A Web-Based Intelligent Sensory Evaluation System For Textile Products

To complete the intelligent sensory evaluation for Textile Products, we use the methods of fuzzy techniques, neural networks, classical factorial analysis and other data analysis to analyze and transact the sensory data which from different panels, as well as the objective data from KES, Moreover we also build the relationship between them. Based on which we establishe the Web-Based Intelligent Sensory Evaluation System for Fabrics.In disposing the objective data, in order to avoid of high dimension problem, we develop some methods for selecting the relevant rules that are used to build the relationship between the selected objective feature parameters and the sensory fabric data. We use Java language and SQL database to develop the sensory evaluation system in Internet environment. So given the objective data of a new fabric sample, the system can provide its sensory data and its total hand grade. In meantime, if the sensory data are also given the total hand grade can be obtained directly from the sensory fabric hand dataIn the first chapter, we firstly introduce several basic conceptions and then present contents of the thesis.In the second chapter, taking into account the theory bases and the practical demands of market, the definitions of sensory evaluation and intelligent sensory evaluation are introduced. Based on it we put forward the whole model of intelligent sensory evaluation system, as well as its sub-models. Then the function of individual model and the relationships among them are explained.In the third chapter, we treat with the sensory date by the model of Sensory Data Aggregated Model (SDAM), in which a new linguistic 2-tuple fuzzy model based approach is proposed for evaluating fabrics, and aggregating the sensory data. Otherwise using the built model to analyze different panels and evaluation results.In the fourth chapter, we deal with the objective date, including the Objective Date Processing Model (ODPM) and the Objective Date Clustering Model (ODCM). In the former model, using PCA we extract the features from KES parameters to reduce the dimension of discussed data space. In the latter one, using grade clustering method and FCM we give the classes of the samples based on physical parameters.In the fifth chapter, we take use of ANFIS to build the relationship between the sensory data and objective data. Through this model, given theobjective data we can get one or all sensory evaluation of criterion, conversely we can also get the sensory data.In the sixth chapter, we develop the WISES, in detail Java language is used to compile the mail program, SQL is used to manage the data system and Frontpage is used to make web page.In the final chapter, we prospect the next study work for making progresses.The work in this thesis puts focus on the innovation in techniques, following is the main innovation: (1) fuzzy logic and neural networks are used to deal with data which differ from old classical methods. (2) the Internet is used as the service platform, we use several kinds of software to write programs concerned.