Neural Network Toolbox™

Symmetric hard limit transfer function

Graph and Symbol

Syntax

• A = hardlims(N,FP)
  dA_dN = hardlims('dn',N,A,FP)
  info = hardlims(code)
  

Description

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

hardlims(N,FP) takes N and optional function parameters,

N	S x Q matrix of net input (column) vectors
FP	Struct of function parameters (ignored)

and returns A, the S x Q +1/-1 matrix with +1's where N 0.

hardlims('dn',N,A,FP) returns the S x Q derivative of A with respect to N. If A or FP is not supplied or is set to [], FP reverts to the default parameters, and A is calculated from N.

hardlims('name') returns the name of this function.

hardlims('output',FP) returns the [min max] output range.

hardlims('active',FP) returns the [min max] active input range.

hardlims('fullderiv') returns 1 or 0, depending on whether dA_dN is S x S x Q or S x Q.

hardlims('fpnames') returns the names of the function parameters.

hardlims('fpdefaults') returns the default function parameters.

Examples

Here is how to create a plot of the hardlims transfer function.

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

Assign this transfer function to layer i of a network.

• net.layers{i}.transferFcn = 'hardlims';
  

Algorithm

hardlims(n) = 1 if n 0, -1 otherwise.

See Also

sim, hardlim

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