Research and Development

Solutions is R&D department of PNN company that develops innovative data mining and real time signal processing methods and algorithms and is responsible for their applications in computational chemistry, neurophysiology, medicine (ECG & EEG analysis), financial market analysis and forecasting and other fields.

PNN research team includes the experts in the fields of modeling and forecasting on experimental data, decision support and pattern recognition, linear and nonlinear analyses, signal processing and numerical methods.

PNN provides both scientific solution and software implementation.

Polynomial Neural Network (PNN) Algorithms

Polynomial Neural Network (PNN) algorithm - GMDH-type neural network - one of the most promising methods developed by PNN Research team. The goal of the algorithm application is to extract knowledge from experimental data and to determine its best mathematical description. The proposed method can be used to analyze complex data sets with the aim to determine internal data relationships and to present knowledge about these relationships in the form of mathematical description (polynomial regression). As an examples of possible application area of PNN algorithm one can consider any sphere where sets of observation data should be analyzed and data relationships models should be build. There are, for example, chemistry (QSAR), economical systems analyses, stocks and financial market instruments, insurance risks study, medical diagnostics, etc.

Phase Space Technique (PST) Algorithms

Phase Space Technique (PST) is another innovative method for nonlinear analysis, signal processing and pattern recognition developed by PNN Research. This approach considers signals in phase space and accounts for two different types of noise: additive and perturbative. The first type, additive noise, contributes to distortion of the absolute values of signal peaks. The second type, perturbative noise, contributes to variations of the retention times of signal peaks and distorts the time scale. The ability to consider both types of noise significantly distinguishes it from existing methods of data analysis, which are usually designed to treat only the additive noise. Analysis of signals in phase space eliminates the problem of perturbation noise and enables detection and comparison of similar signal segments realized at different retention times. PNN Company PST algorithms implementation not only gives the unique opportunity for signal processing but also provides the efficient real time software.

Epilepsy seizure diagnostics and prediction

Early epilepsy seizure diagnostics and prediction technique based on automatic real time numerical EEG analysis. The distinctions are

Prediction time (15-20 min) that is enough to prevent the seizure by injection, to go away from the risk zone and avoid the incidents (road incidents)
Computational effective algorithms that allow implementing method as personal portable medical facility

Scientific research was performed in collaboration with

GenChemiCs, Princeton, NJ, US
University of Lausanne, Switzerland
University of Missouri-St. Louis, US
Center for Molecular Design, University of Portsmouth, UK

Software for financial forecasting was developed for

Nelson&Moritz, Hamburg, Germany

Our Phase Space Technique (PST) algorithms are used for Pharmaceutical Fingerprinting based on nonlinear modeling of chromatograms (HPLC), for the mixture of neuron spikes separation, EEG signal processing and epilepsy seizure forecasting. Our Polynomial Neural Network (PNN) algorithms are applied in drug design Quantitative Structure Relationship (QSAR) studies for forecasting of compounds activity and in financial modeling and forecasting.