In IT, a neural network is a system of programs and data structures that approximates the operation of the human brain. Usually a neural network involves a great number of processors operating in parallel, all with its own small sphere of knowledge and access to data in its local memory. In general, a neural network is primarily "trained" or fed large amounts of data and rules regarding data relationships (for instances, "A grandfather is elder than a person's father"). Then a program can tell the network how to act in response to an external stimulus (for instance, to input from a computer user who is interacting with the network) or can start activity on its own (within the limits of its access to the external world).
In making determinations, neural networks use numerous principles, by including genetic algorithms, fuzzy logic, gradient-based training, and Bayesian methods. Neural networks are sometimes described in terms of knowledge layers, with, normally, more complex networks having deeper layers. In feed forward systems, learned relationships regarding data can "feed forward" to higher layers of knowledge. Neural networks can also study temporal concepts and have been extensively utilized in signal processing and time series analysis.
Present applications of neural networks include: weather prediction, oil exploration data analysis, the interpretation of nucleotide sequences into biology labs, and the exploration of models of thinking and consciousness.
Benefits of neural networks:
Neural networks, along their remarkable capability to derive meaning from complex or imprecise data, can be utilized to extract patterns and detect trends that are too complicated to be noticed by either humans or other computer techniques. A trained neural network may be thought of as an "expert" in the category of information it has been given to examine. Then this expert can be used to provide projections given new situations of interest and answer "what if" questions.
Other advantages include:
An ability to study how to do tasks depends on the data given for initial and training experience.
An ANN can create its own representation or organization of the information it attain during learning time.
Real Time Operation:
ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this ability.
Fault Tolerance via Redundant Information Coding:
Partial destruction of a network leads to the corresponding degradation of performance. Though, some network capabilities may be retained even with major network damage.
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