During the last decade, the field of artificial immune system (A1S) is progressing slowly and steadily as a branch of computational intelligence (CI). There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts - computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the artificial immune system (AIS) is emerging as an active and attractive, field involving models, techniques and applications of greater diversity

Advances in artificial immune systems

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories-however, if the field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the past, and ask the difficult question ''was it worth it ?''. We then attempt to suggest a set of problem features that we believe will allow the true potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS.

Application areas of AIS: The past, the present and the future

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the
paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the
field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to
be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the
past, and ask the difficult question ‘‘was it worth it ?’’. We then attempt to suggest a set of problem features that we believe will allow the true
potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS

Application areas of AIS: the past, present and future.

This survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic non-linear dynamical modelling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided.

/pdf/model-based-fault-diagnosis-for-aerospace-systems-a-survey-jpkifgdebi.pdf

Model-based fault diagnosis for aerospace systems: a survey:

Over the last decade, the field of immunological computation has progressed slowly and steadily as a branch of computational intelligence. Immunological Computation: Theory and Applications presents up-to-date immunity-based computational techniques. After a brief review of fundamental immunology concepts, the book presents computational models based on the negative selection process that occurs in the thymus. It then examines immune networks, including continuous and discrete immune network models, clonal selection, hybrid models, and computational models based on danger theory. The book also discusses real-world applications for all of the models covered in each chapter.

/pdf/immunological-computation-theory-and-applications-2eh0booef6.pdf

Immunological Computation: Theory and Applications

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

/pdf/negative-selection-algorithm-for-aircraft-fault-detection-1pvgqxgm8g.pdf

Negative Selection Algorithm for Aircraft Fault Detection

We present a model of spatial navigation based on the non-convergent dynamics of brain activity. The system includes a hippocampal module that processes global spatial information and a cortical module that deals with local sensory information. We test the model using several spatial navigation paradigms: goal finding, shortcutting and detouring. Computer simulations show that the performance of the agent qualitatively matches that of animals and related models. This new approach provides a novel interpretation of how the brain accomplishes spatial navigation.

/pdf/spatial-navigation-model-based-on-chaotic-attractor-networks-2jlj7jcdgp.pdf

Spatial navigation model based on chaotic attractor networks

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

/pdf/immunity-based-aircraft-fault-detection-system-25nzi4ye3m.pdf

Immunity-Based Aircraft Fault Detection System

Learning environmental clues in the KIV model of the cortico-hippocampal formation

https://www.memphis.edu/clion/pdf-papers/learningintentionalbehaviorinthek-model.pdf

Derek Wong

Papers

Negative Selection Algorithm for Aircraft Fault Detection

Spatial navigation model based on chaotic attractor networks

Immunity-Based Aircraft Fault Detection System

Learning environmental clues in the KIV model of the cortico-hippocampal formation

Learning intentional behavior in the K-model of the amygdala and entorhinal cortex with the cortico-hyppocampal formation