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6th International Symposium on Instrumentation and Control Technology

作者： Hao Xiaoli Xie Keming Taiyuan Technol Univ Taiyuan 030024 Shanxi Peoples R China

ISBN: (纸本)081946452X

When employed in fault diagnosis, rough set can realize attribution reduction. But it can not discrete attribution and reduct attribution simultaneously, therefore we can not say that it can automatically extract rules. To solve the problem, a new rule extraction method based on developed artificial immune algorithm is firstly proposed in the paper. At first, a new method of encoding is produced which can make the process of discretion and reduction unify. Secondly, a new definition of concentration of antibodies not only compare individuals in structure and space, but also in fitness value. Thirdly, the algorithm provide dissimilation operator and similar-taxis operator, which replace choice, expansion and mutation in traditional artificial immune algorithm. All these developments not only maintain diversity of the antibody population, but also converge faster. Finally, we apply the algorithm to fault diagnose of heat recoup system in steam turbine. Tests proved that the algorithm is feasible, and the diagnose rules acquired by the algorithm have higher accuracy rate.

关键词： artificial immune algorithm fault diagnose rule extraction steam turbine

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Tracking Extrema in Dynamic Environments Using a Learning Automata-Based immune algorithm

Tracking Extrema in Dynamic Environments Using a Learning Au...

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International Conference on Grid and Distributed Computing/International Conference on Control and Automation

作者： Rezvanian, Alireza Meybodi, Mohammad Reza Islamic Azad Univ Hamedan Branch Dept Comp Engn Tehran Iran Amirkabir Univ Technol Dept Comp & IT Engn Tehran Iran

ISBN: (纸本)9783642176241

In recent years, bio-inspired algorithms have increasingly been used by researchers for solving various optimization problems increasingly. Many real world problems are mostly time varying optimization problems, which require special mechanisms for detecting changes in environment and then responding to them. The present paper has been proposed to combination the learning automata and artificial immune algorithm in order to improve the performance of immune system algorithm in dynamic environments. In the proposed algorithm, the immune cells are equipped with a learning automaton. So they can increase diversity in response the dynamic environments. Learning automata based immune algorithm for dynamic environment has been tested in the moving parabola as a popular standard dynamic environment and compared by several famous algorithms in dynamic environments.

关键词： artificial immune algorithm Learning Automata Dynamic Environments Time Varying Problems

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Optimized Approach to Architecture Thermal Comfort in Hot Summer and Warm Winter Zone

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International Conference on Life System Modeling and Simulation / International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS-ICSEE)

作者： Huang, Xianfeng Lu, Yimin Guangxi Univ Educ Minist Key Lab Disaster Prevent & Struct Saf 100 Daxue Rd Nanning 530004 Peoples R China Guangxi Univ Coll Civil Engn & Architecture Nanning 530004 Peoples R China Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China

ISBN: (纸本)9783642158520

According to the Fanger thermal comfort equations and the advantage of artificial immune algorithm in solving combinatorial optimization to engineering problems, the artificial immune algorithm has been applied to the thermal design and optimization of parameters for the HVAC (Heating, Ventilation, and Air Conditioner) within a building. Then, aiming at climate characteristics of high temperature and humidity in hot summer and warm winter zone, the interior thermal comfort objective function is deduced. Then, the preferable range of indoor air temperatures and air velocities which meet the thermal comfort requirements under different activities and humidity is obtained by simulation. Therefore, the relationship between indoor thermal comfort and energy saving to a building is also managed to reveal. Furthermore, it is evidence that the proposed method in this paper should be employed in the criteria for thermal design to a building and control of an air conditioning system.

关键词： artificial immune algorithm Thermal Comfort Optimization Hot Summer and Warm Winter Zone

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An artificial immune Univariate Marginal Distribution algorithm

An Artificial Immune Univariate Marginal Distribution Algori...

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4th International Symposium on Intelligence Computation and Applications

作者： Zhang, Qingbin Kang, Shuo Gao, Junxiang Wu, Song Tian, Yanping Shijiazhuang Inst Railway Technol Shijiazhuang 050041 Peoples R China Hebei Med Univ Second Hosp Shijiazhuang 050000 Peoples R China

ISBN: (纸本)9783642049613

Hybridization is an extremely effective way of improving the performance of the Univariate Marginal Distribution algorithm (UMDA). Owing to its diversity and memory mechanisms, artificial immune algorithm has been widely used to construct hybrid algorithms with other optimization algorithms. This paper proposes a hybrid algorithm which combines the UMDA with the principle of general artificial immune algorithm. Experimental results on deceptive function of order 3 show that the proposed hybrid algorithm can get more building blocks (BBs) than the UMDA.

关键词： UMDA artificial immune algorithm hybridization

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A kind of two-dimensional entropic image segmentation method based on artificial immune algorithm

A kind of two-dimensional entropic image segmentation method...

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6th World Congress on Intelligent Control and Automation

作者： Li, Youxin Mao, Zongyuan Tian, Lianfang Tan, Guangxing South China Univ Technol Coll Automat Sci & Technol Guangzhou 510640 Guangdong Peoples R China Guangdong Univ Technol Coll Informat Engn Guangzhou 510643 Guangdong Peoples R China

ISBN: (纸本)1424403316

Two-dimensional entropic segmentation method has been greatly developed because of high segmentation accuracy and good stability, while a hard problem is that it gives rise tot eh exponential increment of computational time in comparison with the traditional one-dimensional histogram partition technology. To solve this problem, a new kind of image thresholding method is presented based on the combination of the artificial immune algorithm (AIA) and two-dimensional entropy techniques in this paper. This method can effectively improve the computation time and avoid getting into local optimization of the threshold by making use of AIA's characteristics of the intelligent computation, adaptive evolution and globally optimizing. The test results show that the method is effective and practicable.

关键词： artificial immune algorithm two-dimensional entropy image segmentation

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Path Planning of Soccer Robot Based on artificial immune Mechanics

Path Planning of Soccer Robot Based on Artificial Immune Mec...

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International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

作者： Xiaosong Li Junqin Cao Chunbo Chang College of Electronics and Information Taiyuan University of Science and Technology Taiyuan China

The path planning of soccer robot based on artificial immune mechanics was proposed in detail in this paper. Adopting artificial immune mechanics, the soccer robot can find a shorter path by detecting current position, destination and obstacles, and then the robot can move from starting point to the target point and avoid the obstacles based on effective control stage. The experiment results show that the proposed artificial immune algorithm has good performance in robot path planning.

关键词： artificial immune algorithm path planning soccer robot

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Study of fault diagnosis in brushless machines based on artificial immune algorithm

Study of fault diagnosis in brushless machines based on arti...

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IEEE International Symposium on Industrial Electronics

作者： Wang Tao Liu Nian Xie Chi Sun Kejin Sichuan Univ Dept Elect Engn Chengdu Sichuan Peoples R China Sichuan Univ Dept Measurement & Control Sichuan Peoples R China

ISBN: (纸本)9781424404964

It is describes a kind of artificial immune algorithm (AIA), which is based on the nature immune system in this paper, and the basic principle of the immune system is presented. And the negative-selection algorithm is inspired by the negative-selection mechanism of the nature immune system, and it is can detect any tiny change in an actual system, so it can be used in the state monitoring and fault diagnosis. In order to increase effectively the operation reliability of the brushless machines, a new detection method has been presented to the faults diagnosis of rotating rotor rectifiers in generators with brushless excitation systems, which is based on the negative-selection algorithm. When any rotating rotor rectifier has some faults, such as one bridge arm breaking, then the rectifiers are under abnormal operating mode, and the current waveform is changed, so the signal waveform monitored is unusual. Different fault has different signal waveform, and the signal waveform can be also analyzed by taking: advantage of the negative-selection algorithm, then the fault type can be identified The development trends of the application of the negative-selection algorithm for fault diagnosis are also introduced.

关键词： brushless machines fault diagnosis optimisation rectifying circuits reliability rotors artificial immune algorithm brushless excitation systems immune system negative-selection algorithm operation reliability rotating r

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Optimized Approach to Architecture Thermal Comfort in Hot Summer and Warm Winter Zone

Optimized Approach to Architecture Thermal Comfort in Hot Su...

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2010 International Conference on Life System Modeling and Simulation & 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment

作者： Xianfeng Huang1,1 and Yimin Lu2 1 Education Ministry Key Laboratory of Disaster Prevention and Structural Safety & College of Civil Engineering and Architecture, Guangxi University, 100 Daxue Rd.,530004 Nanning, China2 College of Electrical Engineering, Guangxi University, 100 Daxue Rd., 530004 Nanning, China

关键词： artificial immune algorithm Thermal Comfort Optimization Hot Summer and Warm Winter Zone.

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Optimization of Grouping Batch and Sorting Order for Smelting Charges in Refined Copper Strip Producing by AIA

Optimization of Grouping Batch and Sorting Order for Smeltin...

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The 2010 IEEE International Conference on Progress in Informatics and Computing

作者： Chunguang Chang~(1,2), 1:Key Laboratory of Industrial Informatics Shenyang Institute of Automation,Chinese Academy of Sciences Shenyang,China Yunlong Zhu~1,Kunyuan Hu~1,Yi Zhang~1 2:School of Management Shenyang Jianzhu University Shenyang,China

Grouping batch and sorting order(GBSO) for smelting charges is a key cycle in refined copper strip producing. To improve its scientific degree,optimization problem of GBSO for smelting charges in refined copper strip producing by artificial immune algorithm(AIA) is *** multi-objective optimization model for GBSO of smelting charges is established with the objectives which includes minimizing fluctuation in ingredient ratios between the nearest neighbor charges,meeting order priority requirement as much as possible,insuring order due date as much as possible,maximizing number of same brand charges and sorting order in continuous way for same order as much as *** constraints are adequately considered, which includes order due data of each charge,sorting order limit for the charge with high quality requirement,sorting order limit for the charge with the high ratio of virtual orders,sorting order limit for the charge including multi-order,sorting order continuously for same brand charges,ingredient ratio limit for the charge which is sorted order as the first charge,ingredient ratio difference limit between each two nearest neighbor charges. To solve the model in easy way,some constraints are transformed into the objective function,and AIA is *** detail steps of AIA is designed in detail including antibody representation and encoding,affinity calculation,clone selection,antibody population *** validate the validity of above model and AIA,select practical typical GBSO problem of smelting charges as application *** result shows that,the obtained optimal solution is better than that by heuristic method both on diversity and optimization *** is suitable for solving complex problem with requirement on solution diversity.

关键词： refined copper strip charge grouping batch sorting order artificial immune algorithm

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Intelligent modelling of a batch reactor with partially unmeasurable states based upon a structure approaching hybrid neural networks

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2009年第I2期223卷 161-173页

作者： Cao, L. L. Li, X. G. Jiang, P. Wang, J. Beijing Univ Chem Technol Inst Automat Beijing 100029 Peoples R China

The success of model-based control of chemical processes is dependent on good process models. For processes that are poorly known, the generic modelling capability of neural networks offers an attractive alternative. However, for satisfactory performance, the conventional implementations of neural networks require large sets of offline data in addition to online measurement of key variables, such as concentrations. Meeting each of these requirements is often infeasible in chemical processes. By combining the structural information from a first-principles model and the virtual supervisor-artificial immune algorithm, a novel hybrid neural network, called a structure approaching hybrid neural networks (SAHNN), is proposed. The proposed approach solves the structural problem of neural models and requires a more manageable number of offline data and online key variables. The accurate prediction of online partially unmeasurable concentrations in a batch reactor demonstrated that SAHNN is a promising tool to model complicated batch processes and can be utilized as a vehicle for the control and optimization of other similar chemical reactors.

关键词： structure approaching hybrid neural networks modelling batch reactor virtual supervisor artificial immune algorithm

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