Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Theme-Based Comprehensive Evaluation In New Product Development Using Fuzzy Hierarchical Criteria Group Decision-Making Method, Jie Lu, Jun Ma, Guangquan Zhang, Yijun Zhu, Xianyi Zeng, Ludovic Koehl
Theme-Based Comprehensive Evaluation In New Product Development Using Fuzzy Hierarchical Criteria Group Decision-Making Method, Jie Lu, Jun Ma, Guangquan Zhang, Yijun Zhu, Xianyi Zeng, Ludovic Koehl
Faculty of Engineering and Information Sciences - Papers: Part A
One of the features of the digital ecosystem is the integration of human cognition and socio-economic themes into the process of new product development (NPD). In a socio-economic theme-based NPD, ranking a set of product prototypes that have been designed always requires the participation of multiple evaluators and consideration of multiple evaluation criteria. Using the well-being theme-based garment NPD as a background, this paper first presents a fuzzy hierarchical criteria group decision-making (FHCGDM) method which can effectively calculate final ranking results through fusing all assessment data from human beings and machines. It then presents a garment NPD comprehensive evaluation model …
Fuzzy Universal Hashing And Approximate Authentication, Rei Safavi-Naini, Joseph Tonien
Fuzzy Universal Hashing And Approximate Authentication, Rei Safavi-Naini, Joseph Tonien
Faculty of Engineering and Information Sciences - Papers: Part A
No abstract provided.
A Kernel Fuzzy C-Means Clustering-Based Fuzzy Support Vector Machine Algorithm For Classification Problems With Outliers Or Noises, Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma
A Kernel Fuzzy C-Means Clustering-Based Fuzzy Support Vector Machine Algorithm For Classification Problems With Outliers Or Noises, Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma
Faculty of Engineering and Information Sciences - Papers: Part A
The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set …