【正文】 ey of AIS for Pattern RecognitionThe applications of artificial immune systems are vast, ranging from machine learning to robotic autonomous navigation. This section will review some of the works from the AIS literature applied to the pattern recognition domain. The rationale is to provide a guide to the literature and a brief description of the scope of applications of the algorithms. The section is divided into two parts for ease of prehension: 1) puter security, and 2) other applications. The problem of protecting puters (or networks of puters) from viruses, unauthorised users, etc., constitutes a rich field of research for pattern recognition systems. Due, mainly, to the appealing intuitive metaphor of building artificial immune systems to detect puter viruses, there has been a great interest from the puter science munity to this particular application. The use of the negative and clonal selection algorithms have been widely tested on this application. The former because it is an inherent anomaly (change) detection system, constituting a particular case of a pattern recognition device. The latter, the clonal selection algorithm, has been used in conjunction to negative selection due to its learning capabilities. Other more classical pattern recognition tasks, such as character recognition, and data analysis have also been studied within artificial immune systems. 5 AIS and ANN for Pattern RecognitionSimilar to the use of artificial neural networks, performing pattern recognition with an AIS usually involves three stages: 1) defining a representation for the patterns。 in an Ldimensional shapespace, S, (s 206。3. Select n1 of the best highest affinity elements of M and generate copies of these individuals proportionally to their affinity with the antigen. The higher the affinity, the higher the number of copies, and viceversa。 and is protected by a blood barrier capable of efficiently excluding nonself antigens from the thymic environment. Thus, most elements found within the thymus are representative of self instead of nonself. As an oute, the Tcells containing receptors capable of recognising these self antigens presented in the thymus are eliminated from the repertoire of Tcells through a process named negative selection. All Tcells that leave the thymus to circulate throughout the body are said to be tolerant to self, ., they do not respond to self.From an information processing perspective, negative selection presents an alternative paradigm to perform pattern recognition by storing information about the plement set (nonself) of the patterns to be recognised (self). A negative selection algorithm has been proposed in the literature with applications focused on the problem of anomaly detection, such as puter and network intrusion detection, time series prediction, image inspection and segmentation, and hardware fault tolerance. Given an appropriate problem representation (Section 3), define the set of patterns to be protected and call it the self set (P). Based upon the negative selection algorithm, generate a set of detectors (M) that will be responsible to identify all elements that do not belong to the selfset, ., the nonself elements. After generating the set of detectors (M), the next stage of the algorithm consists in monitoring the system for the presence of nonself patterns (Fig 2(b)). In this case, assume a set P* of patterns to be protected. This set might be posed of the set P plus other new patterns, or it can be a pletely novel set.For all elements of the detector set, that corresponds to the nonself patterns, check if it recognises (matches) an element of P* and, if yes, then a nonself pattern was recognized and an action has to be taken. The resulting action of detecting nonself varies according to the problem under evaluation and extrapolates the pattern recognition scope of this chapter. Clonal SelectionComplementary to the role of negative selection, clonal selection is the theory used to explain how an immune response is mounted when a nonself antigenic pattern is recognised by a Bcell. In brief, when a Bcell receptor recognises a nonself antigen with a certain affinity, it is selected to proliferate and produce antibodies in high volumes. The antibodies are soluble forms of the Bcell receptors that are released from the Bcell surface to cope with the invading nonself antigen. Antibodies bind to antigens leading to their eventual elimination by other immune cells. Proliferation in the case of immune cells is asexual, a mitotic process。信息工程學(xué)院軟件工程Artificial Immune Systems:A Novel Paradigm to Pattern RecognitionAbstractThis chapter introduces a new putational intelligence paradigm to perform pattern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune system in order to build novel putational tools to solve problems in a vast range of domain areas. The basic immune theories used to explain how the immune system perform pattern recognition are described and their corresponding putational models are presented. This is followed with a survey from the literature of AIS applied to pattern recognition. The chapter is concluded with a tradeoff between AIS and artificial neural networks as pattern recognition paradigms.Keywords: Artificial Immune Systems；Negative Selection；Clonal Selection；Immune Network1 IntroductionThe vertebrate immune system (IS) is one of the most intricate bodily systems and its plexity is sometimes pared to that of the brain. With the advances in the biology and molecular genetics, the prehension of how the immune system behaves is increasing very rapidly. The knowledge about the IS functioning has unraveled several of its main operative mechanisms. These mechanisms have demonstrated to be very interesting not only from a biological standpoint, but also under a putational perspective. Similarly to the way the nervous system inspired the development of artificial neural net