Sensitivity of Muscle Force Estimations to Changes in Muscle Input Parameters Using Nonlinear Optimization Approaches

1992 ◽  
Vol 114 (2) ◽  
pp. 267-268 ◽  
Author(s):  
Walter Herzog
Keyword(s):  
Nonlinear Optimization ◽  
Muscle Force ◽  
Anatomical Variations ◽  
Degree Of Freedom ◽  
Optimization Approach ◽  
Force Sharing ◽  
Input Parameters ◽  
Synergistic Muscles

The purpose of this study was to analyze the sensitivity of muscle force calculations to changes in muscle input parameters. Force sharing between two synergistic muscles was derived analytically for a one-degree-of-freedom system using three nonlinear optimization approaches. Changes in input parameters that are within normal anatomical variations often caused changes in muscular forces exceeding 100 percent. These results indicate that errors in muscle force calculations may depend as much on inadequate muscle input parameters as they may on the choice of the objective and constraint functions of the optimization approach.

PREDICTION OF MUSCLE FORCES USING STATIC OPTIMIZATION FOR DIFFERENT CONTRACTILE CONDITIONS

2013 ◽  
Vol 13 (03) ◽  
pp. 1350022 ◽  
Author(s):  
YUNUS ZIYA ARSLAN ◽  
AZIM JINHA ◽  
MOTOSHI KAYA ◽  
WALTER HERZOG
Keyword(s):  
Cost Function ◽  
Fiber Type ◽  
Degree Of Freedom ◽  
Muscle Forces ◽  
Cross Sectional ◽  
Satisfactory Description ◽  
New Approach ◽  
Force Sharing ◽  
Synergistic Muscles ◽  
The Cost

In this study, we introduced a novel cost function for the prediction of individual muscle forces for a one degree-of-freedom musculoskeletal system. Unlike previous models, the new approach incorporates the instantaneous contractile conditions represented by the force-length and force-velocity relationships and accounts for physiological properties such as fiber type distribution and physiological cross-sectional area (PCSA) in the cost function. Using this cost function, it is possible to predict experimentally observed features of force-sharing among synergistic muscles that cannot be predicted using the classical approaches. Specifically, the new approach allows for predictions of force-sharing loops of agonistic muscles in one degree-of-freedom systems and for simultaneous increases in force in one muscle and decreases in a corresponding agonist. We concluded that the incorporation of the contractile conditions in the weighting of cost functions provides a natural way to incorporate observed force-sharing features in synergistic muscles that have eluded satisfactory description.

Velocity-dependent cost function for the prediction of force sharing among synergistic muscles in a one degree of freedom model

2009 ◽  
Vol 42 (5) ◽  
pp. 657-660 ◽  
Author(s):  
Gudrun Schappacher-Tilp ◽  
Paul Binding ◽  
Elena Braverman ◽  
Walter Herzog
Keyword(s):  
Cost Function ◽  
Degree Of Freedom ◽  
Force Sharing ◽  
Synergistic Muscles

An Optimization Approach to Vibration Isolation of Rigid Bodies

1997 ◽  
Vol 25 (3) ◽  
pp. 165-175
Author(s):  
P. S. Heyns
Keyword(s):  
Design Problem ◽  
Vibration Isolation ◽  
Rigid Bodies ◽  
Degree Of Freedom ◽  
Optimization Approach ◽  
Local Minima ◽  
Single Degree Of Freedom ◽  
Single Degree

The conventional single-degree-of-freedom approach to isolator design dealt with in most undergraduate curricula, is not always adequate for the design of practical isolator systems. In this article, an optimization approach to the design problem is presented and the viability of the approach demonstrated. It is, however, also shown that multiple local minima may exist and that due care should be exercised in the application of the method.

scholarly journals Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Jamal Abdulrazzaq Khalaf ◽  
Abeer A. Majeed ◽  
Mohammed Suleman Aldlemy ◽  
Zainab Hasan Ali ◽  
Ahmed W. Al Zand ◽  
...  
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Deep Learning ◽  
Structural Engineering ◽  
Major Influence ◽  
Support Vector ◽  
Optimization Approach ◽  
Tree Model ◽  
Input Parameters ◽  
Deep Learning Neural Network

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model.

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