| Popis: |
Due the complexity of the actual systems based on heterogeneous methods, artificial neural networks approaches can reduce this complexity by modeling the environment as stochastic. Algorithms based on Neural Networks can take into account the dynamics of these environments with no model of dynamics to be given. Main idea of the approaches developed in this chapter is to take advantage from distributed processing and task simplification by dividing an initially complex processing task into a set of simpler subtasks using complexity estimation based loop to control the splitting process. An appealing consequence of combining complexity estimation based splitting and artificial neural networks based processing techniques is decreasing of user's intervention in specifying processing parameters. A first modular structure is proposed. We have focused our attention in some special kind of Constrained Based Routing in wired networks which we called QoS self-optimization Routing. In a second part, we study the use of T-DTS selforganizing and task adaptive abilities. Beside complexity estimation based self-organization and adaptation abilities of our approach, the neural nature of generated models leads to additional attractive features which are modularity and some universality of the issued processing system, opening new dimensions in bio-inspired artificial intelligence. Moreover, the distributed nature of T-DTS makes the processing phase potentially realizable using either parallel machine or network of sequential machines. Very promising results, obtained from experimental validation, involving either the presented set of classification benchmarks (problems) or the reported pattern recognition dilemma, show efficiency of such self-organizing multiple models' generator to enhance global and local processing capabilities by reducing complexity on both processing and data levels. |
