کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2076893 | 1079468 | 2007 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
مدلسازی و شبیه سازی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network](/preview/png/2076893.png)
چکیده انگلیسی
The aim of this study is to design an artificial neural network (ANN) to model force-velocity relation in skeletal muscle isotonic contraction. We obtained the data set, including physiological and morphometric parameters, by myography and morphometric measurements on frog gastrocnemius muscle. Then, we designed a multilayer perceptron ANN, the inputs of which are muscle volume, muscle optimum length, tendon length, preload, and afterload. The output of the ANN is contraction velocity. The experimental data were divided randomly into two parts. The fisrt part was used to train the ANN. In order to validate the model, the second part of experimental data, which was not used in training, was employed to the ANN and then, its output was compared with Hill model and the experimental data. The behavior of ANN in high forces was more similar to experimental data, but in low forces the Hill model had better results. Furthermore, extrapolation of ANN performance showed that our model is more or less able to simulate eccentric contraction. Our results indicate that ANNs represent a powerful tool to capture some essential features of muscle isotonic contraction.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biosystems - Volume 90, Issue 2, SeptemberâOctober 2007, Pages 529-534
Journal: Biosystems - Volume 90, Issue 2, SeptemberâOctober 2007, Pages 529-534
نویسندگان
Sharareh Dariani, Mansoor Keshavarz, Mohsen Parviz, Mohammad Reza Raoufy, Shahriar Gharibzadeh,