Company / Technology


Neural Network Technology

Alyuda's innovative patent-pending algorithms and acknowledged expertise have earned the company the reputation of a trusted provider of software solutions that use the artificial intelligence technology. The company's products are used globally in financial applications, budgeting and planning, human resource management, process control, customer retention and scientific applications.

The tools and solutions that comprise the Alyuda family of products are based on the company´s proprietary algorithms of automatic neural network architecture selection and data pre-processing. These are the core algorithms that can be applied in a wide range of neural network-based solutions, from easy-to-use forecasting tools to corporate data analysis systems.

These new methods and algorithms are combined with the latest advances in artificial neural networks and genetic algorithms, carefully selected and tested by the Alyuda experts on a comprehensive set of real-world applications.

Why Neural Networks?

Neural networks are now globally recognized as the most effective and appropriate artificial intelligence technology for pattern recognition. Superior results in pattern recognition can be directly applied for business purposes in forecasting, classification and data analysis. This new approach gives an extra advantage in solving "real-world" problems in business and engineering.

However, to bring proper results, neural networks require correct data preprocessing, architecture selection and network training. In the past, most failures in using neural networks were attributable to the users´ poor skills in the appropriate preparation of data and neural network design.

It is exactly these two issues, preventing many businesses from using the tremendous potential of this promising technology. Alyuda´s efforts are focused on. Owing to the automatic data preprocessing techniques and architecture selection algorithms, skillfully implemented in the company´s easy-to-use tools, neural networks have become easy enough for any manager to use - without any prior expensive training.

What Are Neural Networks?

Neural networks are data analysis methods and algorithms, indirectly based on the nervous systems of humans and animals.

In general terms, an artificial neural network consists of a large number of simple processing units, linked by weighted connections. By analogy, the processing units can be referred to as neurons. Each unit receives inputs from many other units and generates a single output. The output acts as an input to other processing units.

The power of a neural network stems from the combination of many units in a network. A certain network can be tuned to solve a specific problem by varying the connection topology and values of the connecting weights between the units.

An artificial neural network is nonlinear in nature and, thus, represents an exceptionally powerful method of analyzing real-world data that allows modeling extremely difficult dependencies.

Neural nets have proven to be among the best methods of detecting hidden relations in a dataset (for example, in.stock market or sales data). After a neural network has analyzed your dataset (this process is called network training), it can make predictions and perform pattern recognition and categorization based on the found hidden dependencies.