An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design

A novel artificial immune system algorithm with social learning mechanisms (AIS-SL) is proposed in this paper. In AIS-SL, candidate antibodies are marked with an elitist swarm (ES) or a common swarm (CS). Correspondingly, these antibodies are named ES antibodies or CS antibodies. In the mutation operator, ES antibodies experience self-learning, while CS antibodies execute two different social learning mechanisms, that is, stochastic social learning (SSL) and heuristic social learning (HSL), to accelerate the convergence process. Moreover, a dynamic searching radius update strategy is designed to improve the solution accuracy. In the numerical simulations, five benchmark functions and a practical industrial application of proportional-integral-differential (PID) controller tuning is selected to evaluate the performance of the proposed AIS-SL. The simulation results indicate that AIS-SL has better solution accuracy and convergence speed than the canonical opt-aiNet, IA-AIS, and AAIS-2S

A bioinspired methodology based on an artificial immune system for damage detection in structural health monitoring

Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.Postprint (published version

Kernel Extended Real-Valued Negative Selection Algorithm (KERNSA)

Artificial Immune Systems (AISs) are a type of statistical Machine Learning (ML) algorithm based on the Biological Immune System (BIS) applied to classification problems. Inspired by increased performance in other ML algorithms when combined with kernel methods, this research explores using kernel methods as the distance measure for a specific AIS algorithm, the Real-valued Negative Selection Algorithm (RNSA). This research also demonstrates that the hard binary decision from the traditional RNSA can be relaxed to a continuous output, while maintaining the ability to map back to the original RNSA decision boundary if necessary. Continuous output is used in this research to generate Receiver Operating Characteristic (ROC) curves and calculate Area Under Curves (AUCs), but can also be used as a basis of classification confidence or probability. The resulting Kernel Extended Real-valued Negative Selection Algorithm (KERNSA) offers performance improvements over a comparable RNSA implementation. Using the Sigmoid kernel in KERNSA seems particularly well suited (in terms of performance) to four out of the eighteen domains tested

Design and validation of structural health monitoring system based on bio-inspired algorithms

The need of ensure the proper performance of the structures in service has made of structural health monitoring (SHM) a priority research area. Researchers all around the world have focused efforts on the development of new ways to continuous monitoring the structures and analyze the data collected from the inspection process in order to provide information about the current state and avoid possible catastrophes. To perform an effective analysis of the data, the development of methodologies is crucial in order to assess the structures with a low computational cost and with a high reliability. These desirable features can be found in biological systems, and these can be emulated by means of computational systems. The use of bio-inspired algorithms is a recent approach that has demonstrated its effectiveness in data analysis in different areas. Since these algorithms are based in the emulation of biological systems that have demonstrated its effectiveness for several generations, it is possible to mimic the evolution process and its adaptability characteristics by using computational algorithms. Specially in pattern recognition, several algorithms have shown good performance. Some widely used examples are the neural networks, the fuzzy systems and the genetic algorithms. This thesis is concerned about the development of bio-inspired methodologies for structural damage detection and classification. This document is organized in five chapters. First, an overview of the problem statement, the objectives, general results, a brief theoretical background and the description of the different experimental setups are included in Chapter 1 (Introduction). Chapters 2 to 4 include the journal papers published by the author of this thesis. The discussion of the results, some conclusions and the future work can be found on Chapter 5. Finally, Appendix A includes other contributions such as a book chapter and some conference papers.La necesidad de asegurar el correcto funcionamiento de las estructuras en servicio ha hecho de la monitorización de la integridad estructural un área de gran interés. Investigadores en todas las partes del mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización contínua de estructuras que permitan analizar e interpretar los datos recogidos durante el proceso de inspección con el objetivo de proveer información sobre el estado actual de la estructura y evitar posibles catástrofes. Para desarrollar un análisis efectivo de los datos, es necesario el desarrollo de metodologías para inspeccionar la estructura con un bajo coste computacional y alta fiabilidad. Estas características deseadas pueden ser encontradas en los sistemas biológicos y pueden ser emuladas mediante herramientas computacionales. El uso de algoritmos bio-inspirados es una reciente técnica que ha demostrado su efectividad en el análisis de datos en diferentes áreas. Dado que estos algoritmos se basan en la emulación de sistemas biológicos que han demostrado su efectividad a lo largo de muchas generaciones, es posible imitar el proceso de evolución y sus características de adaptabilidad al medio usando algoritmos computacionales. Esto es así, especialmente, en reconocimiento de patrones, donde muchos de estos algoritmos brindan excelentes resultados. Algunos ejemplos ampliamente usados son las redes neuronales, los sistemas fuzzy y los algoritmos genéticos. Esta tesis involucra el desarrollo de unas metodologías bio-inspiradas para la detección y clasificación de daños estructurales. El documento está organizado en cinco capítulos. En primer lugar, se incluye una descripción general del problema, los objetivos del trabajo, los resultados obtenidos, un breve marco conceptual y la descripción de los diferentes escenarios experimentales en el Capítulo 1 (Introducción). Los Capítulos 2 a 4 incluyen los artículos publicados en diferentes revistas indexadas. La revisión de los resultados, conclusiones y el trabajo futuro se encuentra en el Capítulo 5. Finalmente, el Anexo A incluye otras contribuciones tales como un capítulo de libro y algunos trabajos publicados en conferencias

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Computational intelligence techniques for data analysis

Ph.DDOCTOR OF PHILOSOPH