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Separable least squares identification of long memory block structured models: application to lung tissue
1Dept. of Electr. & Comput. Eng., Schulich Sch. of Eng., Calgary, Alta, Canada.
Summary
A new algorithm simplifies Wiener model parameter estimation for biomechanical analysis. This method enhances the accuracy of identifying material properties from experimental stress/strain data.
Area of Science:
- Biomechanics
- Mathematical Modeling
- Materials Science
Background:
- Accurate modeling of biological tissues is crucial for understanding their mechanical behavior.
- Wiener models are often used to describe the viscoelastic properties of materials.
- Identifying parameters for complex models can be computationally intensive.
Purpose of the Study:
- To develop an efficient algorithm for identifying parameters of a modified Wiener model.
- To simplify the optimization procedure for Wiener model identification.
- To apply the developed algorithm to experimental biomechanical data.
Main Methods:
- Development of a separable least squares algorithm.
- Modification of a constant phase model to include a viscous term within a Wiener model framework.
- Reduction of the parameter search space from 5 to 2 dimensions.
Main Results:
- The separable least squares algorithm significantly simplifies the optimization process.
- Successful identification of Wiener model parameters using experimental stress/strain data from lung parenchyma.
- Demonstration of the algorithm's effectiveness on real-world biomechanical data.
Conclusions:
- The developed separable least squares algorithm provides an efficient method for Wiener model identification.
- This approach is well-suited for analyzing the mechanical properties of biological tissues like lung parenchyma.
- The simplified optimization procedure facilitates broader application in biomechanical research.
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