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What Is the Connection between Neural Networks and Fuzzy Logic?

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  • Written By: Ray Hawk
  • Edited By: E. E. Hubbard
  • Last Modified Date: 05 December 2016
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Neural networks and fuzzy logic are both usually software systems that are designed to recognize patterns in data or events and simulate natural human reactions and decision-making processes. Whereas traditional computational models utilize discrete calculations for output from the onset of turning on the system, neural networks and fuzzy logic require a period of training or learning in order to produce meaningful results. Conceptually, the antithesis to neural networks and fuzzy logic in advanced computer systems is the application of expert systems, which are preset data stores or knowledge bases that are compilations of previously established understanding by a variety of experts in a field.

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Both the inherent advantage and flaw in adaptive systems that employ neural networks and fuzzy logic is their predictive ability. They are non-linear statistical data modeling tools, which means that they may arrive at different conclusions to the same problem depending on the path taken to analyze the problem. Where an expert system based on standard programming constructs would decide if an individual were considered tall based on a clear cutoff point, say 6 feet (1.83 meters) or greater defines tall, where 5 feet 11 inches (1.8 meters) does not, neural networks and fuzzy logic make the decision based on analysis of supporting data, the number of individuals in a group and each one's height, how average heights for sub-groups within the group affect the overall perception of what is tall, and so on. This ability in humans is referred to as intuition, or the nature of looking at the world in a non-linear way and accounting for exceptions to the rule in making decisions.

Other terms used for neural networks and fuzzy logic systems include case-based reasoning, genetic algorithms, studies in chaos theory as it applies to software, and artificial intelligence, in general. The two systems tend to differ in their approach to solving subjective problems. Neural networks are a direct attempt to model the way that neurons function in the human brain, through a growth cycle of a artificial neural network that analyzes problems as it encounters them. Fuzzy logic, on the other hand, is a software construct that attempts to code for analysis of all the gray areas in the natural world, mathematically beforehand, and goes beyond binary 0/1 Boolean logic to include partial truths that are weighed against each other to arrive at a conclusion. This mimics the spectrum of value judgments that human beings continually make when a simple yes or no response to conditions is inadequate.

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