OS and Networks Commons^™

Adaptive Security-Aware Scheduling For Packet Switched Networks Using Real-Time Multi-Agent Systems, Ma'en Saleh Saleh

Dissertations

Conventional real-time scheduling algorithms are in care of timing constraints; they don’t pay any attention to enhance or optimize the real-time packet’s security performance. In this work, we propose an adaptive security-aware scheduling with congestion control mechanism for packet switching networks using real-time agentbased systems. The proposed system combines the functionality of real-time scheduling with the security service enhancement, where the real-time scheduling unit uses the differentiated-earliest-deadline-first (Diff-EDF) scheduler, while the security service enhancement scheme adopts a congestion control mechanism based on a resource estimation methodology.

The security service enhancement unit was designed based on two models: singlelayer and weighted …

Adaptive Radial Basis Function Neural Networks-Based Real Time Harmonics Estimation And Pwm Control For Active Power Filters, Eyad Kh Almaita

Dissertations

With the proliferation of nonlinear loads in the power system, harmonic pollution becomes a serious problem that affects the power quality in both transmission and distribution systems. Active power filters (APF) have been proven to be one of the most successful methods for mitigating harmonics problems. So far, different techniques have been used in harmonics extraction and control of APF to satisfy the fast response and the accuracy required by the APF. Neural networks techniques have been used successfully in different real-time and complex situations. This dissertation demonstrates four main tasks; (i) a novel adaptive radial basis function neural networks …

Efficient Reinforcement Learning In Multiple-Agent Systems And Its Application In Cognitive Radio Networks, Jing Zhang

Dissertations

The objective of reinforcement learning in multiple-agent systems is to find an efficient learning method for the agents to behave optimally. Finding Nash equilibrium has become the common learning target for the optimality. However, finding Nash equilibrium is a PPAD (Polynomial Parity Arguments on Directed graphs)-complete problem. The conventional methods can find Nash equilibrium for some special types of Markov games.

This dissertation proposes a new reinforcement learning algorithm to improve the search efficiency and effectiveness for multiple-agent systems. This algorithm is based on the definition of Nash equilibrium and utilizes the greedy and rational features of the agents. When …