Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Fuzzy Neural Network <s> This paper describes a system that responds to a significant problem involving credit card theft. After a card has been stolen, some of the transactions processed subsequently have to be granted if it seems that they were carried out by the card owner himself (e.g. by a standing order) while other transactions should be denied authorization if they were carried out by the misuser of the card. The system developed makes use of a neural network integrated with a Case-Based Reasoning (CBR) component. While the CBR system can use specific cases to determine when a transaction should be granted or denied authorization, the neural network can recognise general patterns of behaviour for the use and the misuse of credit cards and can use this knowledge in the classification task. This paper presents the architecture of the classification system proposed, highlighting the main aspects of each of its components. The system's performance is evaluated and conclusions are drawn. <s> BIB001 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Fuzzy Neural Network <s> This paper presents a new approach for learning general knowledge in a diagnostic case-based system through the use of a neural network. We take advantage of the self-adapting nature of the neural network to discover the most relevant features and combination of features for each diagnosis considered. The knowledge acquired by the network is interpreted and mapped into symbolic diagnosis descriptors, which are kept and used by the case-based system to guide its reasoning process, to retrieve cases from a case library and to build explanations. The neural network used in the learning process was the Combinatorial Neural Model, a network that has been combined with other symbolic approaches previously. The paper presents the method used to interpret the knowledge learned in the neural network, as well as the guidelines followed by the reasoning process of the CBR system. An initial experiment in clinical psychology is also reported, where the case-based model introduced here was used to learn and represent the psychological profile of patients in evaluation for heart transplant. <s> BIB002 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Fuzzy Neural Network <s> The early stages of design are characterised by imprecise and incomplete information about the features and properties of targeted product or process. Equipment selection is one of the decisions to be taken in an initial phase of design. Mixing is an example of process where the selection of the equipment is practically realised ‘by hand’. An application of rule-based or object-oriented tools in the selection of mixing equipment encounters many problems. The main reasons are the lack of well-structured knowledge and the great diversity of mixing problems. Case-based reasoning constitutes a good tool to handle the problems of mixing equipment selection. An instance generator for case-based reasoning system is described in this work. A fuzzy neural network system has been used to produce the cases. The following types of the equipment for heat exchange in stirred vessels have been considered: plain jacket, plain jacket with agitation nozzles, plain jacket with spiral baffles, dimple jacket, halfpipe jacket, helical coil, vertical coil and vertical plate coil. <s> BIB003
The fuzzy-set-theory-based neural networks have been used efficiently for aggregation of information in multi-criteria decision making systems, such as financial management and medical diagnosis  . In general, a fuzzy network can be trained to describe complex relationships among symbolic concepts. As discussed in Section 4.3, the main advantage of employing the fuzzy neural networks for a CBR system design, or integrating the network into a CBR system as a component, is that information contained in the network can be easily understood and integrated. Such a property is extremely important for a reasoning system, so as to explain its reasoning results. Furthermore, it is possible to transform the knowledge embodied in the connecting weights into a set of rules or symbolic representations. In addition, using the fuzzy network can be an aid when handling complex uncertainty in engineering practice, which is the usual situation a CBR system encounters in real-world applications. Compared with earlier work BIB003 BIB001 BIB002 , a generalised logical operator was used as a node transfer function to construct a fuzzy-set-based hierarchical network model  . The operator can behave both as a union operator and an intersection operator, in addition to being a compensatory operator. Such a generalised hybrid operator is expressed by the socalled ␥-model, as where variables {x i ͉x i [0,1]͖ are m inputs to a node in the network, ␦ i represents the connecting weight associated with x i , ⌺ m iϭ1 ␦ i ϭ m, and variable y is the node output that depends on {x i ͖ and {␦ i ͖. ␥ is a parameter that controls the degree of compensation between the union and intersection operation parts. Based on the ␥-model, a more complex and effective hierarchical network can be constructed for a CBR system, in which case, further research should be focused on the following three aspects: ¼ Appropriate network structure for a given problem of interest, which includes the number of hidden layers and the number of hidden nodes in each hidden layer. ¼ Efficient network learning strategy for speeding up the network training. Also, the control parameter ␥ may be considered as a parameter to be determined through the network training process. ¼ Appropriate criteria for pruning some connections in the network, where the dependences among the nodes linked by these pruned connections are weak.