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Related Experiment Video

Updated: Jul 14, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

An approach to estimating product design time based on fuzzy v-support vector machine.

Hong-Sen Yan¹, Duo Xu

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.

Related Experiment Videos

Last Updated: Jul 14, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

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.