Analysis of wormhole routings in cayley graphs of permutation groups.

Over a decade, a new class of switching technology, called wormhole routing, has been investigated in the multicomputer interconnection network field. Several classes of wormhole routing algorithms have been proposed. Most of the algorithms have been centered on the traditional binary hypercube, k-ary n-cube mesh, and torus networks. In the design of a wormhole routing algorithm, deadlock avoidance scheme is the main concern. Recently, new classes of networks called Cayley graphs of permutation groups are considered very promising alternatives. Although proposed Cayley networks have superior topological properties over the traditional network topologies, the design of the deadlock-free wormhole routing algorithm in these networks is not simple. In this dissertation, we investigate deadlock free wormhole routing algorithms in the several classes of Cayley networks, such as complete-transposition and star networks. We evaluate several classes of routing algorithms on these networks, and compare the performance of each algorithm to the simulation study. Also, the performances of these networks are compared to the traditional networks. Through extensive simulation we found that adaptive algorithm outperformed deterministic algorithm in general with more virtual channels. On the network performance comparison, the complete transposition network showed the best performance among the similar sized networks, and the binary hypercube performed better compared to the star graph

The connection machine

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Bibliography: leaves 134-157.by William Daniel Hillis.Ph.D

Applications of neural networks to control systems

Tese de dout., Engenharia Electrónica, School of Electronic Engineering Science, Univ. of Wales, Bangor, 1992This work investigates the applicability of artificial neural networks to control systems. The following properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt. Exploiting the first feature, a new method is proposed for PID autotuning. Based on integral measures of the open or closed loop step response, multilayer perceptrons (MLPs) are used to supply PID parameter values to a standard PID controller. Before being used on-line, the MLPs are trained offline, to provide PID parameter values based on integral performance criteria. Off-line simulations, where a plant with time-varying parameters and time varying transfer function is considered, show that well damped responses are obtained. The neural PID autotuner is subsequently implemented in real-time. Extensive experimentation confirms the good results obtained in the off-line simulations. To reduce the training time incurred when using the error back-propagation algorithm, three possibilities are investigated. A comparative study of higherorder methods of optimization identifies the Levenberg-Marquardt (LM)algorithm as the best method. When used for function approximation purposes, the neurons in the output layer of the MLPs have a linear activation function. Exploiting this linearity, the standard training criterion can be replaced by a new, yet equivalent, criterion. Using the LM algorithm to minimize this new criterion, together with an alternative form of Jacobian matrix, a new learning algorithm is obtained. This algorithm is subsequently parallelized. Its main blocks of computation are identified, separately parallelized, and finally connected together. The training time of MLPs is reduced by a factor greater than 70 executing the new learning algorithm on 7 Inmos transputers