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International Conference on Computer Science and Education (CSE 2011)

作者： Wu, Jui-Yu Lunghwa Univ Sci & Technol Dept Business Adm Tao Yuan Taiwan

ISBN: (纸本)9783642224171

While lacking previous knowledge, general users often have difficulty in using nonlinear statistical time series methods (e.g., bilinear model, threshold autoregressive model, smoothing transition autoregressive model, autoregressive conditional heteroscedastic model and generalized autoregressive conditional heteroscedastic model) and spectral analytical approaches. Moreover, standard back-propagation neural networks (BPNNs) using the steepest descent algorithm have the disadvantage that they converge to local optima. To overcome the above limitations, this work develops a self-organizing polynomial neural network (SOPNN) based on the group method of data handling (GMDH) algorithm, which is a statistical learning network. The proposed SOPNN scheme can create an optimal network topology by using an evolutionary process and is easily to be used. Performance of the proposed SOPNN scheme is also measured by using the Mackey-Glass chaotic time series problem. Additionally, numerical results of the proposed SOPNN scheme are compared with those of conventional BPNN, cerebellar model articulation controller NN (CMAC NN), advanced simulated annealing-based BPNN (ASA-BPNN) and artificial immune algorithm-based BPNN (AIA-BPNN). Experimental results indicate that the optimum training and test capabilities of the proposed SOPNN are superior to those of a conventional BPNN. Furthermore, the generalization capability and computational CPU time of the proposed SOPNN scheme are competitive for those of CMAC NN, AIA-BPNN and ASA-BPNN.

关键词： group method of data handling cerebellar model articulation controller back-propagation neural network artificial immune algorithm chaotic time series

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Research on the Evolutionary Strategy Based on AIS and Its Application on Numerical Integration

Research on the Evolutionary Strategy Based on AIS and Its A...

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International Conference on Intelligent Computing and Information Science

作者： Bei, Li Henan Univ Urban Construct Dept Comp Sci & Engn Pingdingshan 467001 Henan Peoples R China

ISBN: (纸本)9783642181337

Based on the features of artificial immune system, a new evolutionary strategy is proposed in order to calculate the numerical integration of functions. This evolutionary strategy includes the mechanisms of swarm searching and constructing the fitness function. Finally, numerical examples are given for verifying the effectiveness of evolutionary strategy. The results show that the performance of evolutionary strategy is satisfactory and more accurate than traditional methods of numerical integration, such as trapezoid formula and Simpson formula.

关键词： artificial immune algorithm evolutionary strategy numerical integration trapezoid formula Simpson formula

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Parallel artificial immune clustering algorithm based on granular computing

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11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007)

作者： Xie, Keming Hao, Xiaoli Xie, Jun Taiyuan Univ Technol Coll Informat Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Comp Sci Taiyuan 030024 Peoples R China

ISBN: (纸本)9783540725299

When samples number, classification and dimension of clustering are much more, traditional clustering algorithm usually leads to unharmonious character between clustering and transcendent knowledge. Therefore, a new clustering algorithm is proposed, which is parallel artificial immune clustering algorithm based on granular computing. artificial immune system model has the characteristics, such as parallel, random searching and maintaining diversity, which can solve premature problem in latter evolution and converge to a global optimization solution faster. Besides, we unite it to dynamic granulation model and apply granulation description to clustering. In the process of granulation changing, we can choose appropriate granulation size by adjusting to ensure clustering efficiency and quality: Tests show that the algorithm is more effective and more reasonable when we handle clustering of some data with it.

关键词： granular computing artificial immune algorithm clustering harmony degree

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Self-Organizing Polynomial Neural Network for Forecasting Chaotic Time Series: Comparison Results

Self-Organizing Polynomial Neural Network for Forecasting Ch...

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Advances in Information Technology and Education

作者： Jui-Yu Wu Department of Business Administration Lunghwa University of Science and Technology

While lacking previous knowledge, general users often have difficulty in using nonlinear statistical time series methods(e.g., bilinear model, threshold autoregressive model, smoothing transition autoregressive model, autoregressive conditional heteroscedastic model and generalized autoregressive conditional heteroscedastic model) and spectral analytical approaches. Moreover, standard back-propagation neural networks(BPNNs) using the steepest descent algorithm have the disadvantage that they converge to local optima. To overcome the above limitations, this work develops a self-organizing polynomial neural network(SOPNN) based on the group method of data handling(GMDH) algorithm, which is a statistical learning network. The proposed SOPNN scheme can create an optimal network topology by using an evolutionary process and is easily to be used. Performance of the proposed SOPNN scheme is also measured by using the Mackey-Glass chaotic time series problem. Additionally, numerical results of the proposed SOPNN scheme are compared with those of conventional BPNN, cerebellar model articulation controller NN(CMAC NN), advanced simulated annealing-based BPNN(ASABPNN) and artificial immune algorithm-based BPNN(AIA-BPNN). Experimental results indicate that the optimum training and test capabilities of the proposed SOPNN are superior to those of a conventional BPNN. Furthermore, the generalization capability and computational CPU time of the proposed SOPNN scheme are competitive for those of CMAC NN, AIA-BPNN and ASA-BPNN.

关键词： group method of data handling cerebellar model articulation controller back-propagation neural network artificial immune algorithm chaotic time series

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Image recognition technology in rotating machinery fault diagnosis based on artificial immune

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SMART STRUCTURES AND SYSTEMS 2010年第4期6卷 389-403页

作者： Zhu Dachang Feng Yanping Chen Qiang Cai Jinbao Jiangxi Univ Sci & Technol Coll Mech & Elect Engn Ganzhou Peoples R China Jiangxi Univ Sci & Technol Fac Foreign Studies Ganzhou Peoples R China

By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor's normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It's demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

关键词： rotating machinery fault diagnosis image recognition technology artificial immune algorithm

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Designing an artificial immune System-Based Machine Learning Classifier for Medical Diagnosis 1

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International Conference on Information Computing and Applications

作者： Cheng, Hui-Ping Lin, Zheng-Sheng Hsiao, Hsiao-Fen Tseng, Ming-Lang MingDao Univ Dept Business Innovat & Dev Peetow 52345 Changhua Taiwan MingDao Univ Dept Finance Peetow 52345 Changhua Taiwan

ISBN: (数字)9783642161674

ISBN: (纸本)9783642161667

The purpose of this paper is to develop an efficient approach to improve medical diagnosis performance of breast cancer. First, the medical dataset of breast cancer is selected from UCI Machine Learning Repository. After that, the standardization and normalization of datasets are pre-processing procedure. Secondly, the proposed approach combines support vector machine with artificial immune system as the medical diagnosis classifier. The results of diagnosis are identified and the rates of classification accuracy are evaluated. A simple artificial immune algorithm with various affinity criteria is investigated for comparison. Furthermore, the grid-search with 10-fold cross-validation is applied to choose two parameters of C and gamma for AIS-based machine learning classifier. Through grid-search technique, the proposed classifier could yield the best results.

关键词： artificial immune algorithm support vector machine grid-search distance measure medical diagnosis

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Permutation flowshops with transportation times: mathematical models and solution methods

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期46卷 631-647页

作者： Naderi, B. Javid, A. Ahmadi Jolai, F. Amir Kabir Univ Technol Dept Ind Engn Tehran Iran Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

This paper deals with permutation flowshops with considering transportation times of carrying semi-finished jobs from a machine to another one. The transportation between machines can be done using two types of transportation systems: multi-transporter and single-transporter systems. We formulate the problem with both systems as six different mixed integer linear programs. We also provide solution methods including heuristics and metaheuristics in order to solve large-sized problems. The heuristics are the adaptations of well-known heuristics and the proposed metaheuristics are based on artificial immune systems incorporating an effective local search heuristic and simulated annealing. A comprehensive experiment is conducted to compare and evaluate the performance of the models as well as the algorithms. All the results show the effectiveness of the proposed models and algorithms.

关键词： Scheduling Permutation flowshop Transportation time Mathematical model Mixed integer linear programming Heuristic artificial immune algorithm

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QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2010年第4期33卷 512-522页

作者： Cheng, Hui Wang, Xingwei Yang, Shengxiang Huang, Min Cao, Jiannong Sun Yat Sen Univ Dept Comp Sci Guangzhou 510006 Guangdong Peoples R China Univ Leicester Dept Comp Sci Leicester LE1 7RH Leics England Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Optical Internet QoS Multicast Ant colony optimization artificial immune algorithm

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NUMERICAL NEGATIVE SELECTION algorithm

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INTERNATIONAL JOURNAL ON artificial INTELLIGENCE TOOLS 2010年第5期19卷 703-712页

作者： Cai Tao Ju Shiguang Niu Dejiao Jiangsu Univ Dept Comp Zhenjiang 212013 Jiangsu Peoples R China

The artificial immune algorithm is the hot topic in much research such as the intrusion detection system, the information retrieval system and the data mining system. The negative selection algorithm is the typical artificial immune algorithm. A common representation of binary strings for antibody (detector) and antigen have been associated with inefficiencies when generating detector and inspecting antigen. We use a single integer to represent the detector and provide the basis of improving negative selection algorithm efficiency. In the detector generation algorithm, extracting sub-strings in self that its length is larger than threshold and converting them to single integer in numerical interval, then the rest integers in numerical interval are selected as numerical detectors. It can reduce the time and space overhead of detector generation and provide the facility to analyze the positive and negative errors when antigen inspection. The numerical matching rule is given. The B-tree is used to create index of numerical detector. Extracting sub-strings in antigen that its length is larger than threshold and converting them to some integers. If there is the same value as those integers in the index of numerical detector, then the antigen matches one numerical detector. It can improve the efficiency of antigen inspection. Finally the prototype of the numerical negative selection algorithm and negative selection algorithm are realized to test the overhead of the detector generation and antigen inspection using the live data set. The results show that the numerical negative selection algorithm can reduce the time and space overhead and avoid fluctuation of the overhead.

关键词： Negative selection algorithm artificial immune algorithm

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A New immune Genetic algorithm

A New Immune Genetic Algorithm

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Lu, Yan Wu, Xiangting Dai, Ran Xia, Guanglei Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781424455690

The application of genetic algorithm is widely, but it is easy to premature convergence and is inadequate about the local searching optimization ability. In this paper, a new immune genetic algorithm (IGA) is proposed. Experiments are done to compare the proposed algorithm with the standard GA, and the results indicate that the proposed IGA's optimization results and converging speed are superior and the proposed IGA overcomes the premature convergence and solves the problem of falling into local optimum solution easily. This paper also combines TSP's encoding characteristics to propose a new crossover operator (DEGX). Experiments shows the DEGX can enhance the local search ability greatly.

关键词： genetic algorithm immune genetic algorithm artificial immune algorithm vaccine

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