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A fuzzy approach for key variables identification of EMG evaluation signal.

Authors
Hou-Y; Zurada-JM; Karwowski-W; Marras-WS
Source
Proceedings of International Joint Conference on Neural Networks, July 31 - August 4, 2005, Montreal, Canada. Piscataway, NJ: IEEE Operations Center, 2005 Jul; 4:2520-2525
NIOSHTIC No.
20041196
Abstract
Identification of influence of input variables is very important for complex nonlinear systems with high dimensional input space. In this paper we propose a method using fuzzy average with fuzzy cluster distribution (FAFCD). To avoid the interference of different distributions of the sampling data, we deal with the distribution of fuzzy clusters in the sampling data, instead of the original data set. To discover the input-output relationship, we first use method of fuzzy rules and fuzzy c-means to partition the original sampling data set into fuzzy clusters. We produce a new data set with the same distribution of the fuzzy clusters. Then the fuzzy average method is applied to the new data set. By doing this, the interference of distribution of the original sampling data is removed. This method is straightforward and computationally easy. The performance is tested on both benchmark data and the electromyographic (EMG) signal Evaluation System.
Keywords
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling
Publication Date
20050731
Document Type
Conference/Symposia Proceedings
Funding Type
Grant
Fiscal Year
2005
NTIS Accession No.
NTIS Price
ISBN No.
9780780390485
Identifying No.
Grant-Number-R01-OH-007787
Source Name
Proceedings of International Joint Conference on Neural Networks, July 31 - August 4, 2005, Montreal, Canada
State
OH; KY
Performing Organization
Ohio State University
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