【正文】 multilayer perceptrons, associative memories, and selforganising networks. All these networks are characterised by set(s) of units (artificial neurons)。 in an Ldimensional shapespace, S, (s 206。 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。 a process named clonal selection. In contrast, if a Bcell recognises a selfantigen, it might result in suppression, as proposed by the immune network theory. In the following subsections, each of these processes (negative selection, clonal selection, and network theory) will be described separately, along with their putational algorithms counterparts. Negative SelectionThe thymus is responsible for the maturation of Tcells。s1, s2,…,sL241。 they adapt to the environment through a learning (or storage) algorithm, they can have their architectures dynamically adapted along with the weights, and they have the basic knowledge stored in the connection strengths.Component: The basic unit of an AIS is an attribute string s (along with its connections in network models) represented in the appropriate shapespace. This string s might correspond to an immune cell or molecule. In an ANN, the basic unit is an artificial neuron posed of an activation function, a summing junction, connection strengths, and an activation threshold. While artificial neurons are usually processing elements, attribute strings representing immune cells and molecules are information storage and processing ponents.Location of the ponents: In immune network models, the cells and molecules usually present a dynamic behaviour that tries to mimic or counteract the environment. This way, the network elements will be located according to the environmental stimuli. Unlike the immune network models, ANN have their neurons positioned in fixed predefined locations in the network. Some neural network models also adopt fixed neighbourhood patterns for the neurons. If a network pattern of connectivity is not adopted for the AIS, each individual element will have a position in the population that might vary dynamically. Also, a metadynamic process might allow the introduction and/or elimination of particular units.Structure: In negative and clonal AIS, the ponents are usually structured around matrices representing repertoires or populations of individuals. These matrices might have fixed or variable dimensions. In artificial immune networks and artificial neural networks, the ponents of the population are interconnected and structured around patterns of connectivity. Artificial immune networks usually have an architecture that follows the spatial distribution of the antigens represented in shapespace, while ANN usually have predefined architectures, and weights biased by the environment. Memory: The attribute strings representing the repertoire(s) of immune cells and molecules, and their respective numbers, constitute most of the knowledge contained in an artificial immune system. Furthermore, parameters like the affinity threshold can also be considered part of the memory of an AIS. In artificial immune network models, the connection strengths among units also carry endogenous and exogenous information, ., they quantify the interactions of the elements of the AIS themselves and also with the environment. In most cases, memory is contentaddressable and distributed. In the standa