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ANN Toolbox

Artificial Neural Network toolbox
(29700 downloads for this version - 54460 downloads for all versions)
Ryurick M. Hristev
Allan Cornet
Owner Organization
Private Individual
Administrator ATOMS
Creation Date
December 1, 2018
Source created on
Scilab 6.0.x
Binaries available on
Scilab 5.5.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 6.0.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 6.1.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Install command
--> atomsInstall("ANN_Toolbox")
            This toolbox deals with Artificial Neural Networks. It is based on the
"Matrix ANN" book.

* User manual:
* Changelog:
* Report bugs @

 - Only layered feedforward networks are supported *directly* at the moment
   (for others use the "hooks" provided)
 - Unlimited number of layers
 - Unlimited number of neurons per each layer separately
 - User defined activation function (defaults to logistic)
 - User defined error function (defaults to SSE)
 - Algorithms implemented so far:
    * standard (vanilla) with or without bias, on-line or batch
    * momentum with or without bias, on-line or batch
    * SuperSAB with or without bias, on-line or batch
    * Conjugate gradients
    * Jacobian computation
    * Computation of result of multiplication between 'vector' and
 - Some helper functions provided

For full descriptions start with the toplevel "ANN" man page.

ann_FF — Algorithms for feedforward nets.
ann_FF_ConjugGrad — Conjugate Gradient algorithm.
ann_FF_Hess — computes Hessian by finite differences.
ann_FF_INT — internal implementation of feedforward nets.
ann_FF_Jacobian — computes Jacobian by finite differences.
ann_FF_Jacobian_BP — computes Jacobian trough backpropagation.
ann_FF_Mom_batch — batch backpropagation with momentum.
ann_FF_Mom_batch_nb — batch backpropagation with momentum (without bias).
ann_FF_Mom_online — online backpropagation with momentum.
ann_FF_Mom_online_nb — online backpropagation with momentum.
ann_FF_SSAB_batch — batch SuperSAB algorithm.
ann_FF_SSAB_batch_nb — batch SuperSAB algorithm (without bias).
ann_FF_SSAB_online — online SuperSAB training algorithm.
ann_FF_SSAB_online_nb — online backpropagation with SuperSAB
ann_FF_Std_batch — standard batch backpropagation.
ann_FF_Std_batch_nb — standard batch backpropagation (without bias).
ann_FF_Std_online — online standard backpropagation.
ann_FF_Std_online_nb — online standard backpropagation
ann_FF_VHess — multiplication between a "vector" V and Hessian
ann_FF_grad — error gradient trough finite differences.
ann_FF_grad_BP — error gradient trough backpropagation
ann_FF_grad_BP_nb — error gradient trough backpropagation (without bias)
ann_FF_grad_nb — error gradient trough finite differences
ann_FF_init — initialize the weight hypermatrix.
ann_FF_init_nb — initialize the weight hypermatrix (without bias).
ann_FF_run — run patterns trough a feedforward net.
ann_FF_run_nb — run patterns trough a feedforward net (without bias).
ann_d_log_activ — derivative of logistic activation function
ann_d_sum_of_sqr — derivative of sum-of-squares error
ann_log_activ — logistic activation function
ann_pat_shuffle — shuffles randomly patterns for an ANN
ann_sum_of_sqr — calculates sum-of-squares error
Files (6)
[98.34 kB]
Source code archive

[277.06 kB]
Miscellaneous file
User manual
[413.25 kB]
OS-independent binary for Scilab 5.5.x

[445.05 kB]
OS-independent binary for Scilab 6.0.x

[445.05 kB]
OS-independent binary for Scilab 6.1.x

[3.69 kB]
Miscellaneous file

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