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An approach to estimating product design time based on fuzzy v-support vector machine.
1Research Institute of Automation, Southeast University, Nanjing 210096, China. hsyan@seu.edu.cn
IEEE Transactions on Neural Networks
|May 29, 2007
Summary
This study introduces a novel fuzzy v-support vector machine (Fv-SVM) for accurate product design time estimation, even with limited and uncertain data. The Fv-SVM method demonstrates superior precision and requires fewer samples than fuzzy neural networks.
Area of Science:
- Engineering
- Computer Science
- Artificial Intelligence
Background:
- Product design time estimation faces challenges due to limited samples and data uncertainty.
- Existing methods may lack precision when dealing with fuzzy or imprecise data.
Purpose of the Study:
- To develop a new fuzzy support vector machine (FSVM) model for enhanced product design time estimation.
- To address the limitations of finite samples and uncertain data in estimation tasks.
Main Methods:
- Defined input and output variables as fuzzy numbers with a metric on fuzzy number space.
- Proposed a fuzzy v-support vector machine (Fv-SVM) by integrating fuzzy theory and v-support vector machines.
- Developed a parameter-choosing algorithm for the Fv-SVM model.
Main Results:
- The Fv-SVM method was applied to injection mold and software product design time estimation.
- Results confirmed the feasibility and validity of the proposed estimation approach.
- The Fv-SVM model showed higher estimating precision compared to the fuzzy neural network (FNN) model.
Conclusions:
- The Fv-SVM approach provides a robust solution for product design time estimation with uncertain data.
- This method is more efficient, requiring fewer samples than traditional fuzzy neural networks.
- The study validates the effectiveness of fuzzy logic integration with SVM for complex estimation tasks.
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