Neural Network Toolbox

hardlim

Hard limit transfer function

Graph and Symbol

Syntax

A = hardlim(N)

info = hardlim(code)

Description

The hard limit transfer function forces a neuron to output a 1 if its net input reaches a threshold, otherwise it outputs 0. This allows a neuron to make a decision or classification. It can say yes or no. This kind of neuron is often trained with the perceptron learning rule.

hardlim is a transfer function. Transfer functions calculate a layer's output from its net input.

hardlim(N) takes one input,

• N -- S x Q matrix of net input (column) vectors

and returns 1 where N is positive, 0 elsewhere

hardlim(code) returns useful information for each code string,

• 'deriv' -- Name of derivative function

  'name' -- Full name

  'output' -- Output range

  'active' -- Active input range

Examples

Here is the code to create a plot of the hardlim transfer function.

• n = -5:0.1:5;
  a = hardlim(n);
  plot(n,a)
  

Network Use

You can create a standard network that uses hardlim by calling newp.

To change a network so that a layer uses hardlim, set net.layers{i}.transferFcn to 'hardlim'.

In either case call sim to simulate the network with hardlim.

See newp for simulation examples.

Algorithm

The transfer function output is one is n is less than or equal to 0 and zero if n is less than 0.

hardlim(n) = 1, if n >= 0; 0 otherwise.

See Also

sim, hardlims

gridtop

hardlims

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