**9. References**


With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision making, neural network and some other fuzzy application systems . Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this chapter, we reviewed some recent ranking methods, which will be useful for the

This work was partly supported by Islamic Azad University, FiroozKooh Branch.

Abbasbandy, S. and Asady, B. (2006). Ranking of fuzzy numbers by sign distance, Inform.

Abbasbandy, S. and Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy

Abbasbandy, S. and Hajjari, T. (2011). An improvement on centroid point method for

Asady, B. (2010). The revised method of ranking LR fuzzy number based on deviation

Bodjanova, S. (2005). Median value and median interval of afuzzy number, Inform. Sci. 172:

Chen, S. J. and Chen, S. M. (2003). A new method for handling multicriteria fuzzy decision-

making problems using FN-IOWA operators, Cybernetic and Systems, 34: 109-137.

Fig. 5.

**7. Conclusion** 

**8. Acknowledgment** 

73-89.

**9. References** 

researchers who are interested in this area.

Sci. 176: 2405-2416.

numbers, Comput. Math. Appl. 57: 413-419.

ranking of fuzzy numbers, J. Sci. I.A.U. 78: 109-119.

degree, Expert Systems with Applications, 37: 5056-5060.


**4** 

*Mexico* 

**Neuro-Fuzzy Digital Filter** 

*2Superior School of Mechanical and Electrical Engineering,* 

*1Computing Research Centre, México D. F* 

*Department of Micro Electronics Research* 

José de Jesús Medel1, Juan Carlos García2 and Juan Carlos Sánchez2

An artificial neural net is a computational model which imitates natural biological system actions, through neurons that adapt their gains as occurs in the brain, and these are interconnected constructing a neural net system (Nikola, 1996) (Medel, García y Sánchez,

Fig. 1. Neural Network Interconnections (Source: Benedict Campbell 2008).

The Biological neuron is described illustratively in figure 2, taking into account a biological

In traditional concepts a neuron operates receiving signals from other neurons through bioelectrical connections, called *synapses*. The combination of these signals, in excess of a certain *threshold* or *activation* level, will result in the neuron *firing* that is sending a signal on to other interconnected neurons. Some signals act as *excitations* and others as *inhibitions* to a

These acts applied in a hundred billion interconnected neurons generate "thinking actions". Each neuron has a body, called the *soma*. The soma is much like the body of any other cell, containing the cell nucleus, various bio-chemical factors and other components that support

**1. Introduction 1.1 Neural net** 

description.

neuron firing.

2008), shown in figure 1.

