1 Network Objects, Data, and Training Styles
Neuron Model
Simple Neuron
The fundamental building block for neural networks is the single-input
neuron, such as this example.
There are three distinct functional operations that take place in this example
neuron. First, the s calar inp u t p is multiplied by the scalar weight w to form
the product wp, again a scalar. Second, the weighted input wp is added to
the scalar bias b to form the net input n. (In this case, you can view the bias
as shifting the function f to the left by a n amount b. The bias is much like
a weight, except that it has a constant input of 1.) Finally, the net input is
passed through the transfer function f, which produces the scalar output a.
The names given to these three processes are: the weight function, the net
input function and the transfer function.
For many types of neural networks, the weight function is a product of a
weight times the input, but other weight functions (e.g., the distance between
the weight and the input, |w − p|) are sometimes used. (See “Weight
Functions” in the Neural Network Toolbox Reference for a list of weight
functions.) The most common net input function is the summation of the
weighted inputs with the bias, but other operations, such as multiplication,
can be used. (See “Net Input Functions” in the Reference for a list of net input
functions.) C hapter 5, “Radial Basis Networks” discusses how distance can
be used as the weight function and multiplication can be used as the net
input function. There are also many types of transfer functions. Examples of
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