Neuro-fuzzy-methods

Dazzi, D., Taddei, F., Gavaiini, A., Uggeri, E., Negro, R. and Pezzarossa, A. (2001) The control of blood glucose in the critical diabetic patient a neuro-fuzzy method. Journal of Diabetes and its Complications, 15 (2), 80-7. 

It is recommended to use data fusion techniques such as the Fuzzy approach or other methods like the Neuro-Fuzzy on surface data to locate the most promising sites for drilling. 

In many of the early applications of fuzzy logic, the s and B s in the if-then rules had to be calibrated by cut-and-trial to achieve a desired level of performance. During the past few years, however, the techniques related to the induction of rules from observations have been developed to a point where the calibration of rules—by induction from input-output pairs—can be automated in a wide variety of cases. Particularly effective in this regard are techniques centered on the use of neural network methods and genetic computing for purposes of system identification and optimization. Many of the so-called neuro-fuzzy and fuzzy-genetic systems are of this type. 

Many types of combinations between fuzzy systems and neural networks have been proposed and studied. In what follows, we use the definitions and classification proposed by Detlef Nauck, from the Department of Computer Science, Technical University of Braunschweig, GermanyA neuro-fuzzy or neural fuzzy system is a combination of neural networks and fuzzy systems in such a way that neural networks are used to determine parameters of fuzzy systems. The main intention of a neuro-fuzzy approach is to create or improve a fuzzy system by means of neural network methods. The system should always be interpretable in terms of fuzzy if-then rules. A fuzzy... 

Neuro fuzzy modeling is a useful technique that combines the advantages of neural networks and fuzzy inference systems. In this approach, the fuzzy model is architecturally the same as a neural network. In this case one could use, for example error back-propagation to train the network to find the parameters of the fuzzy model. The most well-known method is the so-called ANFIS method the Adaptive-Network based Fuzzy Inference System. The method will be explained in this chapter and several examples will be developed as an illustration. 

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