| Neural Network Toolbox | ![]() |
Functions
The following functions are the utility functions that you can call to perform a lot of the work of simulating or training a network. You can read about them in their respective help comments.
These functions calculate signals.
These functions calculate derivatives, Jacobians, and values associated with Jacobians.
calcgx is used for gradient algorithms; calcjx and calcjejj can be used for calculating approximations of the Hessian for algorithms like Levenberg-Marquardt.
These functions allow network weight and bias values to be accessed and altered in terms of a single vector X.
| Utility Function Variables | Code Efficiency | ![]() |
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