Hybrid Intelligent Diagnosis Approach Based on Neural Pattern Recognition and Fuzzy Decision-Making

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decisionmaking. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory Amine Chohra Paris-East University, France Nadia Kanaoui Paris-East University, France Véronique Amarger Paris-East University, France Kurosh Madani Paris-East University, France Hybrid Intelligent Diagnosis Approach Based on Neural Pattern Recognition and Fuzzy Decision-Making DOI: 10.4018/978-1-60960-818-7.ch3.7