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Neural Network

Soft computing differs from conventional(hard )
Computing in that , unlike hard computing .it is
Tolerant of precision uncertainity, partial truth and approximation. In effect, the role model for soft computing is the human mind.
The principal constituents, i.e., tools, techniques, of soft computing are :-fuzzy logic ,neural network ,genetic algorithm etc.


Evolution of Neural Networks


Studied the brain
Each neuron in the brain has a relatively simple function
But - 10 billion of them (60 trillion connections)
Act together to create an incredible processing unit
The brain is trained by its environment
Learns by experience
Compensates for problems
by massive parallelism

Inspiration from neurobiology


A neuron: many-inputs / one-output unit
output can be excited or not excited
incoming signals from other neurons determine if the neuron shall excite ("fire")
Output subject to attenuation in the synapses, which are junction parts of the neuron
Basically, a biological neuron receives inputs from other sources, combines them in some way, performs a generally nonlinear operation on the result, and then output the final result.

neuron


Inputs: dentrites
Processing: soma
Outputs: axons
Synapses: electrochemical contact between neurons