Abstract
There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR.
Highlights
Product design is a complex and dynamic process, and its duration is affected by a number of factors, most of which are of fuzzy, random and uncertain characteristics
The 28-day Compressive Strength is taken as the desired output variable
The control and decision of product development are based on the reasonable degree of the distribution of product design time
Summary
Product design is a complex and dynamic process, and its duration is affected by a number of factors, most of which are of fuzzy, random and uncertain characteristics. As product design tasks occur in different companies, uncertain characteristics may vary from product to product. The mapping from the factors to design time is highly nonlinear, and it is impossible to describe this mapping relationship by definite mathematical models. The degree of reasonability of the supposed distribution of product design time is a key factor in product development control and decisions [1,2,3]. The triangular probability distribution was chosen by Cho and Eppinger [1] to represent design task durations, and a process modeling and analysis technique for managing complex design projects was proposed by using advanced simulation. If the assumed distribution of design activity durations does not reflect the true state, the proposed algorithm may fail to obtain ideal results
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