Track 11

Artificial Neural Networks

Dr. Robert Hecht-Nielsen, explains a neural network as a computing system made up of a number of simple, highly related processing elements, which practice information by their dynamic state answer to external inputs.

The idea of ANNs is based on the trust that functioning of the human brain by creating the right connections, can be copied using silicon and wires as living neurons and dendrites. The human brain is collected of 86 billion nerve cells called neurons. They are linked to other thousand cells by Axons. Stimuli from the external environment or inputs from sensory organs are recognized by dendrites. These inputs make electric impulses, which rapidly travel through the neural network. A neuron can then send the communication to other neurons to handle the issue or does not send it forward. ANNs are composed of multiple nodes, which reproduce biological neurons of the human brain. The neurons are related by links and they interact with each other. The nodes can take input data and make simple operations on the data. The output of these operations is passed to other neurons. The yield at each node is called its activation or node value. Each link is connected with weight. ANNs are capable of learning, which takes place by varying weight values.

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