【正文】 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 Survey 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 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。 in an Ldimensional shapespace, S, (s 206。6. Repeat Steps 2 to 5 until a certain criterion is met, such as a minimum pattern recognition or classification error.Note that this algorithm allows the artificial 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 th