top of page

NEURECO : AUTOMATIC PARSIMONIOUS NEURAL NETWORKS GENERATION

Flèche_Logo2.png
Get a Free Trial of NeurEco

NeurEco introduces a new generation of neural networks, based on parsimony. Using NeurEco’s parsimonious approach, the resources required for implementing artificial intelligence are reduced by several orders of magnitude.  This includes reduction in the necessary amount of learning data, computing resources, development time, and energy consumption.

NeurEco is an ANN (artificial neural network) factory. It generates parsimonious ANN models automatically, using only the learning data provided by the user.
 
Note that in the near future, a new convolution-based family of add-ons will be added to the product to handle the problems where the inputs are represented by data on regular grids (for example regression problems with images as inputs).

NeurEco can be used from the command line or via a user-friendly graphical interface and can accept the following data formats: csv, npy and MATLAB matfile.
It comes with an additional API for Python3, making it much easier to interact with standard AI environments.
It allows to export the generated models to C, ONNX or FMU.


Please find our documentation here

NEURECO TABULAR

Feuille_Blanche.png
Tabular regression NeurEco

TABULAR REGRESSION

This template of tabular solution is used to create neural network models where the outputs are values at the points of some continuous process (physical data, measured data…). NeurEco will create a regression predictive model that approximate the underlying process by a function (f) from inputs (X) to the output variable (Y).

Tabular Classification NeurEco

TABULAR CLASSIFICATION

This template of tabular solution is used to create neural network models performing supervised predictive classification. It is used when the output variable is a category and the model attempts to classify the data, that is draw conclusions from observations and predict categorical class labels.

Tabular compression NeurEco

TABULAR COMPRESSION

This template of tabular solution is used to create neural network models where the targets are the same as the inputs. These models will compress the input into a lower dimension representation and then reconstruct the output from this representation. The representation is a compact summary or compression of the input. This solution is a powerful tool to reduce dimensionality without losing the physical meaning of the inputs.

NEURECO DYNAMIC

Feuille_Blanche.png

It is an add-on of NeurEco that handles the problems where the samples in the learning dataset represent a sequence of discrete-time data.

Continuous Dynamic NeurEco

DISCRETE DYNAMIC

This add-on will create recurrent neural models with highly accurate long term-predictive capabilities.

NEURECO PFS

Feuille_Blanche.png
zrom_cont_wave_high_res3(1).png

PFS

Experience the excellence of Quotient Neural Networks, delivering peak performance. It is your go-to solution to achieve optimal design of Antennas and Resonant structures.

NeurEco: Devis
Fond_marbre_PTT.png

To see NeurEco in Action or to find out more
 

Several types of licenses are available to meet your usage needs: single-user / floating ; annual / perpetual.

Get a Free Trial of NeurEco, Arrange a Demonstration or Get More Information
bottom of page