Nguyen-Widrow layer initialization function
net = initnw(net,i)
initnw is a layer initialization function that initializes a layer's weights and biases according to the Nguyen-Widrow initialization algorithm. This algorithm chooses values in order to distribute the active region of each neuron in the layer approximately evenly across the layer's input space. The values contain a degree of randomness, so they are not the same each time this function is called.
initnw requires that the layer it initializes have a transfer function with a finite active input range. This includes transfer functions such as tansig and satlin, but not purelin, whose active input range is the infinite interval [-inf, inf]. Transfer functions, such as tansig, will return their active input range as follows:
activeInputRange = tansig('active') activeInputRange = -2 2
net = initnw(net,i) takes two arguments,
Index of a layer
and returns the network with layer i's weights and biases updated.
There is a random element to Nguyen-Widrow initialization. Unless the default random generator is set to the same seed before each call to initnw, it will generate different weight and bias values each time.
You can create a standard network that uses initnw by calling feedforwardnet or cascadeforwardnet.
To prepare a custom network to be initialized with initnw,
To initialize the network, call init.
The Nguyen-Widrow method generates initial weight and bias values for a layer so that the active regions of the layer's neurons are distributed approximately evenly over the input space.
Advantages over purely random weights and biases are
Few neurons are wasted (because all the neurons are in the input space).
Training works faster (because each area of the input space has neurons). The Nguyen-Widrow method can only be applied to layers
With a bias
With weights whose weightFcn is dotprod
With netInputFcn set to netsum
With transferFcn whose active region is finite
If these conditions are not met, then initnw uses rands to initialize the layer's weights and biases.