
This is a quick testing tool for a new data modeling technique introduced by Mike Poluektov and Andrew Polar.
Users can upload their own data as semicolon separated numbers and see the accuracy of the model. The data
is parsed and processed by JavaScript code in this HTML file. The code can be seen by 'View Page Source' option
of the browser. It can be saved to file, retained and modified.
The used model is KolmogorovArnold representation for multivariate function $G$
$$ y = G(x_1, x_2, x_3, ... , x_n) = \sum_{i=0}^{2n} \Phi_i \left(\sum_{j=1}^{n} f_{i,j}(x_{j})\right), $$
where parameters $x_j$ are inputs, $y$ is a target of modeling and set of functions $f_{i,j}$ and $\Phi_i$ constitute the model.
This model is considered by authors as alternative to neural networks. Provided list of data sets with inputs and corresponding targets,
the suggested method builds all involved functions not in parametric form but by constructing of their actual shapes.
The model functions are continuous and piecewise linear, and the only parameter that user needs specify is number of points per function.
The theoretical concept is published:
 M. Poluektov, A. Polar. Modelling nonlinear control systems using the discrete Urysohn operator.
Journal of the Franklin Institute. Volume 357, Issue 6, April 2020, Pages 38653892.
 A. Polar, M. Poluektov. Patent application #20200050649. Method for Identifying Discrete Urysohn Models.
Filed on 08/11/2018.
 A. Polar, M. Poluektov. A deep machine learning algorithm for construction of
the KolmogorovArnold representation. Jun. 23, 2020. arxiv.org/abs/2001.04652
Nonlinear Adaptive Filtering
 A. Polar, M. Poluektov. Patent application 16/781,657. New form of artificial intelligence and training method thereof.
Filed on 02/04/2020.
Before uploading your data we recommend to use one of those datasets that people use for benchmarks. They
can be downloaded from provided links.
 