A neural network based technique is presented, which is able to successfully extract polynomial classification rules from labeled electroencephalogram (EEG) signals. To represent the classification rules in an analytical form, we use the polynomial neural networks trained by a modified Group Method of Data Handling (GMDH). The classification rules were extracted from clinical EEG data that were...

Intangible resources are the source of competi ti ve advantage in the knowledge economy. Knowledge and intellectual capital (IC) within companies have gathered worldwide att enti on, theories and methods for knowledge creati on management have been developed for organizati ons and alliances. Knowledge has currently become a main part of manufacturing resources and a prerequisite for success in ...

Traffic flow forecasting, the core element of intelligent transportation system, plays an important role in traffic information services and traffic guidance. Since neural network prediction needs plenty of training samples, it cannot guarantee the real-timeness of traffic flow forecasting. In this paper, a GMDH network was constructed by self-organization, and the network was applied to traffi...

This paper introduces Social Programming for use in predicting closing stock prices. Social Programming is a new methodology for creating Complex Adaptive Functional Networks that is based on a social-psychological metaphor. Social Programming is demonstrated to be a logical extension of the Particle Swarm methodology, the Group Method of Data Handling and Cartesian Programming. The Social Prog...

Authors have developed a Group Method of Data Handling (GMDH) type algorithm for designing multilayered neural networks. The algorithm is general enough that it will accept any number of inputs and any sized training set. Each neuron of the resulting network is a function of two of the inputs to the layer. The equation for each of the neurons is a quadratic polynomial. Several forms of the equa...