Self-organizing Map and Cellular Automata Combined Technique for Advanced Mesh Generation in Urban and Architectural Design

This paper presents a technique for building complex and adaptive meshes for urban and architectural design. The combination of a self-organizing map and cellular automata algorithms stands as a method for generating meshes otherwise static. This intends to be an auxiliary tool for the architect or the urban planner, improving control over large amounts of spatial information. The traditional grid employed as design aid is improved to become more general and flexible.

Adaptive Control and Multi-agent Interface for Infotelecommunication Systems of New Generation

Problems for intellectualisation for man-machine interface and methods of self-organization for network control in multi-agent infotelecommunication systems have been discussed. Architecture and principles for construction of network and neural agents for telecommunication systems of new generation have been suggested. Methods for adaptive and multi-agent routing for information flows by requests of external agents- users of global telecommunication systems and computer networks have been described.

String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors

* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Study of the Self-Assessment as а Component of the Model of Self-Regulated Learning for Engineering Students

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015