The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy logic and optimization models for implementing QFD
Proceedings of the 23rd international conference on on Computers and industrial engineering
NREC: Risk Assessment and Planning of Complex Designs
IEEE Design & Test
Optic flow estimation by support vector regression
Engineering Applications of Artificial Intelligence
Forecasting of the daily meteorological pollution using wavelets and support vector machine
Engineering Applications of Artificial Intelligence
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
Application of support vector machines in scour prediction on grade-control structures
Engineering Applications of Artificial Intelligence
The forecasting model based on wavelet ν-support vector machine
Expert Systems with Applications: An International Journal
Brief paper: On-line voltage security assessment of power systems using core vector machines
Engineering Applications of Artificial Intelligence
Prediction of anterior scoliotic spinal curve from trunk surface using support vector regression
Engineering Applications of Artificial Intelligence
Application of support vector machines to bandwidth reservation in sectored cellular communications
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
| Hi-index | 12.05 |
In present competitive environment, it is necessary for companies to evaluate design time and effort at the early stage of product development. However, there is somewhat lacking in systemic analytical methods for product design time (PDT). For this end, this paper explores an intelligent method to evaluate the PDT. At the early development stage, designers are short of sufficient product information and have difficulty in determining PDT by subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of ''function-principle-structure'' to extract product characteristics from customer demands. Then, a fuzzy support vector regression machine (FSVRM) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. In a word, the whole estimation method consists of four steps: time factors identification, product characteristics extraction by QFD and function mapping pattern, FSVRM learning, and PDT estimation. Finally, to illustrate the procedure of the estimation method, the case of injection mold design is studied. The results of experiments show that the fuzzy method is feasible and effective.