Artificial Neural Network
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Simplified view of a feedforward artificial neural network The term neural network was traditionally used to refer to a network or circuit of biological neurons . [ 1 ] The modern usage of the term often refers to artificial neural networks , which are composed of artificial neurons or nodes. Thus the term may refer to either biological neural networks are made up of real biological neurons or artificial neural networks for solving artificial intelligence problems. Unlike von Neumann model computations, artificial neural networks do not separate memory and processing and operate via the flow of signals through the net connections, somewhat akin to biological networks. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset.
An artificial neural network , often just named a neural network , is a mathematical model inspired by biological neural networks . A neural network consists of an interconnected group of artificial neurons , and it processes information using a connectionist approach to computation . In most cases a neural network is an adaptive system changing its structure during a learning phase. Neural networks are used for modeling complex relationships between inputs and outputs or to find patterns in data. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain . [ edit ] Background
An artificial neuron is a mathematical function conceived as a crude model, or abstraction of biological neurons . Artificial neurons are the constitutive units in an artificial neural network . Depending on the specific model used, it can receive different names, such as semi-linear unit , Nv neuron , binary neuron , linear threshold function or McCulloch–Pitts (MCP) neuron .