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

37th International Conference on Quantum Probability and Related Topics

Kamaruddin, Saadi Bin Ahmad^1 ; Tolos, Siti Marponga^1 ; Hee, Pah Chin^1 ; Ghani, Nor Azura Md^2 ; Ramli, Norazan Mohamed^2 ; Nasir, Noorhamizah Binti Mohamed^3 ; Kader, Babul Salam Bin Ksm^3 ; Huq, Mohammad Saiful^3

Computational and Theoretical Sciences Department, Kulliyyah of Science, International Islamic University Malaysia, Jalan Istana, Bandar Indera Mahkota, Pahang Darul Makmur, Kuantan

25200, Malaysia^1

Center for Statistical and Decision Sciences Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Selangor Darul Ehsan, Shah Alam

40450, Malaysia^2

Advanced Mechatronic Research Group (AdMiRe), Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor

86400, Malaysia^3

关键词: Back propagation neural networks;    Complex nonlinear system;    Hybrid Particle Swarm Optimization;    Logical modeling;    Mean Square Error (MSE);    Non-linear modelling;    Nonlinear auto-regressive moving averages;    Time series models;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/819/1/012029/pdf
DOI  :  10.1088/1742-6596/819/1/012029

来源: IOP

【 摘 要 】

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.