شبكات عصبية

Advantages of NNs.

1. High computation rates provided by MP
2. A great degree of robustness
3. Adaptation of connection weights based on current results so as to improve performance
4. Non-parametric


The Artificial Neuron

The basic computation element of a NN is called the artifice neuron or neuron or node
A general neuron model



Bias ?



X1 W1

Activation fn
Out put to
Input Xi Wi
Another
neuron

Xn Wn



Basis fn











The net input in represent ed by a net fn u (i) which also called basis fn the neuron activation by the activation fn f() .
The bias acts exactly as a weight on a connection from a unit whose activation is always 1
The simplest node sums the weighted inputs and passes the result through a non-linearity

i.e. Y=f(?Xi Wi +?)

the non-linear fn(f) or activation fn is typically used for the neuron in any particular layer of a neuron net. Some common activation fns. Are the step , ramp, sigmoid functions