Fuzzy neural networks are software systems that attempt to approximate the way in which the human brain functions. They do this by utilizing two key research areas in computer science technology — fuzzy logic software development and neural network processing architecture. Fuzzy logic software attempts to account for real-world gray areas in the decision making structure of computer software programs that go beyond simple yes or no choices. Artificial neural network design creates software nodes that imitate the functionality and complexity of how neurons interact in the human brain. Together, fuzzy logic and neural network design creates a neuro-fuzzy system that researchers use for experimentation on complex problems such as climate change, or to develop artificial intelligence robotics.
The average microcomputer as of 2011 performs calculations at an incredible rate of billions of instructions per second. This represents an exponential increase in processing speed from the early days of computer development, though such growth has shown no capability towards reasoning in the complex ways that even simple biological organisms do. This is in part due to basic limitations that computer processing still faces, and fuzzy neural networks are an attempt to work around these limitations.
It is estimated that the average human brain carries out 100,000,000,000,000 instructions every second using its neural structure that are analogous to how microprocessors function. By contrast, an average computer system as of 1999 was 24,000 times slower than this, and an early model as of 1981 was 3,500,000 times slower than the human brain in performing calculations. It would take 8,000 personal computers intricately networked together with 2.1 gigahertz processors available on the 2011 market to approximate the speed of an average human brain. A supercomputer capable of performing calculations as fast as the human brain, however, would not equate to the same reasoning power for analyzing conflicting real world data, which is where fuzzy neural networks come into play.
The key elements that make fuzzy neural networks unique from other types of computer processing are their ability at pattern recognition given insufficient data to draw definitive conclusions, and the ability to adapt to the environment. Fuzzy neural networks utilize neural algorithms that are designed to change and grow as they encounter new data sets to process. They do this by approaching problems from two distinct points of view and combining the results into meaningful solutions to problems.
Fuzzy software is based on programming rules that allow for estimating levels of truth when contradictions arise in data that are obvious from a human perspective. Determining who is “tall” versus who is “short” in a group of people, for instance, using traditional computer processing, would create a definitive line where both groups were separated from one another and there was no intermediate range. Someone 6 feet (1.83 meters) in height would be categorized as short if below average height, whereas someone 6 feet and 1 inch (1.85 meters) in height would be categorized as tall. With fuzzy processing, the range of what is considered tall versus short would be continually changing as the group changed and decisions would be made along a more reasonable gradient.
Neural networks, by contrast, have no predefined rules from which to operate, and draw all of their conclusions based on observation. Operating without predefined rules can create unique insights about data that are not otherwise apparent when prior assumptions have been made in either fuzzy programming or traditional programming rule sets. The results of fuzzy software and neural network data processing are combined in fuzzy neural systems in a way that approximates how biological organisms learn and adapt within their environments. As the system adapts to the data that it gathers, it changes the way that it processes that data to become more efficient at solving future problems.
Neural processing, whether from neural programming in a computer or from a biological brain, is a method where added weight is given to certain data points based on observational results. The fuzzy element of fuzzy neural networks serves to more accurately model real conditions than was possible in the past with traditional computer processors, though this fine level of modeling may often not lead to significant performance improvements where fuzzy logic is used as a control over conventional computer controls. The ultimate advantage of fuzzy neural networks is that they have the potential to develop a level of rudimentary independent thinking and decision making that adapts as their environment changes around them.