Fuzzy Rule-Based System
6.6 Fuzzy Inference System
6.6.3 Mamdani’s Fuzzy Inference Method
Mamdani’s fuzzy inference method is the most commonly seen fuzzy method- ology. Mamdani’s method was among the first control systems built using fuzzy set theory. It was proposed by Mamdani (1975) as an attempt to con- trol a steam engine and boiler combination by synthesizing a set of linguistic control rules obtained from experienced human operators. Mamdani’s effort was based on Zadeh’s (1973) paper on fuzzy algorithms for complex systems and decision processes.
Mamdani type inference, as defined it for the Fuzzy Logic Toolbox, ex- pects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output variable that needs defuzzifica- tion. It is possible, and in many cases much more efficient, to use a single spike as the output membership function rather than a distributed fuzzy set.
This is sometimes known as a singleton output membership function, and it can be thought of as a pre-defuzzified fuzzy set. It enhances the efficiency of the defuzzification process because it greatly simplifies the computation re- quired by the more general Mamdani method, which finds the centroid of a two-dimensional function. Rather than integrating across the two-dimensional function to find the centroid, the weighted average of a few data points. Sugeno type systems support this type of model. In general, Sugeno type systems can be used to model any inference system in which the output membership func- tions are either linear or constant.
An example of a Mamdani inference system is shown in Fig. 6.2. To com- pute the output of this FIS given the inputs, six steps has to be followed:
IF Rule THEN
Strength
and x
x
y
y
x0 y0
z
z
z and
Input Distributions
Output Distribution
Fig. 6.2.A two input, two rule Mamdani FIS with crisp inputs
1. Determining a set of fuzzy rules
2. Fuzzifying the inputs using the input membership functions
3. Combining the fuzzified inputs according to the fuzzy rules to establish a rule strength
4. Finding the consequence of the rule by combining the rule strength and the output membership function
5. Combining the consequences to get an output distribution
6. Defuzzifying the output distribution (this step is only if a crisp output (class) is needed).
The following is a more detailed description of this process Creating Fuzzy Rules
Fuzzy rules are a collection of linguistic statements that describe how the FIS should make a decision regarding classifying an input or controling an output.
Fuzzy rules are always written in the following form:
if (input 1 is membership function 1) and/or (input 2 is membership function 2)and/or. . .then (outputn is output membership functionn).
For example:
if temperature is high and humidity is high then room is hot.
There would have to be membership functions that define high temper- ature (input 1), high humidity (input 2), and a hot room (output 1). This process of taking an input such as temperature and processing it through a membership function to determine “high” temperature is called fuzzification and is discussed in section, “Fuzzification.” Also, “AND”/“OR” in the fuzzy rule should be defined. This is called fuzzy combination and is discussed in following section.
Fuzzification
The purpose of fuzzification is to map the inputs from a set of sensors (or features of those sensors such as amplitude or spectrum) to values from 0 to 1 using a set of input membership functions. In the example shown in Fig. 6.2, there are two inputs, x0 and y0 shown at the lower left corner. These inputs are mapped into fuzzy numbers by drawing a line up from the inputs to the input membership functions above and marking the intersection point.
These input membership functions, as discussed previously, can represent fuzzy concepts such as “large” or “small,” “old” or “young,” “hot” or “cold,”
etc. For example, x0 could be the EMG energy coming from the front of the forearm and y0 could be the EMG energy coming from the back of the forearm. The membership functions could then represent large amounts of tension coming from a muscle or small amounts of tension. When choosing the input membership functions, the definition of large and small may be different for each input.
122 6 Fuzzy Rule-Based System Consequence
The consequence of a fuzzy rule is computed using two steps:
1. Computing the rule strength by combining the fuzzified inputs using the fuzzy combination process discussed in previous section. This is shown in Fig. 6.2. In this example, the fuzzy “AND” is used to combine the membership functions to compute the rule strength.
2. Clipping the output membership function at the rule strength.
Combining Outputs into an Output Distribution
The outputs of all of the fuzzy rules must now be combined to obtain one fuzzy output distribution. This is usually, but not always, done by using the fuzzy “OR.” Figure 6.2 shows an example of this. The output membership functions on the right-hand side of the figure are combined using the fuzzy OR to obtain the output distribution shown on the lower right corner of the Fig. 6.2.
Defuzzification of Output Distribution
In many instances, it is desired to come up with a single crisp output from an FIS. For example, if one was trying to classify a letter drawn by hand on a drawing tablet, ultimately the FIS would have to come up with a crisp number to tell the computer which letter was drawn. This crisp number is obtained in a process known as defuzzification. There are two common techniques for defuzzifying:
1. Center of mass. This technique takes the output distribution and finds its center of mass to come up with one crisp number. This is computed as follows:
z=
!q
j=1Zjuc(Zj)
!q
j=1uc(Zj) ,
wherezis the center of mass anduc is the membership in classcat value zj. An example outcome of this computation is shown in Fig. 6.3.
2. Mean of maximum. This technique takes the output distribution and finds its mean of maxima to come up with one crisp number. This is computed as follows:
z= l j=1
zj l ,
wherezis the mean of maximum,zjis the point at which the membership function is maximum, andlis the number of times the output distribution reaches the maximum level. An example outcome of this computation is shown in Fig. 6.4.
Output Distribution
Z*
Fig. 6.3.Defuzzification using the center of mass Output
Distribution
Z*
Fig. 6.4.Defuzzification using the mean of maximum
Fuzzy Inputs
In summary, Fig. 6.5 shows a two input Mamdani FIS with two rules. It fuzzi- fies the two inputs by finding the intersection of the crisp input value with the input membership function. It uses the minimum operator to compute the fuzzy AND for combining the two fuzzified inputs to obtain a rule strength.
It clips the output membership function at the rule strength. Finally, it uses the maximum operator to compute the fuzzy OR for combining the outputs of the two rules.