Abstract: In this paper two models using a functional network
were employed to solving classification problem. Functional networks
are generalized neural networks, which permit the specification of
their initial topology using knowledge about the problem at hand. In
this case, and after analyzing the available data and their relations, we
systematically discuss a numerical analysis method used for
functional network, and apply two functional network models to
solving XOR problem. The XOR problem that cannot be solved with
two-layered neural network can be solved by two-layered functional
network, which reveals a potent computational power of functional
networks, and the performance of the proposed model was validated
using classification problems.
Abstract: Scheduling algorithm is a key technology in satellite
switching system with input-buffer. In this paper, a new scheduling
algorithm and its realization are proposed. Based on Crossbar
switching fabric, the algorithm adopts serial scheduling strategy and
adjusts the output port arbitrating strategy for the better equity of every
port. Consequently, it increases the matching probability. The
algorithm can greatly reduce the scheduling delay and cell loss rate.
The analysis and simulation results by OPNET show that the proposed
algorithm has the better performance than others in average delay and
cell loss rate, and has the equivalent complexity. On the basis of these
results, the hardware realization and simulation based on FPGA are
completed, which validate the feasibility of the new scheduling
algorithm.