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Xiao-Jun Zeng

Also published under: Xiao-jun Zeng

Publications
22
Citations
845
Publications by Year
19942015
Co-Authors:
Show All Co-Authors (9)
Image of author Xiao-Jun Zeng

Xiao-Jun Zeng

Also published under: Xiao-jun Zeng

Affiliation

School of Computer Science
University of Manchester
Manchester, U.K

Publication Topics

Biography

Xiao-Jun Zeng received the B.Sc. degree in mathematics in 1982, the M.Sc. degree in computer and systems sciences from Xiamen University, Xiamen, China, in 1985, and the Ph.D. degree in computation from the University of Manchester Institute of Science and Technology, Manchester, U.K., in 1996.,He is currently a Senior Lecturer in the School of Informatics, University of Manchester. From 1996 to 2002, he was with Knowledge Support Systems Ltd., Manchester, where he was a Scientific Developer, Senior Scientific Developer, and Head of Research. From 1985 to 1992, he was with the Department of Computer and Systems Sciences, Xiamen University, where he was a Lecturer and an Associate Professor. His current research interests include fuzzy syste...
Publications
22
Citations
845
Publications by Year
19942015
Co-Authors:
Show All Co-Authors (9)
Author's Published Works

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Accurate similarity analysis and computing of Gaussian membership functions for FNN simplification

Wei Li;Junfei Qiao;Xiao-Jun Zeng

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
This paper provides a complete solution for the problem how to accurately compute the similarity between fuzzy sets with Gaussian membership functions, which is a fundamental issue for the identification and simplification of FNNs. It is shown that there are three different types of similarities between a pair of Gaussian membership functions dependent on the relative positioning between the given...Show More

Accurate similarity analysis and computing of Gaussian membership functions for FNN simplification

Wei Li;Junfei Qiao;Xiao-Jun Zeng

Year: 2015 | Conference Paper |

Controlling bloating using depth constraint crossover

Geng Li;Xiao-Jun Zeng

2010 UK Workshop on Computational Intelligence (UKCI)
Year: 2010 | Conference Paper |
We develop a simple modification of crossover called depth constraint crossover to control bloating. As the name suggests, depth constraint crossover adds a depth constraint on the selection of crossover point. This method is motivated by the analysis of removal bias bloating theory. In this paper, we quantitatively define removal bias as the depth difference between swapped subtrees in crossover....Show More

Controlling bloating using depth constraint crossover

Geng Li;Xiao-Jun Zeng

Year: 2010 | Conference Paper |

An incremental construction learning algorithm for identification of T-S Fuzzy Systems

Di Wang;Xiao-Jun Zeng;John A. Keane

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)
Year: 2008 | Conference Paper |
Cited by: Papers (4)
This paper proposes an incremental construction learning algorithm for identification of T-S Fuzzy Systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. Beginning with a simplest T-S fuzzy system, the algorithm develops the system structure by adding more fuzzy terms and rules to reduce the model errors in a ‘greedy’ way. The main features of the proposed ...Show More

An incremental construction learning algorithm for identification of T-S Fuzzy Systems

Di Wang;Xiao-Jun Zeng;John A. Keane

Year: 2008 | Conference Paper |

Fuzzy Systems Approach to Approximation and Stabilization of Conventional Affine Nonlinear Systems

Xiao-Jun Zeng;J.A. Keane;Di Wang

2006 IEEE International Conference on Fuzzy Systems
Year: 2006 | Conference Paper |
Cited by: Papers (18)
This paper investigates the stabilization of conventional nonlinear systems by fuzzy control approach. Firstly, it is shown that the class of nonlinear systems whose stabilization problem can be solved by the fuzzy control approach available today based on T-S fuzzy control models is affine nonlinear systems as this is the only class of nonlinear systems which can be approximated to any degree of ...Show More

Fuzzy Systems Approach to Approximation and Stabilization of Conventional Affine Nonlinear Systems

Xiao-Jun Zeng;J.A. Keane;Di Wang

Year: 2006 | Conference Paper |

Learning for Hierarchical Fuzzy Systems Based on the Gradient-Descent Method

Di Wang;Xiao-Jun Zeng;J.A. Keane

2006 IEEE International Conference on Fuzzy Systems
Year: 2006 | Conference Paper |
Cited by: Papers (5)
Standard fuzzy systems suffer the "curse of dimensionality" which has become the bottleneck when applying fuzzy systems to solve complex and high dimensional application problems. This curse of dimensionality results in a larger number of fuzzy rules which reduces the transparency of fuzzy systems. Furthermore too many rules also reduce the generalization capability of fuzzy systems. Hierarchical ...Show More

Learning for Hierarchical Fuzzy Systems Based on the Gradient-Descent Method

Di Wang;Xiao-Jun Zeng;J.A. Keane

Year: 2006 | Conference Paper |

Approximation capabilities of hierarchical hybrid systems

Xiao-Jun Zeng;J.A. Keane

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
Year: 2006 | Volume: 36, Issue: 5 | Journal Article |
Cited by: Papers (12)
This paper investigates the approximation capabilities of hierarchical hybrid systems, which are motivated by research in hierarchical fuzzy systems, hybrid intelligent systems, and modeling of model partly known systems. For a function (system) with known hierarchical structure (i.e., one that can be represented as a composition of some simpler and lower dimensional subsystems), it is shown that ...Show More

Approximation capabilities of hierarchical hybrid systems

Xiao-Jun Zeng;J.A. Keane

Year: 2006 | Volume: 36, Issue: 5 | Journal Article |

Separable Approximation Property of Hierarchical Fuzzy Systems

Xiao-Jun Zeng;J.A. Keane

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.
Year: 2005 | Conference Paper |
Cited by: Papers (4)
This paper discusses the capabilities of standard hierarchical fuzzy systems to approximate continuous functions with natural hierarchical structure. The separable approximation property of hierarchical fuzzy systems is proved, that is, the construction of a hierarchical fuzzy system with required approximation accuracy can be achieved by the separate construction of each sub-system with required ...Show More

Separable Approximation Property of Hierarchical Fuzzy Systems

Xiao-Jun Zeng;J.A. Keane

Year: 2005 | Conference Paper |

Approximation capability analysis of hierarchical Takagi-Sugeno fuzzy systems

Xiao-Jun Zeng;J.A. Keane

2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542)
Year: 2004 | Conference Paper |
Cited by: Papers (1)
Based on the approach of hierarchical structure analysis of continuous functions, this paper discusses the approximation capacities of hierarchical Takagi-Sugeno fuzzy systems. By first introducing the concept of the natural hierarchical structure, it is proved that continuous functions with the natural hierarchical structure can be naturally and effectively approximated by hierarchical fuzzy syst...Show More

Approximation capability analysis of hierarchical Takagi-Sugeno fuzzy systems

Xiao-Jun Zeng;J.A. Keane

Year: 2004 | Volume: 3 | Conference Paper |

Knowledge bounded least squares method for the identification of fuzzy systems

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
Year: 2003 | Volume: 33, Issue: 1 | Journal Article |
Cited by: Papers (12)
This paper presents the knowledge bounded least squares method that uses both linguistic information (i.e., human knowledge and experience) and numerical data to identify fuzzy models. Based on the concept of fuzzy interval systems, the basic idea of this method is: first, to utilize all the available linguistic information to obtain a fuzzy interval system and then use the obtained fuzzy interval...Show More

Knowledge bounded least squares method for the identification of fuzzy systems

Xiao-Jun Zeng;M.G. Singh

Year: 2003 | Volume: 33, Issue: 1 | Journal Article |

Design and implementation of a turbo decoder for 3G W-CDMA systems

Xiao-Jun Zeng;Zhi-Liang Hong

IEEE Transactions on Consumer Electronics
Year: 2002 | Volume: 48, Issue: 2 | Journal Article |
This paper presents the design and hardware implementation of a log-MAP turbo decoder used in the third generation (3G) mobile communication W-CDMA systems. The decoding algorithm is highly data dominated and needs many memories for data storing. The memory requirements are systematically investigated and optimized from two aspects: (1) an alternative called state metric difference-storing method ...Show More

Design and implementation of a turbo decoder for 3G W-CDMA systems

Xiao-Jun Zeng;Zhi-Liang Hong

Year: 2002 | Volume: 48, Issue: 2 | Journal Article |

Fuzzy bounded least squares method for the identification of fuzzy systems

Xiao-Jun Zeng;M.G. Singh

Proceedings of 6th International Fuzzy Systems Conference
Year: 1997 | Conference Paper |
Cited by: Papers (1)
This paper presents the fuzzy bounded least squares method which uses both linguistic information and numerical data to identify fuzzy models. Based on the concept of fuzzy interval systems, the basic idea of this method is: first, to utilize all the available linguistic information to obtain a fuzzy interval system and then to use the fuzzy interval system to give the admissible model set (i.e. t...Show More

Fuzzy bounded least squares method for the identification of fuzzy systems

Xiao-Jun Zeng;M.G. Singh

Year: 1997 | Volume: 1 | Conference Paper |

Decomposition property of fuzzy systems and its applications

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Fuzzy Systems
Year: 1996 | Volume: 4, Issue: 2 | Journal Article |
Cited by: Papers (38)
This paper presents the decomposition property of fuzzy systems using a simple, constructive, decomposition procedure. That is, by properly dividing the input space into sub-input spaces, a general fuzzy system is decomposed into several sub-fuzzy systems which are the simplest fuzzy systems in the sub-input spaces. Based on the decomposition property of fuzzy systems, the analysis of fuzzy system...Show More

Decomposition property of fuzzy systems and its applications

Xiao-Jun Zeng;M.G. Singh

Year: 1996 | Volume: 4, Issue: 2 | Journal Article |

Fuzzy bounded least squares method for systems identification

Xiao-Jun Zeng;M.G. Singh

Proceedings of 28th Southeastern Symposium on System Theory
Year: 1996 | Conference Paper |
This paper presents the fuzzy bounded least squares method which uses both linguistic information and numerical data to identify linear systems. This method introduces a new type of fuzzy system, i.e., a fuzzy interval system. The steps in the method are: first, to utilize all the available linguistic information to obtain a fuzzy interval system and then to use the fuzzy interval system to give t...Show More

Fuzzy bounded least squares method for systems identification

Xiao-Jun Zeng;M.G. Singh

Year: 1996 | Conference Paper |

Comments on Singh and Zeng: "Approximation theory of fuzzy systems-SISO case" [with reply]

F. Watkins;Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Fuzzy Systems
Year: 1996 | Volume: 4, Issue: 1 | Journal Article |
Cited by: Papers (2)
The author comments on the paper by Singh and Zeng (see ibid., vol.2, no.2, p.162-76, 1994). He states that every bounded function f: R/spl rarr/R has an exact representation as an additive fuzzy system. If f is not constant, one fuzzy set and two rules define the system. Otherwise, a single rule suffices. This result shows that the approximation properties of one-input fuzzy systems derive solely...Show More

Comments on Singh and Zeng: "Approximation theory of fuzzy systems-SISO case" [with reply]

F. Watkins;Xiao-Jun Zeng;M.G. Singh

Year: 1996 | Volume: 4, Issue: 1 | Journal Article |

Approximation accuracy analysis of fuzzy systems as function approximators

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Fuzzy Systems
Year: 1996 | Volume: 4, Issue: 1 | Journal Article |
Cited by: Papers (179)
This paper establishes the approximation error bounds for various classes of fuzzy systems (i.e., fuzzy systems generated by different inferential and defuzzification methods). Based on these bounds, the approximation accuracy of various classes of fuzzy systems is analyzed and compared. It is seen that the class of fuzzy systems generated by the product inference and the center-average defuzzifie...Show More

Approximation accuracy analysis of fuzzy systems as function approximators

Xiao-Jun Zeng;M.G. Singh

Year: 1996 | Volume: 4, Issue: 1 | Journal Article |

Approximation properties of fuzzy systems generated by the min inference

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Year: 1996 | Volume: 26, Issue: 1 | Journal Article |
Cited by: Papers (18)
This paper discusses the approximation properties of fuzzy systems generated by the min inference. Firstly, the paper analyzes the properties of fuzzy basis functions (FBFs); Then based on the properties of FBPs, several basic approximation properties concerning approximation mechanisms, uniform approximation bounds, uniform convergency, and universal approximation are obtained. Further, the simil...Show More

Approximation properties of fuzzy systems generated by the min inference

Xiao-Jun Zeng;M.G. Singh

Year: 1996 | Volume: 26, Issue: 1 | Journal Article |

A relationship between membership functions and approximation accuracy in fuzzy systems

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Year: 1996 | Volume: 26, Issue: 1 | Journal Article |
Cited by: Papers (24)
This paper presents a relationship between membership functions and approximation accuracy in fuzzy systems. This relationship suggests an idea to design membership functions such that the approximation accuracy of fuzzy systems is improved.Show More

A relationship between membership functions and approximation accuracy in fuzzy systems

Xiao-Jun Zeng;M.G. Singh

Year: 1996 | Volume: 26, Issue: 1 | Journal Article |

Approximation theory of fuzzy systems-MIMO case

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Fuzzy Systems
Year: 1995 | Volume: 3, Issue: 2 | Journal Article |
Cited by: Papers (282)
In this paper, the approximation properties of MIMO fuzzy systems generated by the product inference are discussed. We first give an analysis of fuzzy basic functions (FBF's) and present several properties of FBF's. Based on these properties of FBF's, we obtain several basic approximation properties of fuzzy systems: 1) basic approximation property which reveals the basic approximation mechanism o...Show More

Approximation theory of fuzzy systems-MIMO case

Xiao-Jun Zeng;M.G. Singh

Year: 1995 | Volume: 3, Issue: 2 | Journal Article |

Approximation theory of fuzzy systems/spl minus/SISO case

Xiao-Jun Zeng;M.G. Singh

IEEE Transactions on Fuzzy Systems
Year: 1994 | Volume: 2, Issue: 2 | Journal Article |
Cited by: Papers (228)
In this paper, the approximation problem of SISO fuzzy systems is discussed. Based on the fact that fuzzy systems can be represented by a linear combination of fuzzy basic functions (FBF's), we first give a systematic and detailed analysis of FBF's and present five properties of FBF's: structure similarity and compatibility between the membership functions and FBF's, complementarity and less fuzzi...Show More

Approximation theory of fuzzy systems/spl minus/SISO case

Xiao-Jun Zeng;M.G. Singh

Year: 1994 | Volume: 2, Issue: 2 | Journal Article |

Approximation properties of fuzzy systems

Xiao-Jun Zeng;M.G. Singh

Proceedings of IEEE International Conference on Systems, Man and Cybernetics
Year: 1994 | Conference Paper |
This paper discusses the approximation properties of fuzzy systems generated by the min inference. Firstly the paper analyzes the fuzzy basis functions (FBFs) and presents several properties of FBFs. Then based on the properties of FBFs, several basic approximation properties concerning approximation mechanism, uniform approximation bounds, uniform convergency, and universal approximation are obta...Show More

Approximation properties of fuzzy systems

Xiao-Jun Zeng;M.G. Singh

Year: 1994 | Volume: 1 | Conference Paper |

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