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ANN Toolbox

ANN Toolbox
(15136 downloads for this version - 54067 downloads for all versions)
A more recent valid version exists: 0.5
Ryurick M. Hristev
Allan Cornet <>
Owner Organization
Private Individual
Administrator ATOMS
Samuel Gougeon
Creation Date
November 24, 2011
Source created on
Scilab 5.4.x
Binaries available on
Scilab 5.4.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 5.5.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Install command
--> atomsInstall("ANN_Toolbox")
            This represents a toolbox for artificial neural networks,
based on "Matrix ANN" book

Current feature:
 - 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 (3)
[88.39 kB]
Source code archive

[169.30 kB]
OS-independent binary for Scilab 5.4.x
Mise à jour du fichier de Description.
[161.41 kB]
OS-independent binary for Scilab 5.5.x
Binary version (all platforms)
Automatically generated by the ATOMS compilation chain

News (0)
Comments (5)     Leave a comment 
Comment from Rajive Ganguli -- June 14, 2012, 02:25:56 AM    
I have basic questions on the NN toolbox.
Do the inputs and outputs have to be normalized [0,1] or [-1,1] before use? Are there any 
example Scilab codes for conjugate gradient? 
Answer from mike mike -- May 12, 2015, 12:26:28 PM    
> I have basic questions on the NN toolbox.
> Do the inputs and outputs have to be normalized [0,1] or [-1,1] before use? Are there
> any 
> example Scilab codes for conjugate gradient? 

It depends on activation function. In ordinary way input aren't limited (no need for 
normalization) but output is in range (0,1). I use my "line function" and then it
unlimited too. 
Comment from Е;л;е;н;а; Р;о;ж;и;н;а; -- November 18, 2013, 07:31:15 PM    
once installed run NN toolbox? ANN_ToolboxEdit();?
Answer from mike mike -- May 12, 2015, 12:28:38 PM    
> once installed run NN toolbox? ANN_ToolboxEdit();?

As far as I am able to judge it is only a few function with a lot of limitations (like 
only one type of activation function for all net) :(
Comment from Jeff Waters -- July 10, 2017, 09:58:34 PM    
are you planning a version for Scilab 6 ?? When ??



Answer from Samuel Gougeon -- December 1, 2018, 11:14:34 PM    
ANN_Toolbox 0.5 is now released for Scilab 5.5, Scilab 6.0, and Scilab 6.1.
Comment from Thomas Haregot -- July 18, 2017, 02:46:10 AM    
Dear Sirs,

First, thank you for creating this tool. It has been a great learning tool.

I have been using the following call, which provides a working hypermatrix:

W = ann_FF_Std_online(traindata,targetdata,N,W,lp,Epochs)

lp = [0.2, 0.0]
traindata is: 23x1887
targetdata is: 1x1887
N is: [23 38 38 1]
Epochs is: 2000
W is initialized by: W = ann_FF_init(N)

I am now trying to use the ann_FF_ConjugGrad with the same matrices, but I am getting the
following error.

W = ann_FF_ConjugGrad(traindata,targetdata,N,W,Epochs,1.01)

I am uncertain as to what value I should be using for the last item, dW, which I have
applied a 1.01 value.

Error produced by ConjugGrad call:
Division by zero...
at line      46 of function ann_FF_ConjugGrad called by :  
    W = ann_FF_ConjugGrad(traindata,targetdata,N,W,Epochs,1.01);

Any help you can provide will be greatly appreciated.

Comment from Vanshika Adiani -- September 18, 2018, 08:51:13 AM    
I have an input variable of 6*26 matrix
target variable of 2*26
and an unknown test sample to be predicted of 6*4 matrix

net_1_1 = ann_FFBP_lm(input,target,[6,1,2])
ann_FFBP_lm does the training for me.
ann_FFBP_run predicts the output for unknown data.
but if I wish to save the trained net, such that it will generate the same output
everytime, how can it be done.
bcoz fresh training always will generate new weights
any suggestions
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