Neuro-FLExSys is a unique software package which allows users to implement self-learning expert systems. Trained on historical data, Neuro-FLExSys becomes a very effective Data Mining tool capable of discovering data patterns and detecting out-of-pattern transactions.

Fuzzy Logic expert systems can be effectively used in every area of business where prioritizing and resource scheduling are important. They are successfully utilized in manufacturing process control and robotics. Neural networks are good in pattern matching, function approximation, optimization, classification, clustering.

Neural networks and fuzzy logic control systems are both numerical model-free estimators and dynamic systems. They share the common ability to deal with difficulties arising from uncertainty, imprecision, and noise in the natural environment.
NeuroFLExSys is a neuro-fuzzy hybrid system. It is a fuzzy logic expert system implemented as a feedforward multi-layer neural network. Neural network allows for the system learning and noise tolerance. Fuzzy logic provides for the neural network's manageable structure and eliminates opacity of its internal layers. In addition, NeuroFLExSys allows to introduce high level thinking into neural networks by manually presetting some expert rules.

NeuroFLExSys is a multiple-input-multiple-output (MIMO) system. It is trained on the sets of input/output historical data.
The system training is performed in several steps.
Input and output variables universes of discourse are split into sets. Input and output variable sets are trained by unsupervised learning: Kohonen Feature Maps.
Rules are trained by competitive learning. Week rule elimination is performed based on the rule weights calculated during the competitive learning procedure.
Special rule combination procedure reduces rule redundancy.
The entire system is then fine-tuned by supervised learning in the form of the Back Propagation algorithm minimizing quadratic error function.

The system is programmed in VB6 and its parameters are stored in SQL Server database. Database structure allows to analyze derived rules as data clusters.