Learn something new every day More Info... by email
An adaptive neural network is a system that processes information and makes adjustments to the network when necessary. Such networks can be found in computer systems or in organic life forms. They are used to interpret large amounts of complex information and are the basis of modern artificial intelligence technology.
A man-made adaptive neural network, also called an artificial neural network, is modeled after the naturally occurring neural networks in the brains of humans and animals. They work using a series of information-gathering sensors — the neurons — that are interpreted by a central processing unit. These connections can alter and change the way they interact with the central processing unit based on their own evaluation of how to most efficiently carry out their functions.
There are two main ways an adaptive neural network "learns": supervised learning and unsupervised learning. Supervised learning requires a human counterpart who instructs the network on how to interpret and interact with various inputs. The purpose of this style of learning is to ensure that there are no errors in the methods that the adaptive neural network uses to process information, and to reinforce the desired actions of the network.
Non-supervised learning relies on the central processing unit interacting with its environment and making its own decisions on how it should operate based on its original programming. To do this it organizes and reorganizes the information it receives and makes predictions about what the results of changing this data could be. A network can either learn online or offline. Online learning means that network learns while it is also performing tasks. Offline learning requires the network to learn separately from acting.
Currently there are four main tasks that are performed by adaptive neural networks. They all deal with processing and interpreting patterns. First, there is clustering, where the network examines a number of patterns and groups related patterns into clusters.
A second task that an adaptive neural network may perform is recognizing and interpreting a pattern, such as written or spoken words. In doing this, it may attempt to understand completely unknown patterns based on its understanding of related patterns. Providing an estimate of the value of a function is the third main task, and is often used in science or engineering. The fourth main task that an adaptive neural network may perform is to make predictions of what will occur in the future if changes are made to certain data models.
An artificial neural network is a form of artificial intelligence and its most modern uses involve advanced robotic technology. It is more commonly used by data analysts, since their jobs deal with interpreting and sorting through large amounts of information. An artificial neural network can help an analyst organize her data, conduct research, and test possible changes to her company’s products and services. As technology becomes more advanced, applications of neural networks will become more common.
One of our editors will review your suggestion and make changes if warranted. Note that depending on the number of suggestions we receive, this can take anywhere from a few hours to a few days. Thank you for helping to improve wiseGEEK!