델은 모두 다음을 사용하여 24번의 지진과 XNUMX번의 핵폭발 세트에서 테스트되었습니다. 일대일 휴가 테스트 전략. 1차 실험 결과는 클러스터의 모양, 수 및 중첩이 지진 분류에 중요한 역할을 한다는 것을 보여주었습니다. 결과는 또한 부적절한 기능 공간 분할이 클러스터링 및 인식 단계를 모두 크게 약화시키는 방법을 보여주었습니다. 수치 결과를 개선하기 위해 본 논문에서는 새로운 결합 FLVQ 알고리즘을 사용했습니다. 알고리즘은 두 개의 중첩된 하위 알고리즘으로 구성됩니다. 내부 하위 알고리즘은 특징 공간의 퍼지 참조 벡터를 사용하여 잘 정의된 퍼지 분할을 생성하려고 시도합니다. 이 목표를 달성하기 위해 비용 함수는 퍼지 참조 벡터의 수, 모양 및 중첩의 함수로 정의됩니다. 업데이트 규칙은 단계적 학습 알고리즘에서 이 비용 함수를 최소화하려고 시도합니다. 반면, 외부 하위 알고리즘은 각 단계에서 클러스터 수에 대한 최적 값을 찾으려고 합니다. 외부 루프의 최적화를 위해 두 가지 다른 기준을 사용했습니다. 첫 번째 기준에서는 새로 정의된 "퍼지 엔트로피"를 사용하고 두 번째 기준에서는 학습률에 대한 Huntsberger 공식을 일반화하여 성능 지수를 사용합니다. 퍼지 거리. 새로운 모델의 실험 결과는 오류율, 허용 가능한 수렴 시간 및 경계 의사 결정의 유연성이 향상되었음을 보여줍니다.
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부
Payam NASSERY, Karim FAEZ, "Seismic Events Discrimination Using a New FLVQ Clustering Model" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 7, pp. 1533-1539, July 2000, doi: .
Abstract: In this paper, the LVQ (Learning Vector Quantization) model and its variants are regarded as the clustering tools to discriminate the natural seismic events (earthquakes) from the artificial ones (nuclear explosions). The study is based on the six spectral features of the P-wave spectra computed from the short period teleseismic recordings. The conventional LVQ proposed by Kohenen and also the Fuzzy LVQ (FLVQ) models proposed by Sakuraba and Bezdek are all tested on a set of 26 earthquakes and 24 nuclear explosions using the leave-one-out testing strategy. The primary experimental results have shown that the shapes, the number and also the overlaps of the clusters play an important role in seismic classification. The results also showed how an improper feature space partitioning would strongly weaken both the clustering and recognition phases. To improve the numerical results, a new combined FLVQ algorithm is employed in this paper. The algorithm is composed of two nested sub-algorithms. The inner sub-algorithm tries to generate a well-defined fuzzy partitioning with the fuzzy reference vectors in the feature space. To achieve this goal, a cost function is defined as a function of the number, the shapes and also the overlaps of the fuzzy reference vectors. The update rule tries to minimize this cost function in a stepwise learning algorithm. On the other hand, the outer sub-algorithm tries to find an optimum value for the number of the clusters, in each step. For this optimization in the outer loop, we have used two different criteria. In the first criterion, the newly defined "fuzzy entropy" is used while in the second criterion, a performance index is employed by generalizing the Huntsberger formula for the learning rate, using the concept of fuzzy distance. The experimental results of the new model show a promising improvement in the error rate, an acceptable convergence time, and also more flexibility in boundary decision making.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_7_1533/_p
부
@ARTICLE{e83-d_7_1533,
author={Payam NASSERY, Karim FAEZ, },
journal={IEICE TRANSACTIONS on Information},
title={Seismic Events Discrimination Using a New FLVQ Clustering Model},
year={2000},
volume={E83-D},
number={7},
pages={1533-1539},
abstract={In this paper, the LVQ (Learning Vector Quantization) model and its variants are regarded as the clustering tools to discriminate the natural seismic events (earthquakes) from the artificial ones (nuclear explosions). The study is based on the six spectral features of the P-wave spectra computed from the short period teleseismic recordings. The conventional LVQ proposed by Kohenen and also the Fuzzy LVQ (FLVQ) models proposed by Sakuraba and Bezdek are all tested on a set of 26 earthquakes and 24 nuclear explosions using the leave-one-out testing strategy. The primary experimental results have shown that the shapes, the number and also the overlaps of the clusters play an important role in seismic classification. The results also showed how an improper feature space partitioning would strongly weaken both the clustering and recognition phases. To improve the numerical results, a new combined FLVQ algorithm is employed in this paper. The algorithm is composed of two nested sub-algorithms. The inner sub-algorithm tries to generate a well-defined fuzzy partitioning with the fuzzy reference vectors in the feature space. To achieve this goal, a cost function is defined as a function of the number, the shapes and also the overlaps of the fuzzy reference vectors. The update rule tries to minimize this cost function in a stepwise learning algorithm. On the other hand, the outer sub-algorithm tries to find an optimum value for the number of the clusters, in each step. For this optimization in the outer loop, we have used two different criteria. In the first criterion, the newly defined "fuzzy entropy" is used while in the second criterion, a performance index is employed by generalizing the Huntsberger formula for the learning rate, using the concept of fuzzy distance. The experimental results of the new model show a promising improvement in the error rate, an acceptable convergence time, and also more flexibility in boundary decision making.},
keywords={},
doi={},
ISSN={},
month={July},}
부
TY - JOUR
TI - Seismic Events Discrimination Using a New FLVQ Clustering Model
T2 - IEICE TRANSACTIONS on Information
SP - 1533
EP - 1539
AU - Payam NASSERY
AU - Karim FAEZ
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2000
AB - In this paper, the LVQ (Learning Vector Quantization) model and its variants are regarded as the clustering tools to discriminate the natural seismic events (earthquakes) from the artificial ones (nuclear explosions). The study is based on the six spectral features of the P-wave spectra computed from the short period teleseismic recordings. The conventional LVQ proposed by Kohenen and also the Fuzzy LVQ (FLVQ) models proposed by Sakuraba and Bezdek are all tested on a set of 26 earthquakes and 24 nuclear explosions using the leave-one-out testing strategy. The primary experimental results have shown that the shapes, the number and also the overlaps of the clusters play an important role in seismic classification. The results also showed how an improper feature space partitioning would strongly weaken both the clustering and recognition phases. To improve the numerical results, a new combined FLVQ algorithm is employed in this paper. The algorithm is composed of two nested sub-algorithms. The inner sub-algorithm tries to generate a well-defined fuzzy partitioning with the fuzzy reference vectors in the feature space. To achieve this goal, a cost function is defined as a function of the number, the shapes and also the overlaps of the fuzzy reference vectors. The update rule tries to minimize this cost function in a stepwise learning algorithm. On the other hand, the outer sub-algorithm tries to find an optimum value for the number of the clusters, in each step. For this optimization in the outer loop, we have used two different criteria. In the first criterion, the newly defined "fuzzy entropy" is used while in the second criterion, a performance index is employed by generalizing the Huntsberger formula for the learning rate, using the concept of fuzzy distance. The experimental results of the new model show a promising improvement in the error rate, an acceptable convergence time, and also more flexibility in boundary decision making.
ER -