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

Ahmad Kamaruddin, Saadi and Tolos, Siti Marponga and Pah, Chin Hee and Md Ghani, Nor Azura and Mohamed Ramli, Norazan and Mohamed Nasir, Noorhamizah and KSM Kader Ibrahim, Babul Salam and Huq, Mohammad Saiful (2017) The quadriceps muscle of knee joint modelling using hybrid particle swarm optimization-neural network (PSO-NN). Journal of Physics: Conference Series, 819 (012029). pp. 1-11. ISSN 16609336

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Official URL: doi:10.1088/1742-6596/819/1/012029

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:T Technology > TJ Mechanical engineering and machinery > TJ210.2-211 Mechanical devices and figures. Automata. Ingenious mechanisms.
Divisions:Faculty of Electrical and Electronic Engineering > Department of Robotic and Mechatronic Engineering
ID Code:9841
Deposited By:Mr. Mohammad Shaifulrip Ithnin
Deposited On:13 Aug 2018 11:17
Last Modified:13 Aug 2018 11:17

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