The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

Ahmad Kamaruddin, Saadi and Tolos, Siti Marponga and Hee, Pah Chin and Md Ghani, Nor Azura and Ramli, Norazan Mohamed and Mohamed Nasir, Noorhamizah and Ksm Kader, Babul Salam and Huq, Mohammad Saiful (2017) The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN). In: 37th International Conference on Quantum Probability and Related Topics (QP37) 2016, 22nd-26th August 2016, Kuantan, Pahang, Malaysia.

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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:	Conference or Workshop Item (Plenary Papers)
Additional Information:	7581/56926
Uncontrolled Keywords:	artificial neural network, backpropagation, nonlinear autoregressive, quadriceps muscle
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science > Department of Computational and Theoretical Sciences
Depositing User:	Mr. Saadi Ahmad Kamaruddin
Date Deposited:	21 Jun 2017 10:10
Last Modified:	21 Jun 2017 10:10
URI:	http://irep.iium.edu.my/id/eprint/56926

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