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International Conference on Computational Materials Science (CMS 2011)

作者： Huang Yourui Hu Haibo Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan Peoples R China

ISBN: (纸本)9783037851555

PID control scheme has been widely used in most of control system. The method of design PID controller is mature gradually. Due to the controlled object is nonlinear and time-varying, so the integer PID controller can not achieve the desired effect. After study people found that the application of fractional order PID controllers can solve the problem of time-varying and nonlinear very well and the controller has high control precision. Currently, the method of design fractional order PID controllers is little. This article describes an artificial immune algorithm and using MATLAB for simulation, the simulation results demonstrate that the artificial immune algorithm has little error and high optimization speed than traditional optimization algorithm, and the fractional order PID controller has a better control effect than traditional integer PID controller.

关键词： Fractional order PID controller artificial immune algorithm Parameters optimizing

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Hybrid artificial immune algorithm and CMAC Neural Network Classifier for Supporting Business and Medical Decision Making

Hybrid Artificial Immune Algorithm and CMAC Neural Network C...

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7th International Conference on Advanced Data Mining and Applications

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

ISBN: (纸本)9783642258558

Decision making that involves credit scoring and medical diagnosis can be considered to solve classification problems. Among the many data miming (DM) methods that have been developed to solve these classification problems are neural network (NN) and support vector machine (SVM) classifiers. Despite their successful application to classification problems, these classifiers are limited, in that users must use trial-and error to modify specific parameter settings. Fortunately, the setting of the parameters for those classifiers can be viewed as an unconstrained global optimization problem. To overcome this limitation of those classifiers, this work develops an advanced DM method that combines an artificial immune algorithm (AIA) and a MIMO cerebellar model articulation controller NN (CMAC NN) classifier (AIA-MIMO CMAC NN classifier). The AIA is a stochastic global optimization method and its parameters are easily set. The proposed CMAC NN classifier is characterized by its fast learning, reasonable generalization ability and robust noise resistance. The proposed AIA-MIMO CMAC NN classifier uses an outer AIA to optimize the parameter settings of an inner MIMO CMAC NN classifier, which is used to solve classification problems. The performance of the proposed classifier is also evaluated using a set of real-world classification problems, such as credit scoring and medical diagnosis. Moreover, this work compares the numerical results obtained using the proposed AIA-MIMO CMAC NN classifier with those obtained using published classifiers (such as SVM, SVM-based classifiers, NN classifiers and C4.5). Experimental results indicate that the classification accuracy of the proposed AIA-MIMO CMAC NN classifier is superior to those of some published classifiers. Hence, the AIA-MIMO CMAC NN classifier can be viewed an alternative DM method for supporting business and medical decision making.

关键词： data mining classification artificial immune algorithm neural network classifier cerebellar model articulation controller

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Study of Feature Selection for the Stored-grain Insects Based on artificial immune algorithm

Study of Feature Selection for the Stored-grain Insects Base...

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Zhang, Hongtao Hu, Yuxia North China Univ Water Conservancy & Elect Power Sch Elect power Zhengzhou Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China

ISBN: (纸本)9781424451944

The feature subset selection is critical for the realtime detection of the stored-grain insects based on image recognition technology. This study proposed a feature selection method based on artificial immune algorithm immune (MA). The single objective affinity evaluation function was developed to evaluate the feature subset by introducing the v-fold cross validation training model accuracy and the number of the selected features. The feature subset selection for the nine species of the insects of wheat was conducted from the 17 morphological features by using AIA. Compared with the feature selection method based on genetic algorithm, AIA can further decrease the size of the feature subset and improve the classification accuracy. By using the 7 features selected based on AIA, the SVM classifier whose parameters had been optimized classified over 95.5% of the ninety image samples of the insects. The results show that it is practical and feasible.

关键词： stored-grain insects artificial immune algorithm feature selection support vector machine

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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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Trajectory Planning for UAV Based on Improved artificial immune algorithm 2

Trajectory Planning for UAV Based on Improved Artificial Imm...

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2nd International Conference on algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Ye, Zi Yi, Zihao Li, Yuxuan School of Information Engineering Wuhan University of Technology Wuhan China School of Science Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350390254

Our study centers on the multi-UAV multi-store delivery problem and considers the power problem of the UAV for the charging setup of the UAV. In our study, we developed an optimized mathematical model for the problem background and setting, and we selected an artificial immune algorithm to solve the problem with reference to existing studies. In the solution, we proposed equal-length encoding and IPOX crossover operation for the encoding problem of the solution vector, and based on the traditional algorithm, we proposed a two-population strategy to improve the traditional algorithm in order to improve the information interaction of the populations and to jump out of the local optimum, and we introduced an adaptive variation probability to enhance the convergence of the algorithm, and finally, we compared the improved algorithm with the traditional algorithm and came up with the results of the effectiveness and the improvement of the traditional algorithm in solving the problem. We finally compare the improved algorithm with the traditional algorithm and conclude the effectiveness of our improvement on the problem and its superiority over the traditional algorithm. © 2024 IEEE.

关键词： adaptive variation probability artificial immune algorithm IPOX crossover two-population strategy

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Applying artificial immune algorithm to explor the seasonal effect in the stock market

Applying artificial immune algorithm to explor the seasonal ...

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International Conference on Software Intelligence Technologies and Applications and International Conference on Frontiers of Internet of Things, 2014

作者： Chiu, Deng-Yiv Zhou, Jun-Yan Wang, Zhi-Cheng Department of Information Management Chung Hua University WuFu Rd. Hsinchu Taiwan

ISBN: (纸本)9781849199704

The cyclic time effect, such as January effect or weekend effect, is well documented in literature. Therefore, it is very important to consider the seasonality dynamism. We propose a hybrid approach of artificial immune algorithm, support vector regression, and seasonal moving window to explore stock quarterly seasonality dynamism among same seasons in continuous years.

关键词： artificial immune algorithm Moving window Vector support regression

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An artificial immune algorithm based on bidding of power market

An artificial immune algorithm based on bidding of power mar...

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International Conference on Power System Technology

作者： Li, MJ Tang, Z Changsha Univ Elect Power Dept Elect Engn Changsha 410077 Peoples R China

ISBN: (纸本)0780374592

This paper presents an artificial immune algorithm simulating the biological immune systems with self-adjustment function, and introduces its basic principle. For the bidding in power markets, this paper gives computation steps of the artificial immune algorithm. The artificial immune algorithm can simultaneously solve unit commitment and economic load distribution, and can expediently handle constraints of bidding in,power markets. So the artificial immune algorithm is an effective method of the bidding in power markets.

关键词： artificial immune algorithm bidding algorithm power markets

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artificial immune algorithm Convergence Rate Estimation Based on Stochastic Process Theory

Artificial Immune Algorithm Convergence Rate Estimation Base...

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第30届中国控制与决策会议

作者： Hong Lu Chu Yu Xiao Wen Department of Electronic Engineering Huaihai Institute of Technology Marine Resources Development Institute of Jiangsu School of Information Technology Monash University

The stochastic process theory is utilized to replace the traditional eigenvalue estimation method of the state transition matrix so as to study the convergence rate of the real coded artificial immune algorithm. On the premise of satisfying the necessary condition for probability strong convergence of artificial immune algorithm, this method regards it as the sufficient condition for the general artificial immune algorithm and proposes a new method for estimating the exponential velocity probability strong convergence of real coded artificial immune algorithm. This method takes the final convergence of the optimum antibody in the population and avoids the deficiency of the conservative property of traditional estimation method. It can estimate the convergence property and convergence rate of a class of artificial immune algorithm. In the practical application of artificial immune algorithm, it has a certain theoretical reference value in optimizing the convergence rate.

关键词： artificial immune algorithm Convergence rate estimation Stochastic Process Probability strong convergence

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Optimization of thread partitioning parameters in speculative multithreading based on artificial immune algorithm

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Frontiers of Information Technology & Electronic Engineering 2015年第3期16卷 205-216页

作者： Yu-xiang LI Yin-liang ZHAO Bin LIU Shuo JI Department of Computer Science and Technology Xi'an Jiaotong University

Thread partition plays an important role in speculative multithreading （SpMT） for automatic parallelization of ir- regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap- plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold （DT）, lower limit of thread size （TSL）, upper limit of thread size （TSU）, lower limit of spawning distance （SDL）, and upper limit of spawning distance （SDU）. Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com- binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm （AIA） to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench- marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00% on a 4-core platform compared with a machine learning based approach, and 8.92% compared with a heuristics-based approach.

关键词： Speculative multithreading Thread partitioning artificial immune algorithm

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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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