W. Huang et al. / Ocean Engineering 30 (2003) 22752295
3.1. One-neuron model
By starting with a one-neuron model, it may be easier to understand the neural
network structure. A neuron is defined as an information-processing unit that is fun-
damental to the operation of a neural network. Fig. 3 shows a simple one-neuron
model to illustrate the neural network structure.
As shown in Fig. 3, there are three basic elements in an ANN:
(a) A set of connecting links, w, each of which is characterized by a weight of its
own. The weights on the connections from the input Xi (i = 1, ..., n) to the neuron
Y are wi (i = 1, ..., n).
(b) An adder, , for summing the weighted input signals; the operation constitute
a linear combiner, v:
(b) An activation function, f(.), for limiting the amplitude of the output of a neuron.
The output from the neuron model can be described by
There are several types of activation functions. Examples of activation functions
related to this study are given below.
i) linear function:
ii) sigmoid function:
where a is the slope parameter
One neuron structure.