Artificial immune systems applied to fault detection and isolation: A brief review of immune response-based approaches and a case study

2017 ◽  
Vol 57 ◽  
pp. 118-131 ◽  
Author(s):  
Guilherme Costa Silva ◽  
Walmir Matos Caminhas ◽  
Reinaldo Martinez Palhares
Keyword(s):  
Immune Response ◽  
Fault Detection ◽  
Artificial Immune Systems ◽  
Fault Detection And Isolation ◽  
Artificial Immune ◽  
Immune Systems

Once More Unto the Breach

2011 ◽  
pp. 2151-2175
Author(s):  
Mark Neal ◽  
Jon Timmis
Keyword(s):  
Artificial Immune Systems ◽  
Endocrine System ◽  
The Self ◽  
Artificial Immune ◽  
Biologically Inspired ◽  
Immune Systems ◽  
Robot Controller ◽  
Computational Systems ◽  
And Robotics

The field of biologically inspired computing has generated many novel, interesting and useful computational systems. None of these systems alone is capable of approaching the level of behaviour for which the artificial intelligence and robotics communities strive. We suggest that it is now time to move on to integrating a number of these approaches in a biologically justifiable way. To this end we present a conceptual framework that integrates artificial neural networks, artificial immune systems and a novel artificial endocrine system. The natural counterparts of these three components are usually assumed to be the principal actors in maintaining homeostasis within biological systems. This chapter proposes a system that promises to capitalise on the self-organising properties of these artificial systems to yield artificially homeostatic systems. The components develop in a common environment and interact in ways that draw heavily on their biological counterparts for inspiration. A case study is presented, in which aspects of the nervous and endocrine systems are exploited to create a simple robot controller. Mechanisms for the moderation of system growth using an artificial immune system are also presented.

scholarly journals Artificial Immune Systems: An Overview for Faulting Actuators

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 53
Author(s):  
Kidd
Keyword(s):  
Real Time ◽  
Artificial Immune Systems ◽  
Adaptive Algorithms ◽  
Actuator Faults ◽  
Artificial Immune ◽  
Immune Systems ◽  
Limited Success ◽  
Fault Compensation

This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

A FUZZY INFERENCE SYSTEM TO DETERMINE THE NUMBER OF CLONES IN A CLASS OF ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 11 (01) ◽  
pp. 1250005
Author(s):  
LUIZ ANTONIO CARRARO ◽  
LEANDRO NUNES DE CASTRO ◽  
ANGELITA MARIA DE RE ◽  
FABRĹCIO OLIVETTI DE FRANÇA
Keyword(s):  
Immune Response ◽  
Immune Cell ◽  
Fuzzy Inference ◽  
Clonal Selection ◽  
Selection Process ◽  
Artificial Immune Systems ◽  
Reproduction Rate ◽  
Artificial Immune ◽  
Inference System ◽  
Immune Systems

Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

Applications of Artificial Immune Systems in Agents

2011 ◽  
pp. 99-122
Author(s):  
Luis Fernando Niño Vasquez ◽  
Fredy Fernando Muñoz Mopan ◽  
Camilo Eduardo Prieto Salazar ◽  
José Guillermo Guarnizo Marín
Keyword(s):  
Immune Response ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Multi Agent Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Agent Systems ◽  
Adaptive Immune ◽  
Multi Agent ◽  
Object Transportation

Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high efficacy. This is a survey chapter within which single and multi-agent systems inspired by immunology concepts are presented and analyzed. Most of the work is usually based on the adaptive immune response characteristics, such as clonal selection, idiotypic networks, and negative selection. However, the innate immune response has been neglected and there is not much work where innate metaphors are used as inspiration source to develop robotic systems. Therefore, a work that involves some interesting features of the innate and adaptive immune responses in a cognitive model for object transportation is presented at the end of this chapter.

Once More Unto the Breach

2005 ◽  
pp. 340-366 ◽  
Author(s):  
Mark Neal ◽  
Jon Timmis
Keyword(s):  
Artificial Immune Systems ◽  
Endocrine System ◽  
The Self ◽  
Artificial Immune ◽  
Biologically Inspired ◽  
Immune Systems ◽  
Robot Controller ◽  
Computational Systems ◽  
And Robotics

The field of biologically inspired computing has generated many novel, interesting and useful computational systems. None of these systems alone is capable of approaching the level of behaviour for which the artificial intelligence and robotics communities strive. We suggest that it is now time to move on to integrating a number of these approaches in a biologically justifiable way. To this end we present a conceptual framework that integrates artificial neural networks, artificial immune systems and a novel artificial endocrine system. The natural counterparts of these three components are usually assumed to be the principal actors in maintaining homeostasis within biological systems. This chapter proposes a system that promises to capitalise on the self-organising properties of these artificial systems to yield artificially homeostatic systems. The components develop in a common environment and interact in ways that draw heavily on their biological counterparts for inspiration. A case study is presented, in which aspects of the nervous and endocrine systems are exploited to create a simple robot controller. Mechanisms for the moderation of system growth using an artificial immune system are also presented.

Sign in / Sign up

Export Citation Format

Share Document