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