The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)
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...
| Main Authors: | , , , , , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IOP Publishing
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/56926/ http://irep.iium.edu.my/56926/1/56926-The%20quadriceps%20muscle%20of%20knee%20joint.pdf http://irep.iium.edu.my/56926/2/56926-The%20quadriceps%20muscle%20of%20knee%20joint_SCOPUS.pdf |
| _version_ | 1848784834470608896 |
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| author | Ahmad Kamaruddin, Saadi Tolos, Siti Marponga Hee, Pah Chin Md Ghani, Nor Azura Ramli, Norazan Mohamed Mohamed Nasir, Noorhamizah Ksm Kader, Babul Salam Huq, Mohammad Saiful |
| author_facet | Ahmad Kamaruddin, Saadi Tolos, Siti Marponga Hee, Pah Chin Md Ghani, Nor Azura Ramli, Norazan Mohamed Mohamed Nasir, Noorhamizah Ksm Kader, Babul Salam Huq, Mohammad Saiful |
| author_sort | Ahmad Kamaruddin, Saadi |
| building | IIUM Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T16:43:33Z |
| format | Proceeding Paper |
| id | iium-56926 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:43:33Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-569262017-06-21T02:10:34Z http://irep.iium.edu.my/56926/ The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) Ahmad Kamaruddin, Saadi Tolos, Siti Marponga Hee, Pah Chin Md Ghani, Nor Azura Ramli, Norazan Mohamed Mohamed Nasir, Noorhamizah Ksm Kader, Babul Salam Huq, Mohammad Saiful QA76 Computer software 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. IOP Publishing 2017-04-03 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/56926/1/56926-The%20quadriceps%20muscle%20of%20knee%20joint.pdf application/pdf en http://irep.iium.edu.my/56926/2/56926-The%20quadriceps%20muscle%20of%20knee%20joint_SCOPUS.pdf 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. http://iopscience.iop.org/article/10.1088/1742-6596/819/1/012029/pdf :10.1088/1742-6596/819/1/012029 |
| spellingShingle | QA76 Computer software Ahmad Kamaruddin, Saadi Tolos, Siti Marponga Hee, Pah Chin Md Ghani, Nor Azura Ramli, Norazan Mohamed Mohamed Nasir, Noorhamizah Ksm Kader, Babul Salam Huq, Mohammad Saiful The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) |
| title | The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) |
| title_full | The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) |
| title_fullStr | The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) |
| title_full_unstemmed | The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) |
| title_short | The quadriceps muscle of knee joint modelling using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN) |
| title_sort | quadriceps muscle of knee joint modelling using hybrid particle swarm optimization-neural network (pso-nn) |
| topic | QA76 Computer software |
| url | http://irep.iium.edu.my/56926/ http://irep.iium.edu.my/56926/ http://irep.iium.edu.my/56926/ http://irep.iium.edu.my/56926/1/56926-The%20quadriceps%20muscle%20of%20knee%20joint.pdf http://irep.iium.edu.my/56926/2/56926-The%20quadriceps%20muscle%20of%20knee%20joint_SCOPUS.pdf |