Advanced elements for transforming networks

VLADISLAV SKORPIL Department of Telekommunications, Brno University of Technology, Purkynova 118, Brno, CZECH REPUBLIC

Synopsis New generation transforming communication networks are based on convergence of classical telecommunication networks into computer networks. This new generation network needs new advanced network elements (NE). Classical sequential data processing is constrained by the speed of central processing unit. The aim of the paper is to seek an alternative way of increasing the performance of systems by parallel data processing whose activities can be realised using the neural networks. This paper recommends NE based on active elements, which process the transmitted data units.

Introduction Neural networks are very suitable for controlling of future NE. Optimising of Communication Systems Design, using neural networks involves the research, simulation and hardware implementation of selected problems of the fast evolving converged platform of classical communication and advanced data services. Neural networks are used for advanced research solution. We have primary usable results with Kohenen neural network ant with Back/propagation learning algorithm.

II. APPROACH TO STUDY The switching-over is the basic function of the active network elements that work above physical layer of OSI model. The function of switch is to transport fastest the inputs to (output) target. The speed can be limited by blocking when data flow from two or more inputs are to be directed to one output.

Objective application of Genetic algorithm (GA) for design of Neural Network (NN) control of communication Network Element (NE) by NN seek an alternative way of increasing the performance of NE by parallel data processing

classical version of Genetic algorithm uses three genetic operators – reproduction, crossover and mutation Radial Basis Function Network (RBFN) is here used it is a type of single-direction multilayer network

Design of a General Schematic of the Genetic Algorithm Generating of initial population Ageing Mutation Calculation Sorting of upward population Crossing Finalization

III. THE BASIC MODEL OF THE NETWORK ELEMENT modern possibility, how to change classical sequential control of Network Elements NE to control using of neural networks design a simulation of NE, containing in the process of control of switching area artificial neural network with GA NE switches single data units making provision for priority

Fig. 1 Model of the switch with artificial neural network

The basic scheme of the element We think over the single-stage switching area, which has three inputs and three outputs, it is switch on the Fig.2 The switching area is realized on the cross- bar switch, i.e. in the described case the switching area with 9 switching points. We can connect arbitrary input to arbitrary output.

Fig.2 Switch

Fig.3 Switching area

Fig.4 Switching area with addressing

Fig.5 Frame structure

Fig.6 Switching area controlled by control matrix

4. Conclusion crushing majority to learn neural network for diagnostic of one object completely on 100% with GA time of learning is shorter than for classical methods the results and the learning time highly depends on GA parameters setting the best results were obtained by GA using the D- operator and not using sexual reproduction. it is shown modern possibility, how to change classical sequential control of network elements to control using of neural networks

The convergence of classical telecommunication networks and data networks is the first step in designing universal broadband integrated networks for different types of user services, including videoconference applications or multimedia services and unified network management. The integrated network must be able to guarantee different transport parameters for different services.

The problem is in the network elements, which must guarantee the required parameters and also offer a sufficiently broad bandwidth, all this at a reasonable price. In the area of the development of high- speed networks the possibilities of increasing the throughput and effectiveness of active elements are sought.