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We are presented with a few interesting possibilities regarding the interpretation of sensor readings. We can have the microcontroller mimic the function of neural and/or fuzzy logic devices.
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Intelligence
Fuzzy logic
In 1965, Lotfi Zadah, a Professor at the University of California at Berkeley, first published a paper on fuzzy logic. Since its inception, fuzzy logic has been both hyped and criticized. In essence, fuzzy logic attempts to mimic in computers the way people apply logic in grouping and feature determination. A few examples should clear this fuzzy definition. For instance, how is a warm, sunny day determined not to be warm but to be hot instead, and by whom The threshold of when someone considers a warm day hot depends on a person s personal heat threshold and the influence of his or her environment (see Fig. 6.27). There is no universal thermometer that states at 81.9 degrees Fahrenheit ( F) it is warm and at 82 F it is hot. Extending this example further, a group of people living in Alaska has a different set of temperature values for hot days when compared to a group of people living in New York, and both these values will be different from that of a group of people living in Florida. And let s not forget seasonal variations. A hot day is a different temperature in winter than summer. So what this boils down to is that classifications (for example, What is a hot day ) may be a range of temperatures determined by the opinions of a group of people. Further classifications can be differentiated by different groups of people. Any particular temperature will find membership in the group where it fits into the range of values. Sometimes a temperature will fit into two overlapping groups. True membership will then be determined by how a particular temperature varies from the median values. The idea of group and range classifications can be applied to many other things, like navigation, speed, and height. Let s use height for
6.27 Grading temperature from warm to hot, gradually or by step six
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Medium
Medium
Short
Tall Short Gaussian Binary Tall
Medium
Medium
Short Digitized
Tall
Short Fuzzy
Tall
6.28 Grouping people by height using different schemes
one more example. If we graph the height of 1000 people, our graph will resemble the first graph shown in Fig. 6.28. We can use this graph of heights to classify the heights into groups: short, medium, and tall. If we applied a hard rule that stated everyone under 5 7 is short and everyone taller then 6 0 is tall, our graph will resemble the second graph. This classifies a person who is 5 11.5 tall as medium, when in actuality the person s height is closer to the tall (6 0 and over) group. Instead of hard-and-fast rules typically employed by computers, people typically use soft and imprecise logic, or fuzzy logic. To implement fuzzy logic in computers we define groups and quantify the membership in that group. Groups overlap, as seen in the fourth graph of Fig. 6.28. So the person who is 5 11.5 tall is almost out of the medium group (small membership) and well into the tall group (large membership). Fuzzy logic provides an alternative to the digitized graph shown as the third graph of Fig. 6.28. A high-resolution digitized graph is
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