Fuzzy neural networks combine fuzzy logic software and neural network processing to mimic the human brain’s functionality. They recognize patterns with insufficient data and adapt to the environment. Fuzzy software estimates levels of truth when contradictions arise, while neural networks draw conclusions based on observation. The combination of both approximates how biological organisms learn and adapt. The ultimate benefit is the potential for independent thinking and decision-making that adapts to changing surroundings.
Fuzzy neural networks are software systems that attempt to approximate how the human brain works. They do this by using two key research areas in computer technology: fuzzy logic software development and neural network processing architecture. Fuzzy logic software attempts to explain real-world gray areas in the decision structure of computer software programs that go beyond simple yes-or-no choices. Artificial neural network design creates software nodes that mimic the functionality and complexity of how neurons interact in the human brain. Together, fuzzy logic and neural network design create a neuro-fuzzy system that researchers use for experimentation on complex problems such as climate change or to develop artificial intelligence robotics.
The average 2011 microcomputer is executing calculations at an incredible rate of billions of instructions per second. This represents an exponential increase in processing speed since the early days of computer development, although that growth has shown no ability to reason in the complex ways that even simple biological organisms do. This is partly due to the basic limitations that computer processing still faces, and fuzzy neural networks are an attempt to get around these limitations.
It is estimated that the average human brain executes 100,000,000,000,000 instructions every second using its neural structure which are analogous to how microprocessors work. In contrast, an average computer system in 1999 was 24,000 times slower than this, and an early 1981 model was 3,500,000 times slower than the human brain at performing calculations. It would take 8,000 intricately networked personal computers with 2.1 gigahertz processors available on the market as of 2011 to approximate the speed of an average human brain. A supercomputer capable of performing calculations at the speed of the human brain, however, would not equal the same reasoning power to analyze 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 computing are their ability to recognize patterns given insufficient data to draw firm conclusions, and their ability to adapt to the environment. Fuzzy neural networks use neural algorithms designed to change and grow as they encounter new data sets to process. They do this by approaching problems from two distinct viewpoints and combining the results into meaningful solutions to problems.
Fuzzy software relies on programming rules that allow estimates of levels of truth when contradictions arise in the data that are obvious from a human point of view. Determining who is “tall” versus who is “short” in a group of people, for example, using traditional computer processing, would create a definitive line where both groups were separated from each other and not c ‘was an intermediate interval. Someone 6 feet (1.83 meters) tall would be classified as short if below average height, while someone 6 feet 1 inch (1.85 meters) tall would be classified as tall. With fuzzy processing, the range of what is considered high versus low would continually change as the group changed, and decisions would be made along a more reasonable gradient.
Neural networks, in contrast, have no predefined rules to operate by and draw all of their conclusions based on observation. Operating without predefined rules can create unique insights into the data that are not otherwise apparent when prior assumptions have been made in fuzzy programming or traditional programming rule sets. The results of fuzzy software and neural network data processing are combined into fuzzy neural systems in a way that approximates how biological organisms learn and adapt within their environments. As the system adapts to the data it collects, it changes the way 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 in which added weight is given to certain data points based on observational results. The fuzzy element of fuzzy neural networks serves to model real-world conditions more accurately than was previously possible with traditional computer processors, although this level of fine modeling often does not lead to significant performance improvements where fuzzy logic is used as a control over conventional computer controls. The ultimate benefit of fuzzy neural networks is that they have the potential to develop a rudimentary level of independent thinking and decision-making that adapts to changing surroundings.
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