Artificial neural networks mimic the human brain using simulated neurons and stimuli. They are used in gaming, robotics, and data processing, and use learning models such as supervised, unsupervised, or reinforced learning. Fuzzy logic helps fill gaps in data for better results. Machine committees provide multiple opinions for decision modeling.
An artificial neural network is a name for a type of computer technology that tries to mimic the human brain. An artificial neural network or ANN includes simulated neurons and stimuli for attempts to reproduce brain functions. This wide range of software and devices use models of neural algorithms to create decision-making processes that planners hope will closely mimic human thought processes. Artificial neural networks are a major advance over the relatively primitive ideas about computers in previous decades.
Neural network software is traditionally applied to gaming and other activities involving relatively calculated human thought. In a more biophysical sense, neural networks are based on examining how neurons in the brain communicate and transmit messages. Neural network applications include the interaction of various functions, where engineers examine the total production output to see how these artificial neural network systems can effectively mimic human thinking. A variety of “real world applications” for ANN include regression analysis, function approximation, robotics, and general data processing.
Various types of artificial neural networks have been developed for different research arrangements. These use different types of learning models such as supervised, unsupervised or reinforced learning. Types of neural networks include a one-way feedforward neural network, a radial basis function or RBF network, a Kohonen self-organizing network, and even modular neural networks where a larger network is composed of several smaller ones.
Another type of new structure applied to artificial neural networks is often called a “machine committee” in which various network structures each provide their own “vote” or “opinion” in a decision modeling process. This is also sometimes called an associative neural network or ASNN. The benefit of this type of research is apparent to engineers who believe that ASNN can help model human group decision making or other complex models in some ways similar to the individual decision models provided by ANN.
A principle that is often used by an artificial neural network is called “fuzzy logic”. The word “fuzzy” is used to describe any gaps in data or knowledge. Neural networks are often able to fill some data or knowledge gap by educated guesses and statistical predictions, which is in contrast to the rigid binary “yes or no” logic traditionally associated with electronic decision making. Overcoming fuzzy logic helps neural networks deliver better results in simulations. Using the building blocks of previous research, designers and engineers skilled with artificial neural networks are continually improving what these tools can do to push the boundaries of our knowledge on our minds.
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