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...
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| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IOP Publishing
2017
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| Subjects: | |
| Online Access: | 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 http://irep.iium.edu.my/56926/ http://iopscience.iop.org/article/10.1088/1742-6596/819/1/012029/pdf |
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| Summary: | 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. |
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