Artificial neural network
There is no precise agreed to what a neural network, but most people believe that a network of simple processing elements (neurons), which can exhibit complex global behavior defined by connections between processing elements among researchers as to include definitions and element parameters. Technology for the original inspiration and central nervous system neurons (and their axons dendrites, and synapses), which its most significant information processing elements (see Neuroscience) formed the test came from. A neural network model, simple nodes (variously called “neurons”, “neurodes”, “PE” (“processing elements”) or “units”) are linked together to form a network of nodes – hence the The term “neural networks.” Although a neural network have to be adaptive copy is not practical to use it to produce a desired signal flow in the network connection strength (weight) comes with algorithms designed to change.
The network also means that tasks are performed collectively and in parallel by the units, there is clear delineation of subtasks instead of being similar to biological neural networks which is assigned to different units (see also connectionism). Currently, the term “artificial neural network (N) for the neural network statistics, cognitive psychology and artificial intelligence to refer mostly to the working model. Neural networks in the mind of the central nervous system (CNS) designed with the simulation model theoretical neuroscience (computational neuroscience) in a subject.
In modern software implementations of artificial neural network approach inspired by biology, for the most part a more practical approach based on statistics and signal processing have been left. These systems, neural networks or parts of neural networks (such as artificial neurons) in some larger systems that combine both adaptive and non-adaptive elements are used as components. While such adaptive systems more general approach is more suitable to solve real world problems, yet traditional artificial intelligence connectionist models what to do with less. What they have in common, however, non-linear, distributed, parallel and local processing and optimization theory.
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