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Taichung, Taiwan

Ling Tung University is a private university founded in 1964. Ling Tung University is located in Nan-tun District, Taichung City, Taiwan. Wikipedia.

Hsu L.-C.,Ling Tung University
Expert Systems with Applications | Year: 2011

Numerous forecasting models have been developed. Each has its own conditions of application. However, it has always been an important research objective to improve prediction accuracy with a small amount of data. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting models still have some potential problems that need to be improved. Therefore, this study proposed an improved transformed grey model based on a genetic algorithm (ITGM(1,1)), and used the output of the opto-electronics industry in Taiwan from 1990 to 2008 as an example for verification. Three grey forecasting models, GM(1,1), rolling GM(1,1), and the transformed GM(1,1), were chosen for the purpose of comparison with ITGM(1,1) by mean absolute percent error and root mean square percent error. The results show that ITGM(1,1) is more accurate than the other three models in both in-sample and out-of-sample forecasting performance, and can greatly improve the accuracy of short-term forecasts. © 2011 Elsevier Ltd. All rights reserved.

Wang K.-C.,Ling Tung University
Expert Systems with Applications | Year: 2011

Nowadays customers choose products strictly in terms of their specific demands. How to quickly and accurately catch customers' feelings and transform them into design elements and vice versa becomes an important issue. This study explores the bi-directional relationship between customers' demands or needs and product forms by using a novel integral approach. High-price machine tools are used as our demonstration target. This integral approach adopts the "grey system theory (GST)", and the state-of-the-art machine learning based modeling formalism "support vector regression (SVR)" in the "Kansei engineering (KE)" process. The GST is used to effectively determine the influence weighting of form parameters on product images and the SVR is used to precisely establish the mapping relationship between product form elements and product images. Furthermore, for practical concerns, a user-friendly design hybrid design expert system was developed based on the proposed novel integral schemes. © 2011 Elsevier Ltd. All rights reserved.

This study proposes a method of cluster validity index that simultaneously provide the measurements of goodness of clustering on clustered data and of classification accuracy for complicated information systems based upon the PBMF-index method and rough set (RS) theory. The maximum value of this index, called the Huang-index, not only provides the best partitioning, but also obtains the optimal accuracy of classification for the approximation sets. The traditional PBMF-index method is only used to ensure the formation of a small number of compact clusters with large separation between at least two clusters. In contrast to the traditional PBMF-index method, the Huang-index method extends the applications of unsupervised optimal cluster to the fields of classification. In the proposed algorithm, all the attributes of the data are first clustered into groups using the Fuzzy C-means (FCM) method. The clustered data are then used to identify approximate regions and classification accuracy and to calculate centroids of clusters for decision attribute based on the RS theory. Finally, all those calculated data are put into the proposed index method to find the cluster validity index. The validity of the proposed approach is demonstrated using the data derived from a hypothetical function of two independent variables and electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The clustering results obtained using the proposed method are compared with the results obtained using the traditional PBMF-index partition method. The effects of the number of clusters on the partitions of clusters and the RS regions are systematically examined and compared. The results show that the proposed Huang-index method not only yields a superior clustering capability than the traditional clustering algorithm, but also yields a reliable classification and obtains a set of suitable decision rules extracted from the RS theory. © 2010 Elsevier Ltd. All rights reserved.

Hsu L.-C.,Ling Tung University
Expert Systems with Applications | Year: 2010

In this article, an improved nonlinear grey Bernoulli model by using genetic algorithms to solve the optimal parameter estimation problem of small amount of data used in the forecast is proposed. The time series data of Taiwan's integrated circuit industry (1990-2007) was used as the test data set. In addition, the mean absolute percentage error and the root mean square percentage error were used to compare the performance of the forecast models. The results showed that the improved nonlinear grey Bernoulli model is more accurate and performs better than the traditional GM(1,1) model and grey Verhulst model. Moreover, the optimum mechanisms indeed improve the grey model of prediction accuracy by using genetic algorithms approach. © 2009 Elsevier Ltd. All rights reserved.

Wan S.,Ling Tung University
Environmental Earth Sciences | Year: 2013

Generation of landslide susceptibility maps is important for engineering geologists and geomorphologists. The goal of this study is to generate a reliable susceptibility map based on digital elevation modeling and remote sensing data through clustering technique. This study focused on the landslide problems on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Two stages of analysis were used to extract the dominant attributes and thresholds: (1) calculate the entropy with regard to the measure of influenced variables to the occurrence of landslide and (2) use the clustering analysis K-means with particle swarm optimization (KPSO) to resolve the difficulties in creating landslide susceptibility maps. The knowledge scope with regard to core factors and thresholds are solved. The self-organization map (SOM) is used as a parallel study for comparison. The overall accuracy of the susceptibility map is 86 and 77 % for KPSO and SOM, respectively. Then, the susceptibility maps are drawn and verifications made. The generation of a susceptibility map is useful for decision makers and managers to handle the landslide risk area. © 2012 Springer-Verlag.

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