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GNOSIS - Polynomial Neural Network Modeling

Many scientific, engineering, marketing, and economic phenomena are complex and difficult to model. Often, a large number of conditions that influence the input variables or an incomplete understanding of the underlying principles governing the system exacerbate the problem. The Barron Associates’ Generalized Networks for the Optimal Synthesis of Information Systems (GNOSIS) software suite provides a versatile and powerful method for modeling a variety of complex phenomena. GNOSIS can solve diversified problems such as:

• modeling systems with continuous-valued outputs (e.g., what is the temperature inside a reactor?)
• classifying data into two or more categories (e.g., will the market group, down, or stay the same?)
• predicting the future values of time-series data (e.g., what will tomorrow's temperature be?)

GNOSIS offers a number of technical advantages over both traditional statistical modeling and other neural network software tools. These include:

• building blocks that can, on their own, model complex nonlinear datainter relationships
• a network training algorithm that is orders of magnitude faster than the gradient-descent methods employed by most neural network algorithms, and
• a 'C'-language code generator that allows the user to compile and incorporate networks in custom applications on any computer

Additionally, with the optional Algorithm for Synthesis of Polynomial Networks - III (ASPN-III) structure-learning feature, GNOSIS can rapidly sort through thousands of candidate inputs and structures to find a feedforward estimation network of just-sufficient complexity that is designed to perform optimally on unseen data. The structure-learning capability can significantly reduce the time required to obtain a good model, while enhancing model robustness.