logical neuron. The term was modified slightly to indicate an artificial nature. Memory is stored by a certain pattern of the connection weights between the neurodes. Processing information is performed by changing and spreading the connection's weights among the network. Before it can be used a neural network must be trained. Some can learn by themselves, some require training by doing, and others learn by trial and error. A computer learns by naturally associating items the computer is taught and grouping them together physically. Additionally, a computer can retrieve stored information from incomplete or partially incorrect clues. Neural networks are able to generalize categories based on specifics of the contents. Lastly, it is highly fault tolerant. This means that the network can sustain a large amount of damage and still function. Its performance fades proportionally as the neurodes disappear (Butler and Caudill 8). This type of system is inherently an excellent design for any application that requires little human intervention and that must learn on the go. Created by Lotfi Zadeh almost thirty years ago, fuzzy logic is a mathematical system that deals with imprecise descriptions, such as "new", "nice", or "large" (Schmuller 14). This concept was also inspired from biological roots. The inherent vagueness in everyday life motivates fuzzy logic systems (Schmuller 8). In contrast to the usual yes and no answers, this type of system can distinguish the shades in-between. In Los Angeles a fuzzy logic system is used to analyze input from several cameras located at different intersections (Barron 114). This system provides a "smart light" that can decide whether a traffic light should be changed more often or remain green longer. In order for these "smart lights" to work the system assigns a value to an input and analyzes all the inputs at once. Those inputs that have the highest value get the highest amount of attention. For example, ...
