A feedforward neural network is a type of neural network where information travels in a single direct path. It is commonly used in data mining and pattern recognition. The network consists of an input layer, hidden layer, and output layer. The more experimental data fed into the network, the more accurate its responses become.
A feedforward neural network is a type of neural network in which unit connections do not travel in a loop, but rather in a single direct path. This differs from a recurrent neural network, where information can move both forward and backward throughout the system. A feedforward neural network is perhaps the most common type of neural network, as it is one of the easiest to understand and configure. These types of neural networks are used in data mining and other areas of study where predictive behavior is required.
A neural network is an artificial intelligence network designed to loosely mimic the “thought” processes of a human brain. By inserting strings of data into the network, the computer is given the opportunity to “learn” the patterns passing through it, enabling it to correctly identify responses and provide trend analysis. They are used in activities where some learning and pattern recognition is required, such as data mining. Data mining is simply analyzing trends from a collection of information, such as analyzing consumer buying trends and stock market progressions.
Information traveling through a feedforward neural network enters the input layer, travels through the hidden layer, and emerges from the outer layer of the network, providing the end user with an answer to his question. An input level is simply where the user enters raw data or information parameters. The heart of the transaction takes place in the hidden layer, where the computer uses its “experience” of handling similar data to produce an estimated response. The information is funneled through the output layer, where a response is provided to the end user.
A feedforward neural network typically becomes more efficient as the end user feeds it more and more experimental data. Just like calculating an average, you will get a more accurate result by using a large number of test events. For example, the probability of rolling a “1” on a six-sided die is 16.667%; but it will take hundreds or thousands of simulations before the calculated mean is confirmed through the use of real data. Feedforward neural networks are the same; their answers will become more accurate with time and experience.
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