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Lina J.,National Taipei University of Technology | Lian R.-J.,Hwa Hsia University of Technology
Applied Soft Computing Journal | Year: 2013

Self-organizing fuzzy controllers (SOFCs) have excellent learning capabilities. They have been proposed for the manipulation of active suspension systems. However, it is difficult to select the parameters of an SOFC appropriately, and an SOFC may extensively modify its fuzzy rules during the control process when the parameters selected for it are inappropriate. To eliminate this problem, this study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems. The GPSOFC introduces a grey-prediction algorithm into an SOFC, in order to pre-correct its fuzzy rules for the control of active suspension systems. This design solves the problem of SOFCs with inappropriately chosen parameters. To evaluate the feasibility of the proposed method, this study applied the GPSOFC to the manipulation of an active hydraulic-servo suspension system, in order to determine its control performance. Experimental results demonstrated that the GPSOFC achieved better control performance than either the SOFC or the passive method of active suspension control. © 2013 Elsevier B.V. All rights reserved.


Chen Y.-S.,Hwa Hsia University of Technology | Cheng C.-H.,National Yunlin University of Science and Technology
Knowledge-Based Systems | Year: 2013

Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve credit rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in credit rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998-2007. Experimental results demonstrate that the proposed hybrid models for credit rating classification outperform the listing models in this work. A set of decision rules for classifying credit ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners. © 2012 Elsevier B.V. All rights reserved.


Chen Y.-S.,Hwa Hsia University of Technology | Cheng C.-H.,National Yunlin University of Science and Technology
Applied Soft Computing Journal | Year: 2012

In the financial markets, due to limitations of the noise caused continuously by changing market conditions and environments, and a subjective sentiment or other factors unrelated to expected returns on investment decision-making of investors, there is a growing consensus designing and employing a variety of soft computing systems to remedy the aforementioned existing problems objectively and intelligently. Previously, many researchers have long used statistical methods for handling the related problems of investment markets. However, these conventional methods become more complex when relationships in the input/output dataset are nonlinear. Nevertheless, statistical techniques always rely on the assumptions on linear separability, multivariate normality, and independence of the predictive variables; unfortunately, many of the common models of treating the financial markets problems violate these assumptions. Therefore, to reconcile the existing shortcomings, this study offers three hybrid models based on a rough sets classifier to extract decision rules and aid making investment decision for the market investors. The proposed hybrid models include three differently integrated models for solving IPO (Initial Public Offerings) returns problems of the financial markets: (1) Experiential Knowledge (EK) + Feature Selection Method (FSM) + Minimize Entropy Principle Approach (MEPA) + Rough Set Theory (RST) + Rule Filter (RF), (2) EK + Decision Trees (DT)-C4.5 + RST + RF, and (3) EK + FSM + RST + RF. The proposed hybrid models are illustrated by examining an IPO dataset for publicly traded firms. The experimental results indicate that the proposed hybrid models outperform the listing methods in accuracy, number of attributes, standard deviation, and number of rules. Furthermore, the proposed hybrid models generate comprehensible rules readily applied in knowledge-based systems for investors. Meaningfully, the study findings and implications are of value to both academicians and practitioners. © 2011 Elsevier B.V. All rights reserved.


Chen C.-H.,Hwa Hsia University of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Feature selection has been explored extensively for several real-world applications. In this paper, we address a new solution of selecting a subset of original features for unlabeled data. The concept of our feature selection method is referred to a basic characteristic of clustering in thata data instance usually belongs in the same cluster with its geometrically nearest neighbors and belongs to different clusters with its geometrically farthest neighbors. In particular, our method uses instance-based learning for quantifying features in the context of the nearest and the farthest neighbors of every instance, such that using salient features can raise this characteristic. Experiments on several datasets demonstrated the effectiveness of our presented feature selection method. © 2011 Springer-Verlag.


Ou C.-Y.,National Taiwan University of Science and Technology | Hsieh P.-G.,Hwa Hsia University of Technology
Computers and Geotechnics | Year: 2011

A series of parametric studies was performed to examine the influence factors affecting the settlement influence zone induced by excavation in soft clay. It was found that the excavation depth, width, the soft clay bottom depth and the rock-like soil depth are all related to the settlement influence zone. The potential failure surface, as deduced from the failure mechanism, covering the above-mentioned parameters, is consistent with the settlement influence zone. Thus, a simple method based on the analysis results is proposed to predict the settlement influence zone. Ten case histories and statistical data for the settlements in the Shanghai area were used to verify the proposed method. © 2011 Elsevier Ltd.


Lian R.-J.,Hwa Hsia University of Technology
IEEE Transactions on Industrial Electronics | Year: 2014

A self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) has been proposed to control robotic systems. The SFRBNC uses a radial basis-function neural-network (RBFN) to regulate the parameters of a self-organizing fuzzy controller (SOFC) to appropriate values in real time. This method solves the problem caused by the inappropriate selection of parameters in an SOFC. It also eliminates the dynamic coupling effects between degrees of freedom (DOFs) for robotic system control because the RBFN has coupling weighting regulation capabilities. However, its stability is difficult to demonstrate. To overcome the stability issue, this study developed an adaptive self-organizing fuzzy sliding-mode radial basis-function neural-network controller (ASFSRBNC) for robotic systems. The ASFSRBNC solves the problem of an SFRBNC implementation in determining the stability of the system control. It also applies an adaptive law to modify the fuzzy consequent parameter of a fuzzy logic controller to manipulate a robotic system to improve its control performance. The stability of the ASFSRBNC was proven using the Lyapunov stability theorem. From the experimental results of 6-DOF robotic control tests, the ASFSRBNC achieved better control performance than the SFRBNC as well as the SOFC. © 1982-2012 IEEE.


Pan W.-T.,Hwa Hsia University of Technology
Connection Science | Year: 2013

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models. © 2013 © 2013 Taylor & Francis.


Pan W.-T.,Hwa Hsia University of Technology
International Journal of Technology Management | Year: 2014

Since Taiwan and mainland China signed the Economic Cooperation Framework Agreement (ECFA) across the Taiwan Strait, the number of mainland tourists visiting Taiwan has grown significantly. To cope with the needs of mainland tourists, Taiwan must reinforce the software and hardware facilities and service quality of its entire tourism industry. This will attract more tourists to Taiwan and create more opportunities for the Taiwanese tourism industry. In this article, tourists visiting Taiwan are asked to complete a questionnaire survey; we then use the satisfaction information gathered to perform grey relational analysis so as to understand the best and worst scoring questions related to satisfaction. From the analysis results, it can be seen that in the assessment of satisfaction question performance, Taiwanese cuisine scores the highest, the cleanliness of Taiwan's streets scores the lowest, and of people interviewed between 30 and 40 years old, more rated satisfaction performance and characteristics negatively.


Lin J.-H.,Hwa Hsia University of Technology
International Journal of Pavement Engineering | Year: 2014

This study proposes a spectral approach to the evaluation of variations in dynamic vehicle load on road pavement associated with a vehicle moving at constant speed along a road with a rough surface. The influence of vehicle speed and road roughness on variations in dynamic vehicle load was investigated. The results show that the standard deviation of dynamic vehicle load increases twofold, with a fourfold increase in the road roughness coefficient, and increases approximately linearly with the increase in vehicle speed. © 2013 Taylor & Francis.


Chen Y.-S.,Hwa Hsia University of Technology
Knowledge-Based Systems | Year: 2012

Although Asia is at the forefront of global economic growth, its investment environment is very risky and uncertain. Credit ratings are objective opinions about credit worthiness, investment risk, and default probabilities of issues or issuers. To classify credit ratings, analyze their determinants, and provide meaningful decision rules for interested parties, this work proposes an integrated procedure. First, this work adopts an integrated feature-selection approach to select key attributes, and then adopts an objective cumulative probability distribution approach (CPDA) to partition selected condition attributes by applying rough sets local-discretization cuts. This work then applies the rough sets LEM2 algorithm to generate a comprehensible set of decision rules. Finally, this work utilizes a rule filter to eliminate rules with poor support and thereby improve rule quality. The experimental focus was the Asian banking industry. Data were retrieved from a BankScope database that covers 1327 Asian banks. Experimental results demonstrate that the proposed procedure is an effective method of removing irrelevant attributes and achieving increased accuracy, providing a knowledge-based system for classification of rules for solving credit-rating problems encountered by banks, thereby benefiting interested parties. © 2011 Elsevier B.V. All rights reserved.

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