A fuzzy approach for key variables identification of EMG evaluation signal.
Hou-Y; Zurada-JM; Karwowski-W; Marras-WS
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
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.
Models; Computer-models; Mathematical-models; Biomechanical-modeling; Biomechanics; Musculoskeletal-system; Manual-lifting; Materials-handling; Manual-materials-handling
Proceedings of International Joint Conference on Neural Networks, July 31 - August 4, 2005, Montreal, Canada
Ohio State University