• Title/Summary/Keyword: immune algorithm

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A Design of An Active PID control using Immune Algorithm for Vibration Control of Building Structure (구조물 진동제어를 위한 Immune Algorithm을 이용한 Active PID 제어기 설계)

  • Lee, Young-Jin;Cho, Hyun-Cheol;Lee, Kwon-Soon
    • Proceedings of the KIEE Conference
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    • 2005.05a
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    • pp.72-74
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    • 2005
  • In this paper, we propose an adaptive PID controller using a cell-mediated immune response to improve a PID control performance. The proposed controller is based on the specific immune response of the biological immune system that is cell-mediated immunity. The immune system of organisms in the real body regulates the antibody and the T-cells to protect an attack from the foreign materials like virus, germ cells, and other antigens. It has similar characteristics that are the adaptation and robustness to overcome disturbances and to control the plant of engineering application. We first build a model of the T-cell regulated immune response mechanism and then designed an I-PID controller focusing on the T-cell regulated immune response of the biological immune system. We apply the proposed methodology to building structures to mitigate vibrations due to strong winds for evaluation of control performances. Through computer simulations, system responses are illustrated and additionally compared to traditional control approaches.

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An Optimal Parameter Selection of Power System Stabilizer using Immune Algorithm (면역 알고리즘을 이용한 전력 계통 안정화 장치의 최적 파라미터 선정)

  • Jeong, Hyeong-Hwan;Lee, Jeong-Pil;Jeong, Mun-Gyu;Lee, Gwang-U
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.49 no.9
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    • pp.433-445
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    • 2000
  • In this paper, optimal tuning problem of power system stabilizer(PSS) using Immune Algorithm(IA) is investigated to improve power system dynamic stability. In proposed method, objective function is represented as antigens. An affinity calculation is embedded within the algorithm for determining the promotion or suppression of antibody. An antibody that most fits the antigen is considered as the solution to PSS tuning problem. The computaton performance by the proposed method is compared with Genetic Algorithm(GA). The porposed PSS using IA has been applied for two sample system, single-machine infinite bus system and multi-machine power system. The performance of the proposed PSS is compared with that of conventional PSS. It is shown that the proposed PSS tuned using immune algorithm is more robust than conventional PSS.

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PThe Robust Control System Design using Intelligent Hybrid Self-Tuning Method (지능형 하이브리드 자기 동조 기법을 이용한 강건 제어기 설계)

  • 권혁창;하상형;서재용;조현찬;전홍태
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.05a
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    • pp.325-329
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    • 2003
  • This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

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Estimation of Software Reliability with Immune Algorithm and Support Vector Regression (면역 알고리즘 기반의 서포트 벡터 회귀를 이용한 소프트웨어 신뢰도 추정)

  • Kwon, Ki-Tae;Lee, Joon-Kil
    • Journal of Information Technology Services
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    • v.8 no.4
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    • pp.129-140
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    • 2009
  • The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

Autonomous Mobile Robots Navigation Using Artificial Immune Networks and Neural Networks (인공 면역망과 신경회로망을 이용한 자율이동로봇 주행)

  • 이동제;김인식;이민중;최영규
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.8
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    • pp.471-481
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    • 2003
  • The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.

Negative Selection Algorithm for DNA Pattern Classification

  • Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.190-195
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    • 2004
  • We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

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Optimal nonlinear Parameter Estimation of Steady-State Induction Motor using Immune Algorithm

  • Kim, Dong-Hwa;Cho, Jae-Hoon;Hong, Won-Pyo;Lee, Seung-Hack;Lee, Hwan
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.891-895
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    • 2004
  • This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm. The parameter estimation procedure is based on the steady state phase current versus slip and input power versus slip characteristics. The proposed estimation algorithm is of a nonlinear kind based on clonal selection in immune algorithm. The machine parameters are obtained as the solution of a minimization of least-squares cost function by immune algorithm. Simulation shows better results than the conventional approaches.

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Intelligent Parameter Estimation of a Induction Motor Using Immune Algorithm

  • Kim, Dong-Hwa;Cho, Jae-Hoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.10a
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    • pp.21-25
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    • 2004
  • This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm. The parameter estimation procedure is based on the steady state phase current versus slip and input power versus slip characteristics. The proposed estimation algorithm is of a nonlinear kind based on clonal selection in immune algorithm. The machine parameters are obtained as the solution of a minimization of least-squares cost function by immune algorithm. Simulation shows better results than the conventional approaches.

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Application of Immune Algorithm for Harmonic State Estimation (전력시스템 고조파 상태 추정에서 면역 알고리즘 적용)

  • Wang Yong-Peel;Park In-Pyo;Chung Hyeng-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.53 no.12
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    • pp.645-654
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    • 2004
  • The design of a measurement system to perform Harmonic State Estimation(HSE) is a very complex problem. In particular, the number of available harmonic analysis measurement instruments is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents an optimal algorithm of HSE which is based on an optimal placement of measurement points using Immune Algorithm (IAs). This IA-HSE has been applied to power system for the validation of an optimal algorithm of HSE. The study results have indicated an economical and effective method for optimal placement of measurement points using Immune Algorithm (IAs) in the HSE.

An Immune Algorithm based Multiple Energy Carriers System (면역알고리즘 기반의 MECs (에너지 허브) 시스템)

  • Son, Byungrak;Kang, Yu-Kyung;Lee, Hyun
    • Journal of the Korean Solar Energy Society
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    • v.34 no.4
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    • pp.23-29
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    • 2014
  • Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).