Research On Key Technologies Of Computer-Aided Cigarette Formula Design

As an interdisciplinary research project which combined with computer technology and blending technology, computer aided cigarette formula design does not still has unified concept, theoretical framework and technological system. But in recent years, with the acceleration of reorganization in tobacco companies, improvement of brand concentration and more strict safety requirements, many fields have raised serious challenge such as the integration and more valid use of the raw material of tobacco, the mode of traditional formula design and the control capability of homogeneous quality manufacture in multi-manufactory.Based on past researches in computer aided cigarette formula design in the early years, this dissertation faces and solves the new technical difficulties and new challenge which encountered in engineering practice.From the perspective of engineering of machine learning or pattern recognition, this dissertation analyzes the shortcoming of pattern recognition and inductive inference mechanism and gives the new method. The dissertation begins from the analysis of basic principle of aided formula design, and pays more attention on three key technologies. The major research results summarized as follows:1.Based on the review of domestic and international research situation and analysis of complexity of formula field, this dissertation gives the basic principle, basic characteristic, technological target and technological framework about computer aided cigarette formula design, and deeply analyzed some key technologies so as to help the researcher and companies to master the research contents and objective. At the same time, the dissertation points out computer aided quality evaluation is the kernel research to aided cigarette formula design, the performance of quality evaluation will directly influence the selection effect of formula in formula optimization design.2.To the "distance invalidation" issue in raw materials similarity measure, the dissertation analyzes the intrinsic reason about "distance invalidation" from mathematical analysis, geometric understanding and the consistency of distance between points in high-dimensional space to "curse of dimensionality".Based on the studies of Locally Linear embedding (LLE) algorithm in manifold learning, an improved algorithm of LLE (KGLLE) which names LLE based on geodesic distance of kernel transformation is proposed.KGLLE algorithm is designed to adapt the characteristic of tobacco quality data including the sparse of samples, local non-linear, non-smooth features, and satisfied the Isometric in low-dimensional space to similarity measure. The dissertation gives the design principles and process of KGLLE in details, and uses the KGLLE solved the problems of tobacco material similarity measure in high-dimensional space.3.Facing the engineering problem in which experts'experience and domain knowledge are difficult to integrate into classifier and experts only passively accept prediction results of classifier, the dissertation points out the limitation of "induction inference" from the perspective of inference mechanism and the difficulty for current classifier to solve above problems.Then the kernelizing K nearest neighbor metric conformal predictor (CP-KKNN) is proposed based on transduction inference conformal predictor, and verified by the experiment in Iris dataset and tobacco tar dataset. The experiment obtains good classification performance. The research results show that conformal predictor has very important practice value to classification application which is similar to tobacco quality prediction.4.Most of current main classifiers have mechanical and blind prediction behavior on prediction. Instructed by the idea of "cognition" and "coverage" in bionic pattern recognition, the dissertation integrates several theories and methods including hypothesis testing, convex hull and interior point analysis and sequence random testing and so on, designed a classifier prediction control algorithm (RC-PC) which has the characteristic of rejecting recognition and credibility analysis, and verified the good effect for reducing prediction error rate by the experiment on SVM classifier of tobacco aroma style. The studies show that the effect in engineering application is better than improving classification algorithm if is gived emphasis to prediction process.5.After a brief analysis of main issue to multi-technology integrated aided formula design software system, the dissertation gives the system design objective, design instruction principles, four layer frameworks, and proposes the general principles to the algorithm selection, and provides two software screenshots, so as to expect more of the pattern recognition or machine learning researchers to pay more attention to engineering practice.Finding the source of innovation from practice!6.The dissertation summarizes the major innovative work and research findings, and indicates the future research emphasis from four directions:the analysis of data set structure, new predictor, convex hull and aided cigarette design.