The quadriceps muscle of knee joint modelling using neural network approach: Part 1

• Published in: 2016 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2016
• Main Author: Ghani N.A.M.; Kamaruddin S.B.A.; Ramli N.M.; Nasir N.B.M.; Kader B.S.B.K.; Huq M.S.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2017
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029390612&doi=10.1109%2fIC3e.2016.8009039&partnerID=40&md5=45c737376f6c5aac059176e8d29cca02

Artificial neural approach has been executed in various recorded, and a champion amongst the most understood widespread approximators. Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). This paper exhibits the improvement of quadriceps muscle model by utilizing counterfeit smart procedure named backpropagation neural network nonlinear autoregressive (BPNN-NAR) model in view of utilitarian electrical incitement (FES). A progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that BPNN-NAR is suitable and efficient to model this type of data. A neural network model is the best approach for modelling non-linear models such as active properties of the quadriceps muscle with one input, namely output namely muscle force. © 2016 IEEE.