A Hybrid Expert System Architecture for Medical Diagnosis

Brasil, L. M.; Azevedo, Fernando Mendes de; Barreto, Jorge Muniz

doi:10.1007/978-3-7091-6492-1_33

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In recent years, hybrid intelligent systems (HIS) have drawn an increasing research interest. This approach has been successfully applied in various areas, such as speech and natural language understanding [1], [2], robotics [2], medical diagnosis [3], fault diagnosis of industrial equipment [2], financial applications [4], bioinformatics [5], predictive toxicology [6]. A particular attention is paid to HIS incorporating connectionist structures, known as hybrid neural systems [2].…”

Section: Introductionmentioning

confidence: 99%

A Study of Neural and Fuzzy Parameters for Explicit and Implicit Knowledge-based Systems

Neagu¹

2004

Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Deve

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In this paper, a framework of a unified neural and neuro-fuzzy approach to integrate implicit and explicit knowledge in hybrid intelligent systems is presented. In the developed hybrid system, training data used for neural and neuro-fuzzy models represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy rules, directly mapped into equivalent connectionist structures. A formal model for a hybrid intelligent system implemented as neural, neuro-fuzzy and fuzzy modules is proposed. Furthermore, this paper explores the influences of the main identified parameters of the proposed model on the accuracy of the hybrid intelligent system in a predictive data mining application.

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