Neural networks and fuzzy logic are software systems that recognize patterns and simulate human decision-making processes. They require training to produce meaningful results and have the advantage of predictive ability but can arrive at different conclusions. They differ in their approach to solving subjective problems, with neural networks modeling the way neurons work and fuzzy logic attempting to code for gray areas.
Neural networks and fuzzy logic are both usually software systems designed to recognize patterns in data or events and simulate natural human reactions and decision-making processes. While traditional computational models use discrete computations to output from the very beginning of system power-up, neural networks and fuzzy logic require a period of training or learning to produce meaningful results. Conceptually, the antithesis to neural networks and fuzzy logic in advanced computing systems is the application of expert systems, which are pre-set data stores or knowledge bases that are previously established collections of knowledge from a variety of experts in a field.
Both the inherent advantage and the flaw in adaptive systems employing neural networks and fuzzy logic is their predictive ability. They are non-linear statistical data modeling tools, which means they can arrive at different conclusions about the same problem depending on the path they take to analyze the problem. Where an expert system based on standard programming constructs would decide whether an individual was considered tall based on a clear point of separation, e.g. 6 feet (1.83 meters) or taller 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 the height of each, how does the average heights for subgroups within the group affect on the overall perception of what is high and so on. This ability in humans is referred to as intuition, or the nature of looking at the world non-linearly and factoring in exceptions to the rule in making decisions.
Other terms used for neural networks and fuzzy logic systems include case-based reasoning, genetic algorithms, software chaos theory studies, 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 neurons work in the human brain, through a growth cycle of an 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 the analysis of all gray areas in the natural world, mathematically in advance, and goes beyond binary 0/1 Boolean logic to include partial truths that are weigh against each other to come to a conclusion. This mimics the spectrum of value judgments humans make all the time when a simple yes or no answer to conditions is inadequate.
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