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Blending Artificial Intelligence into PID Controller Design: A Biomedical Engineering Experiment

IFAC-PapersOnLine 2016年第6期49卷 366-371页

作者： Oliveira, P. B. de Moura Cunha, J. Boaventura UTAD-University of Trás-os-Montes and Alto Douro Escola de Ciencias e Tecnologia Vila Real5000-801 Portugal INESC TEC Technology and Science Campus da FEUP Porto4200 - 465 Portugal

A teaching experiment is proposed in which an artificial intelligence technique is blended with classical control techniques to design PID controllers. The artificial intelligence technique deployed is currently considered one of the most popular and successfully nature and biological inspired metaheuristics: the particle swarm optimization algorithm. The teaching experiment is proposed for an introductory undergraduate Biomedical Engineering feedback control systems course. The mean arterial pressure control, quite relevant in practical application terms, is revisited. Moreover, another biomedical control problem is proposed for teaching/learning purposes: the minimum temperature control for intracranial tumor treatment. Simulation results concerning both classic and artificial intelligence based techniques for PID controller design are presented. © 2016

关键词： Controllers Adaptive control systems Artificial intelligence Bioinformatics Biomedical engineering Biophysics Blending Blood pressure Design Electric control equipment Feedback control particle swarm optimization (PSO) Proportional control systems swarm intelligence Teaching Three term control systems Tumors Artificial intelligence techniques Biomedical control Classical control techniques Intracranial tumors Mean arterial pressure Minimum temperatures particle swarm optimization algorithm PID controller design

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Solving Multi-objective Portfolio optimization Problem for Saudi Arabia Stock Market Using Hybrid Clonal Selection and particle swarm optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015年第8期40卷 2407-2421页

作者： Bin Shalan, Sara A. Ykhlef, Mourad King Saud Univ Dept Nat & Engn Sci Coll Appl Studies & Community Serv Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

The portfolio is a group of assets held by an institution or a private individual. Each asset has an investment share of the total investment. The investor tries to distribute the investment to these different assets. The main issue in portfolio optimization is the allocation of different assets for maximum return and minimum risk within a suitable time. These two objectives lead to the multi-objective portfolio optimization problem, which must be solved. Several previous studies have addressed this issue. In this article, we propose a new intelligence hybrid evolutionary algorithm that combines clonal selection with particle swarm optimization to optimize the portfolio's return and risk. We then show the results of the proposed solution through experiments that are conducted using stocks in Kingdom of Saudi Arabia stock exchange market (Tadawul). Moreover, we compare our hybrid algorithm, clonal selection and particle swarm optimization-based solution.

关键词： Clonal selection algorithm Hybrid evolutionary algorithm Portfolio optimization particle swarm optimization algorithm

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A New algorithm for Solving Ill-conditioned Linear System with an Auto-Parameter

A New Algorithm for Solving Ill-conditioned Linear System wi...

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Tianyu He Jinshan Wang Zonghao Tian Mathematics section Army Officer Academy

ISBN: (纸本)9781510845008

A new simple and effective method for solving ill-conditioned linear systems is presented in this paper. This method tried mainly not to decrease the error caused in direct solving, instead, it tries to transfer this error to a medium variable. At the same time, a parameter is introduced. In order to obtain the best parameter, PSO is used. An algorithm based on this method is presented, and examples show that this algorithm could solve extremely ill-conditioned linear systems correctly and stably.

关键词： particle swarm optimization algorithm auto-parameter ill-conditioned linear system

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Application research of improved K-means algorithm in network intrusion detection

Application research of improved K-means algorithm in networ...

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2017 International Conference on Education and Management Innovation(EMI2017)

作者： Gongrang Zhang Wei Hu School of Management Hefei University of Technology Anhui Transport Interconnection Management Center School of Computer & InformationHefei University of Technology

The original K-means algorithm is sensitive to the selection of the initial clustering center and unstable in the network intrusion detection application based on data mining. In this paper, the optimization ability of particle swarm optimization(PSO) was used to solve the problem that K-means algorithm is sensitive to the selection of the initial clustering center. Different global optimal solutions were obtained by PSO algorithm and used as the basis of choosing the initial clustering center of K-means clustering algorithm. Based on this idea, the K-means algorithm was improved and a network intrusion detection model was established. Experimental results show that the improved K-means clustering algorithm based on PSO has better clustering effect than original K-means clustering algorithm, and can detect more intrusion behaviors in network intrusion detection.

关键词： Network intrusion detection K-means clustering algorithm particle swarm optimization algorithm Initial clustering center

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Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems

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IFAC-PapersOnLine 2016年第3期49卷 347-352页

作者： Aoun, Oussama Sarhani, Malek El Afia, Abdellatif ENSIAS - Mohammed V University Rabat Morocco

The tuning approach consists in finding the most suitable configuration of an algorithm for solving a given problem. Machine learning methods are usually used to automate this process. They may enable to construct robust autonomous artifacts whose behavior becomes increasingly expert. This paper focuses on the restriction of this general problem to the field of air planning and more specifically the crew scheduling problem. Metaheuristics are widely used to solve this problem. Our approach consists of using hidden markov model to find the best configuration of the algorithm based on the estimation of the most likely state. The experiment consists of finding the best parameter values of the particle swarm optimization algorithm for the crew scheduling problem. Our approach has shown that it can be a promising solution for automatic optimization of airline scheduling problems. © 2016

关键词： Problem solving Air transportation Heuristic algorithms Hidden Markov models Learning systems particle swarm optimization (PSO) Scheduling Airline scheduling algorithm for solving Automatic optimization Crew scheduling problems Machine learning methods Meta heuristics Most likely particle swarm optimization algorithm

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Fault Diagnosis Analysis with Support Vector Regression and particle swarm optimization algorithm

Fault Diagnosis Analysis with Support Vector Regression and ...

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2010 Chinese Control and Decision Conference

作者： WenJie Tian,JiCheng Liu Beijing Automation Institute of Beijing Union University,Beijing,China,100101

The fault diagnosis model with support vector regression(SVR) and particle swarm optimization algorithm (POSA) for is *** novel structure model has higher accuracy and faster convergence *** construct the network structure,and give the algorithm *** impact factor of fault behaviors is *** the ability of strong self-learning and faster convergence,this fault detection method can detect various fault behaviors rapidly and effectively by learning the typical fault characteristic *** the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction,the reduces of the original dataset are calculated and used to train individual SVR for ensemble,and consequently,increase the detection *** validate the effectiveness of the proposed method,simulation experiments are performed based on the electronic circuit *** results show that the proposed method is a promised method owning to its high diversity,high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction particle swarm optimization algorithm

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Optimal Design of Water-supply Pipe Network Based on Improved particle swarm optimization algorithm Considering Reliability

Optimal Design of Water-supply Pipe Network Based on Improve...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Shen, Cheng Yu, T. -C. Zhang, T. -Q. Zhejiang Univ Dept Civil Engn Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781424467129

particle swarm optimization (PSO) algorithm is a heuristic global optimization technology based on colony intelligence. For improving the searching ability of this algorithm, a search factor is added into the movement of the particle to develop it, and the developed PSO algorithm is used for optimal design of water-supply pipe network. Reliability constraint, which is based on the principle of average distribution flux, is added into the process of optimal design to avoid producing tree pipe network for the reason of economic flux distribution. The algorithm is applied to a simple test network. Comparison with the results of basic PSO algorithm shows that the developed algorithm has stronger global optimizing ability and better search accuracy for optimal design of water-supply pipe network.

关键词： particle swarm optimization algorithm search factor water-supply pipe network reliability optimal design

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Network Intrusion Detection Analysis with Neural Network and particle swarm optimization algorithm

Network Intrusion Detection Analysis with Neural Network and...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： WenJie Tian JiCheng Liu Beijing Automation Institute of Beijing Union University Beijing China 100101

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. Utilizing the character that rough set can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train neural network, which increase the detection accuracy. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： Neural Network Rough Set Network Intrusion Reduction particle swarm optimization algorithm

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Fault Diagnosis Analysis with Support Vector Regression and particle swarm optimization algorithm

Fault Diagnosis Analysis with Support Vector Regression and ...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： WenJie Tian JiCheng Liu Beijing Automation Institute of Beijing Union University Beijing China 100101

The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method can detect various fault behaviors rapidly and effectively by learning the typical fault characteristic information. Utilizing the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction particle swarm optimization algorithm

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High-precision clock synchronization based on PSO algorithm under the small sample condition

High-precision clock synchronization based on PSO algorithm ...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Yilin Jiang Fangyuan Zhang Muran Guo College of Information and Communication Engineering Harbin Engineering University

ISBN: (纸本)9781509001668

In order to solve the problem of clock synchronization under the condition of small sample set,a state optimal estimation method of data fusion bases on natural selection PSO algorithm is ***,use Kalman filtering algorithm and Bootstrap method to local filtering process the measurement data under the condition of Monte Carlo simulation experiment,and the reliability of the data is ***,use natural selection PSO algorithm to fuse the data,the optimal fused estimation is achieved and the fusion accuracy is ***,the fusion result is considered as the new information of virtual master clock,which could exchange time messages with node clock of network from the ***,clock synchronization in the network is *** simulated results show that the accuracy is *** precision clock synchronization is realized and the stability time is decreased.

关键词： clock synchronization data fusion particle swarm optimization algorithm Bootstrap method Kalman filtering

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