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Xiaodong Liu

Other affiliations: Dalian Maritime University
Bio: Xiaodong Liu is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 33, co-authored 215 publications receiving 3642 citations. Previous affiliations of Xiaodong Liu include Dalian Maritime University.


Papers
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Journal ArticleDOI
TL;DR: A new quadratic stability condition, more simple than that in a previous paper, has been proposed and two new sufficient conditions in the terms of linear matrix inequalities (LMIs) which guarantee the existence of the state feedback H/sub /spl infin// control for the T-S fuzzy systems have been proposed.
Abstract: In this paper, the problems of quadratic stability conditions and H/sub /spl infin// control designs for Takagi-Sugeno (T-S) fuzzy systems have been studied. First, a new quadratic stability condition, which is more simple than that in a previous paper, has been proposed. Second, two new sufficient conditions in the terms of linear matrix inequalities (LMIs) which guarantee the existence of the state feedback H/sub /spl infin// control for the T-S fuzzy systems have been proposed. The conditions are not only simple but also consider the interactions among the fuzzy subsystems. Finally, based on the LMIs, the H/sub /spl infin// controller designing methods for the T-S fuzzy systems have been given.

249 citations

Journal ArticleDOI
TL;DR: By defining a more general type of Lyapunov functionals, some new less conservative delay-dependent stability criteria are obtained and shown in terms of linear matrix inequalities (LMIs).
Abstract: In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.

219 citations

Journal ArticleDOI
TL;DR: The experimental results on other 8 data sets show the feasibility of the proposed MR-PCC-Tree and its good parallel performance on reducing computational time for large-scale data classification problems.

176 citations

Journal ArticleDOI
TL;DR: Axiomatic Fuzzy Set clustering method has been introduced in this paper and it is effective to determine the optimal logistics center location even with the interactive and interdependent criteria/attributes.
Abstract: Research highlights? The selection of logistics center problem is a key issue for many companies to control their costs. ? The criteria, which are relevant in the selection of logistics center site, is proposed and analyzed and identified. ? Axiomatic Fuzzy Set clustering method is introduced in this paper. ? Axiomatic Fuzzy Set further effectively evaluate factors of logistics center location. ? Final selection of logistics center is determined by TOPSIS method. This article presents a comprehensive methodology for the selection of logistic center location. The proposed methodology consists of two parts: (i) AFS (Axiomatic Fuzzy Set) clustering method (Liu, Wang, & Chai, 2005) has been studied further to effectively evaluate logistics center location, and (ii) TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)-based final selection. The criteria, which are relevant in the selection of logistics center site, have been analyzed and identified, and the logistics center site evaluation system is built by using modern principles of town planning and logistics. A case fifteen regional logistics center cities and thirteen criteria are studied and the numerical results show that the proposed evaluation framework is reasonable to identify logistics center location, and it is effective to determine the optimal logistics center location even with the interactive and interdependent criteria/attributes.

132 citations

Journal ArticleDOI
TL;DR: A novel method of partitioning the universe of discourse of time series based on interval information granules is proposed for improving forecasting accuracy of model and shows results that produce more reasonable intervals exhibiting sound semantics.

115 citations


Cited by
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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Dissertation
01 Jan 1975

2,119 citations

Journal ArticleDOI
TL;DR: A state-of-the-art literature survey is conducted to taxonomize the research on TOPSIS applications and methodologies and suggests a framework for future attempts in this area for academic researchers and practitioners.
Abstract: Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.

1,571 citations