The quadriceps muscle of knee joint modelling using hybrid particle swarm optimization-neural network (PSO-NN)

Mohamed Nasir, Noorhamizah and Huq, Mohammad Saiful and Mohamed Ramli, Norazan and KSM Kader, Babul Salam and Pah, Chin Hee and Tolos, Siti Marponga and Md Ghani, Nor Azura and Ahmad Kamaruddin, Saadi (2015) The quadriceps muscle of knee joint modelling using hybrid particle swarm optimization-neural network (PSO-NN). Journal of Physics: Conference Series, 819 (1). pp. 1-11. ISSN 1742-6596

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Abstract

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). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, 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 both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural network; Backpropagation; Nonlinear autoregressive; Quadriceps muscle "
Subjects:	R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electronic Enngineering
Depositing User:	Mrs. Mashairani Ismail
Date Deposited:	20 Jan 2022 04:19
Last Modified:	20 Jan 2022 04:19
URI:	http://eprints.uthm.edu.my/id/eprint/5673

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