Research of Immune Controllers

Research of Immune Controllers

Fu Dongmei (University of Science and Technology, P.R. China)
DOI: 10.4018/978-1-60566-310-4.ch013
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Abstract

In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object has been chosen. Improving the traditional controller or constructing the new controller is an unfading study field of control theory and application. The control system is greatly enriched and developed by this way. As a complicated self-adaptable system, the biological immune system can effectively and smoothly stand against antigens and viruses intruded into organism. It is possible to improve the self-learning, adaptive and robustness capability of the control system through embedded an artificial immune controller in control system. Based on the biological immune mechanism and artificial immune model, this chapter attempts to study the immune controller design and application in traditional control system..First, a kind of artificial immune controller is proposed based on the T-B cells immunity. The boundedness and the stability of SISO control systems, which constructed by the artificial immune controller, are proved by the little gain theorem. A general controller structure frame based on the T-B cells immunity is proposed, which includes the same kind of controller proposed previously. The validity of this artificial immune controller is verified by simulation. Second, a new type of artificial immune controllers is constructed according to a simple double-cell immune dynamics model. The non-error characteristic of SISO control systems, which constructed by the artificial immune controller, is proved by the nonlinear theory in this chapter. The I/O stability and no-error characteristic of the system are verified by simulations, which show that the kind of artificial immune system have good anti-lag capability. Third, the Varela immune network model has been improved based on which an artificial immune system is proposed. The odd linearization method of the non-linear system is used to prove the stability and non-error characteristic of the SISO system constructed by the artificial immune control system. Its I/O stability, non-error characteristic and strong anti-lag capability are also verified by simulation. Finally, based on the comparison of the three kinds of immune controllers, a general structure of the artificial immune controller is proposed. The further study on this field is indicated in this chapter lastly.
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Background

In real applications, once the controlled object and the measuring components have been determined, the performance of linear control systems will depend on controllers. Therefore, designing and constructing new types of controllers or improving traditional controllers have been being a fascinating topic in the theory of control systems. For instance, one of the important applications of fuzzy control theory is to design and construct controllers which have great adaptability and robustness(Jingzhen & Zengji, 1997), so is the neural network (Dongqiang & Yaqing, 2001) and expert system. Even the widely used PID controllers are also studied by many individuals to make it more intelligent. The study and application of intelligent controllers has extraordinarily enriched and developed the theory of control systems.

Intelligent controllers fall into four levels according to the designing complexity: simple robust feedback control, parameter adaptive control based on error criterion, adaptive control based on optimization of objective function and adaptive control based on global optimization of objective function varied with environment conditions. Corresponding with these, biologic immune system fall into four similar levels: firstly, the innate immune system is corresponding to robust feedback control; secondly, T cells stimulate B cells to resist antigens; thirdly, microphages present antigens to T cells and activate B cells; lastly, microphages have some changes against certain antigens. Studies have found that biologic immune system has a series of excellent control characteristics, such as adaptability, distributed control and coordination, dynamic, robust, self-organization, self-learning etc. So a further study of immune system on its mechanism and strategies will provide some ideas for constructing advanced intelligent controllers.

Key Terms in this Chapter

Artificial Immune Models: Artificial Immune models are a kind of mathematical models which are constructed by macro-biological or micro-biological immune mechanism.

Antigen: The antigen is a substance which stimulate the person or animal body to produce sensitized antibodies or lymphocytes, these products arose specific responses in the person or animal body. Antigens include toxins, bacteria, foreign blood cells, the cells of transplanted organs and so on.

Immune Factor: The immune factors are a sort of small molecule polypeptide which mainly were secreted the biological activity immunocyte, such as transfer factor, distinguishing factor and so on. Immune factors mainly make regulative roles in the immune recognition and immune responses.

Artificial Immune Control: Artificial immune control is similar ordinary control except Artificial immune algorithm and model in it. Artificial immune control is the process to detect, control or manage the applications, such as controlled objects, production procedures, or managements, by constructing immune models or algorithms suitable for control systems through reference of measures and regulars extracted from immune mechanisms, models and the cooperation between immune factors.

Immune Controller: Immune controller is a man-made controller that based on measures and regulars of macro immune mechanisms, models or micro cooperation between immune factors to improve the performance of a control system. Immune controllers mostly are nonlinear dynamic models or algorithms with iteration process. Artificial immune controller is similar ordinary controller except immune algorithm and model in it.

Artificial Immunity: The algorithms or dynamics models which are founded by men imitate the part or all of the biological immune mechanism to gain some special features or peculiar functions. Those algorithms or the dynamics models are usually known as the artificial immunity.

Antibody: Antibodies are protein substances produced in the blood or tissues to response to a specific antigen, such as a bacterium or a toxin. Antibodies destroy or weaken bacteria and neutralize organic poisons, thus forming the basis of immunity.

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Table of Contents
Foreword
Lipo Wang
Preface
Hongwei Mo
Chapter 1
Fabio Freschi, Carlos A. Coello Coello, Maurizio Repetto
This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be... Sample PDF
Multiobjective Optimization and Artificial Immune Systems: A Review
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Chapter 2
Jun Chen, Mahdi Mahfouf
The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to... Sample PDF
Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
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Chapter 3
Licheng Jiao, Maoguo Gong, Wenping Ma
Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection... Sample PDF
An Artificial Immune Dynamical System for Optimization
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Chapter 4
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
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Chapter 5
Luis Fernando Niño Vasquez, Fredy Fernando Muñoz Mopan, Camilo Eduardo Prieto Salazar, José Guillermo Guarnizo Marín
Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high... Sample PDF
Applications of Artificial Immune Systems in Agents
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Chapter 6
Xingquan Zuo
Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling... Sample PDF
An Immune Algorithm Based Robust Scheduling Methods
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Chapter 7
Fabio Freschi, Maurizio Repetto
The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new... Sample PDF
Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems
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Chapter 8
Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large... Sample PDF
Applying the Immunological Network Concept to Clustering Document Collections
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Chapter 9
Xiangrong Zhang, Fang Liu
The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on.... Sample PDF
Feature Selection Based on Clonal Selection Algorithm: Evaluation and Application
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Chapter 10
Yong-Sheng Ding, Xiang-Feng Zhang, Li-Hong Ren
Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network... Sample PDF
Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet
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Chapter 11
Tao Gong
Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing... Sample PDF
A Static Web Immune System and Its Robustness Analysis
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Chapter 12
Alexander O. Tarakanov
Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal... Sample PDF
Immunocomputing for Spatio-Temporal Forecast
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Chapter 13
Fu Dongmei
In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object... Sample PDF
Research of Immune Controllers
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Chapter 14
Xiaojun Bi
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete... Sample PDF
Immune Programming Applications in Image Segmentation
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Chapter 15
Xin Wang, Wenjian Luo, Zhifang Li, Xufa Wang
A hardware immune system for the error detection of MC8051 IP core is designed in this chapter. The binary string to be detected by the hardware... Sample PDF
A Hardware Immune System for MC8051 IP Core
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Chapter 16
Mark Burgin, Eugene Eberbach
There are different models of evolutionary computations: genetic algorithms, genetic programming, etc. This chapter presents mathematical... Sample PDF
On Foundations of Evolutionary Computation: An Evolutionary Automata Approach
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Chapter 17
Terrence P. Fries
Path planning is an essential component in the control software for an autonomous mobile robot. Evolutionary strategies are employed to determine... Sample PDF
Evolutionary Path Planning for Robot Navigation Under Varying Terrain Conditions
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Chapter 18
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony... Sample PDF
Ant Colony Optimization for Use in Content Based Image Retrieval
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Chapter 19
Miroslav Bursa, Lenka Lhotska
The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a... Sample PDF
Ant Colonies and Data Mining
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Chapter 20
Bo-Suk Yang
This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global... Sample PDF
Artificial Life Optimization Algorithm and Applications
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Chapter 21
Martin Macaš, Lenka Lhotská
A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of... Sample PDF
Optimizing Society: The Social Impact Theory Based Optimizer
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Chapter 22
James F. Peters, Shabnam Shahfar
The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for... Sample PDF
Ethology-Based Approximate Adaptive Learning: A Near Set Approach
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Chapter 23
Dingju Zhu
Parallel computing is more and more important for science and engineering, but it is not used so widely as serial computing. People are used to... Sample PDF
Nature Inspired Parallel Computing
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Chapter 24
Tang Mo, Wang Kejun, Zhang Jianmin, Zheng Liying
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is... Sample PDF
Fuzzy Chaotic Neural Networks
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About the Contributors