C++ Neural Networks and Fuzzy Logic: Preface

Crisp Input RangeFuzzy Variable

0–2VeryLittle

1.5–4ALittle

3–7AGoodAmount

6–8.5ALot

7.5–10AWholeLot

Figure 16.5

  Fuzzy membership functions for the output HeatKnob.

Step Four: Create a Fuzzy Rule Base

Now that you have the inputs and the output defined in terms of fuzzy variables, you need only specify what

actions to take under what conditions; that is, you need to construct a set of rules that describe the operation of

the FLC. These rules usually take the form of IF–THEN rules and can be obtained from a human expert

(heuristics), or can be supplied from a neural network that infers the rules from behavior of the system. We

mentioned this idea in Chapter 3.

Let us construct a rule base for our design example. For the two inputs, we define the matrix shown in Table

16.15. Our heuristic guidelines in determining this matrix are the following statements and their converses:

1.  When the temperature is low, the HeatKnob should be set higher than when the temperature is

high.

2.  When the volume of water is low, the HeatKnob does not need to be as high as when the volume

of water is high.

NOTE:  In FLCs, we do not need to specify all the boxes in the matrix. That is perfectly

fine. No entry signifies that no action is taken, for example, in the column for

SenseTemp=XL, no action is required since the temperature is already at or above the target

temperature.

C++ Neural Networks and Fuzzy Logic:Preface

Step Three: Set Up Fuzzy Membership Functions for the Output(s)

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Table 16.15 Fuzzy Rule Base for the Example Design, Output HeatKnob