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 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 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 SENSITIVITY ANALYSIS
NeurEco 3.5 redefines the explainability of artificial intelligence. It allows you to easily understand which features from your dataset play the most important role.