What are the fuzzy neural network?
neural network fuzzy is software systems that try to approach the way the human brain works. They do this by using two key research areas in computer science technology - the development of logical software and architecture of neural network processing. Fuzzy logical software attempts to take into account the gray areas in the real world in the structure of computer software programs that go beyond simple yes or no. The artificial design of the neural network creates software nodes that mimic the functionality and complexity of how neurons interact in the human brain. Together, Fuzzy logic and design of the neuron network create a neuro-fuzzy system that scientists use to experiment with complex problems such as climate change, or for the development of artificial intelligence robotics.
Since 2011, the average microcomputer has been calculating with an incredible billion per second. This represents an exponential increase in speed by processing the first days of computer development, although such growth has not shownEstablishing ability to think in complex ways that even simple biological organisms do. This is partly due to the basic restrictions that computer processing still faces, and the fuzzy neural network is an attempt to solve these restrictions.
It is estimated that the average human brain performs 100 000 000 000 000 000 instructions every second using its nerve structure, which is analogous to how microprocessors work. On the other hand, the average computer system since 1999 was 24,000 times slower than this, and the early model since 1981 was 3,500,000 times slower than the human brain in calculations. This would take 8,000 personal computers complicated together with 2.1 Gigahertz processors available on the 2011 market to approach the speed of the average human brain. Supercomputer capable of pervying calculations as fast as the human brain would not compare the same strong force for analysisZU of the contradictory data of the real world, which is the place where fuzzy neural networks enter the game.
Key elements that make the fuzzy neural networks unique from other types of computer processing are their ability to recognize patterns to make me insufficient data to draw definitive conclusions and the ability to adapt to the environment. Fuzzy neural networks use nerve algorithms that are designed to change and grow when they meet new data files for processing. They do this by approaching problems from two different perspectives and combining results into meaningful solutions to problems.
Fuzzy Software is based on programming rules that allow the level of truth levels to estimate the contradictions in data that are evident from the human perspective. Determination of 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 SEbe and there was no medium range. The height would be 6 feet (1.83 meters) categorized as a short if below the average height, while someone 6 feet and 1 inch (1.85 meters) in height would be categorized as high. In fuzzy processing, the extent of what is considered to be a high versus short, is constantly changing because the group has changed and the decision would be taken along a more sensible gradient.
neuron networks, on the other hand, have no predefined rules from which it can be operated, and draw all their conclusions on the basis of observations. Operation without predefined rules can create unique knowledge of data that is not otherwise obvious if previous prerequisites have been made either in Fuzzy programming or traditional sets of programming. The results of fuzzy software and processing data of neural networks are combined in fuzzy nerve systems in a way that closer to how the biological organisms learn and adapt to their environment. As I customize the systemIt is the data that collects the way it processes this data to become more efficient in solving future problems.
neural processing, whether from nervous programming on a computer or from a biological brain, is a method where the added weight is given by certain data points based on observation results. Fuzzy element of fuzzy neural networks is used to more precisely modeling real conditions than in the past with traditional computer processors, although this fine modeling level may often not lead to a significant improvement in performance where fuzzy logic is used to control conventional computer controls. The final advantage of the fuzzy neural networks is that they have the potential to develop the level of basic independent thinking and decision -making that adapts to their environment around them.