defuzzification

A common and useful defuzzification technique is center of gravity.

There are many different methods of defuzzification available, including the following:

The program will evaluate all the rules that fire and use an appropriate defuzzification method to generate its actual response.

Defuzzification is interpreting the membership degrees of the fuzzy sets into a specific decision or real value.

These results are combined to give a specific ("crisp") answer, the actual brake pressure, a procedure known as "defuzzification".

The second step of Output Processing, which occurs after type-reduction, is still called defuzzification.

The two outputs are then defuzzified through centroid defuzzification:

Determine the defuzzification method.

There are as many type-reduction methods as there are type-1 defuzzification methods.

Representing fuzzification, fuzzy inference and defuzzification through multi-layers feed-forward connectionist networks.

Defuzzification is the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and corresponding membership degrees.

CDD (constraint decision defuzzification)

FCD (fuzzy clustering defuzzification)

GLSD (generalized level set defuzzification)

The first step of defuzzification typically "chops off" parts of the graphs to form trapezoids (or other shapes if the initial shapes were not triangles).

BADD (basic defuzzification distributions)

SLIDE (semi-linear defuzzification)

The diagram below demonstrates max-min inferencing and centroid defuzzification for a system with input variables "x", "y", and "z" and an output variable "n".

Depending on the FIS type, there are several layers that simulate the processes involved in a fuzzy inference like fuzzification, inference, aggregation and defuzzification.

In centroid defuzzification the values are OR'd, that is, the maximum value is used and values are not added, and the results are then combined using a centroid calculation.

The simplest but least useful defuzzification method is to choose the set with the highest membership, in this case, "Increase Pressure" since it has a 72% membership, and ignore the others, and convert this 72% to some number.

A process of defuzzification is said to occur, when fuzzy concepts can be logically described in terms of (the relationships between) fuzzy sets, which makes it possible to define variations in the meaning or applicability of concepts as quantities.

The fuzzification of the inputs and the defuzzification of the outputs are respectively performed by the input linguistic and output linguistic layers while the fuzzy inference is collectively performed by the rule, condition and consequence layers.

Conventional heuristic-based fuzzy logic systems cannot learn and fail to work with many complex applications, the company says, noting that NeuFuz4 gets round the problem by using proprietary new defuzzification, rule inferencing and antecedent processing algorithms based on a modified back propagation algorithm.