A large number of techniques, such a neural networks and neurofuzzy systems, are used to produce empirical models based in part or in whole on observed data. A key stage in the modelling process is the selection of features. Irrelevant or noisy features increase the complexity of the modelling problem, may introduce additional costs in gathering unneeded data, and frequently degrade modelling p...

This paper presents a hybrid soft computing technique for the study of time varying processes based on a combination of neurofuzzy techniques with evolutionary algorithms, in particular, genetic algorithms . Two problems are simultaneously addressed: the discovery of patterns of dependency in general multivariate dynamic systems (in an optimal or quasi-optimal sense), and the construction of a ...

This paper presents an efficient hybrid neurofuzzy control scheme for synchronous generator. The scheme proves to be beneficial as the control scheme targets for the better control of Synchronous generator when used along with other sub systems. The system is stabilize after t=0.13. The control parameter the performances indices of IAE are 4.755 for fuzzy and 7.242 for hybrid neuro fuzzy and fo...

This paper proposes a structure of classes that uses the concepts of object-oriented programming to develop a library based on computational intelligence techniques for optimization in electromagnetics. In this library, artificial neural networks and neurofuzzy networks are considered. Based on this division, the required classes for the library implementation are presented. Finally, the TEAM w...

In this paper, intelligent perception-based systems are proposed in application to medical diagnosis problems. The systems are created by use of connectionist neurofuzzy architectures that reflect fuzzy IFTHEN rules. The perception-based rules can be generated from examples by the system. Fuzzy reasoning, according to these rules, is considered within the framework of the computational theory o...

This paper describes, in a neurofuzzy framework, a method for the classification of different modes of radiowave propagation, followed by generation of linguistic rules justifying a decision. Weight decay during neural learning helps in imposing a structure on the network, resulting in the extraction of logical rules. Use of linguistic terms at the input enables better human interpretation of t...