Browsing by Author "Chang, Ching-Ter"

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House selection via the internet by considering homebuyers' risk attitudes with S-shaped utility functions
(2015-02-16)
Ho, Hui-Ping
;
Chang, Ching-Ter
;
Ku, Cheng-Yuan
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The widespread use of the Internet has significantly changed the behavior of homebuyers. Using online real estate agents, homebuyers can rapidly find some modern houses that meet their needs; however, most current online housing systems provide limit features. In particular, existing systems fail to consider homebuyers\' housing goals and risk attitudes. To increase effectiveness, online real estate agents should provide an efficient matching mechanism, personalized service and house ranking with the aim of increasing both buyers\' satisfaction and deal rate. An efficient online real estate agent should provide an easy way for homebuyers to find (rank) a suitable house (alternatives) with consideration of their different housing philosophies and risk attitudes. In order to comprehend these ambiguous housing goals and risk attitudes, it is also indispensable to determine a satisfaction level for each fuzzy goal and constraint. In this study, we propose fuzzy goal programming with an S-shaped utility function as a decision aid to help homebuyers in choosing their preferred house via the Internet in an easy way. With the use of a decision aid, homebuyers can specify their housing goals and constraints with different priority levels and thresholds as a matching mechanism for a fuzzy search, while the matching mechanism can be translated into a standard query language for a regular relational database. Moreover, a laboratory experiment is conducted on a real case to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed method provides better customer satisfaction than manual systems in housing selection service. (C) 2014 Elsevier B.V. All rights reserved.
House selection via the internet by considering homebuyers\' risk attitudes with S-shaped utility functions
(2014-02-16)
Ho, Hui-Ping
;
Chang, Ching-Ter
;
Ku, Cheng-Yuan
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The widespread use of the Internet has significantly changed the behavior of homebuyers. Using online real estate agents, homebuyers can rapidly find some modern houses that meet their needs; however, most current online housing systems provide limit features. In particular, existing systems fail to consider homebuyers\' housing goals and risk attitudes. To increase effectiveness, online real estate agents should provide an efficient matching mechanism, personalized service and house ranking with the aim of increasing both buyers\' satisfaction and deal rate. An efficient online real estate agent should provide an easy way for homebuyers to find (rank) a suitable house (alternatives) with consideration of their different housing philosophies and risk attitudes. In order to comprehend these ambiguous housing goals and risk attitudes, it is also indispensable to determine a satisfaction level for each fuzzy goal and constraint. In this study, we propose fuzzy goal programming with an S-shaped utility function as a decision aid to help homebuyers in choosing their preferred house via the Internet in an easy way. With the use of a decision aid, homebuyers can specify their housing goals and constraints with different priority levels and thresholds as a matching mechanism for a fuzzy search, while the matching mechanism can be translated into a standard query language for a regular relational database. Moreover, a laboratory experiment is conducted on a real case to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed method provides better customer satisfaction than manual systems in housing selection service. (C) 2014 Elsevier B.V. All rights reserved.
Single-hidden-layer feed-forward quantum neural network based on Grover learning
(2013-09-01)
Liu, Cheng-Yi
;
Chen, Chein
;
Chang, Ching-Ter
;
Shih, Lun-Min
;
資訊工程學系
In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. (C) 2013 Elsevier Ltd. All rights reserved.