【正文】 rtificial immune system to bee increasingly better at its task of recognising patterns (antigens). Thus, based upon an evolutionary like behaviour, CLONALG learns to recognise patterns. Immune NetworkThe immune network theory proposes that the immune system has a dynamic behaviour even in the absence of external stimuli. It is suggested that the immune cells and molecules are capable of recognising each other, what endows the system with an eigen behaviour that is not dependent on foreign stimulation. Several immunologists have refuted this theory, however its putational aspects are relevant and it has proved itself to be a powerful model for putational systems.According to the immune network theory, the receptor molecules contained in the surface of the immune cells present markers, named idiotopes, which can be recognized by receptors on other immune cells. These idiotopes are displayed in and/or around the same portions of the receptors that recognise nonself antigens. To explain the network theory, assume that a receptor (antibody) Ab1 on a Bcell recognises a nonself antigen Ag. Assume now, that this same receptor Ab1 also recognises an idiotope i2 on another Bcell receptor Ab2. Keeping track of the fact that i2 is part of Ab2, Ab1 is capable of recognising both Ag and Ab2. Thus, Ab2 is said to be the internal image of Ag, more precisely, i2 is the internal image of Ag. The recognition of idiotopes on a cell receptor by other cell receptors, lead to ever increasing sets of connected cell receptors and molecules. Note that the network in this case, is a network of affinities, which different from the ‘hardwired’ network of the nervous system. As a result of the network recognition events, it was suggested that the recognition of a cell receptor by another cell receptor results in network suppression, whilst the recognition of an antigen by a cell receptor results in network activation and cell proliferation. The original theory did not account explicitly for the results of network activation and/or suppression, and the various artificial immune networks found in the literature model it in a particular form.3 Modelling Pattern Recognition in AISUp to this point, the most relevant immune principles and their corresponding putational counterparts to perform pattern recognition have been presented. In order to apply these algorithms to putational problems, there is a need to specify a limited number of other aspects of artificial immune systems, not as yet covered. The first aspect to introduce is the most relevant representations to be applied to model self and nonself patterns. Here the selfpatterns correspond to the ponents of the AIS responsible for recognising the input patterns (nonself). Secondly, the mechanism by which the evaluation of the degree of match (affinity), or degree of recognition, of an input pattern by an element of the AIS has to be discussed. To model immune cells, molecules, and the antigenic patterns, the shapespace approach proposed is usually adopted. Although AIS model recognition through pattern matching, given certain affinity functions to be described further, performing pattern recognition through plementarity or similarity is based more on practical aspects than on biological plausibility. The shapespace approach proposes that an attribute string s = 225。 in our context, pattern recognition. If we recapitulate the three immune processes reviewed, negative selection, clonal selection, and immune network, all of them rely on a population M of individuals to recognise a set P of patterns. The negative selection algorithm has to define a set of detectors for nonself patterns。 and 3) applying the system to recognise a set of new patterns (that might contain patterns used in the adaptive phase). Refering to the three immune algorithms presented (negative selection, clonal selection, and immune network), coupled with the process of modelling pattern recognition in the immune system, as described in Section 3, this section will contrast AIS and ANN focusing the pattern recognition applications. Discussion will be based on putational aspects, such as basic ponents, adaptation mechanisms, etc. Common neural networks for pattern recognition will be considered, such as single and multilayer perceptrons, associative memories, and selforganising networks. All these networks are characterised by set(s) of units (artificial neurons)。 and the immune network maintains a set of individuals, connected as a network, to recognize self and nonself.Consider first the binary Hamming shapespace case, which is the most widely used. There are several expressions that can be employed in the determination of the degree of match or affinity between an element of P and an element of M. The simplest case is to simply calculate the Hamming distance (DH) between these two elements, as given by Eq. (1). Another approach is to search for a sequence of rcontiguous bits, and if the number of rcontiguous matches between the strings is greater than a given threshold, then recognition is said to have occurred. As the last approach to be mentioned here, we can describe the affinity measure of Hunt, given by Eq. (2). This last method has the advantage that it favours sequences of plementary matches, thus searching for similar regions between the attribute strings (patterns). （1） （2）where is the length of the ith sequence of matching bits longer than 2.In the case of Euclidean shapespaces, the Euclidean distance can be used to evaluate the affinity between any two ponents of the system. Other approaches such as the Manhattan distance may also be employed. Note that all the methods described rely basically, on determining the match between strings. However, there are AIS in the literature that take into account other aspects, such as the number of patterns matched by each antibody.4 A Surv