Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains xiii, 252 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 250-251).

Thesis (MTech (Electrical Engineering))--Peninsula Technikon, 2002. / Word processed copy. Summary in English. Includes bibliographical references (leaves 170-179). Also available online.

Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains ix, 110 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 90-94).

A lot of work has been done in developing energy efficient network and user devices to reduce the power consumption of nodes and devices in networks. This thesis proposes an innovative approach using fuzzy logic for power saving and extending the life time of network nodes and user devices. Using the concept of silent networking we will define an actionable silent period-this is the period during which the network node or the user device does not expect to originate, receive or relay any traffic. The decision of switching the network interface or user device in the silent mode depends on the history of the network activity. Secondly, if the actionable silent period is high enough, then we can switch the entire interface in power down mode leaving just the timer ON to wake up the interface at the end of silent period. Fuzzy logic is used in mapping the history of the network interface and based on the fuzzy rules that we define, the actionable silent period for interfaces is formulated. Experimental analysis using simulations has been done to view the power saving that can be achieved using this method. Furthermore, a methodology for extending the lifetime of the networked devices is formulated. Using this innovative approach we can save a considerable amount of energy and proportionally increase the lifetime of the networked devices.

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The past few years have witnessed a rapid growth in the use of fuzzy logic controllers for the control of processes which are complex and ill-defined. These control systems, inspired by the approximate reasoning capabilities of humans under conditions of uncertainty and imprecision, consist of linguistic 'if-then' rules which depend on fuzzy set theory for representation and evaluation using computers. Even though the fuzzy rules can be built from purely heuristic knowledge such as a human operator's control strategy, a number of difficulties face the designer of such systems. For any reasonably complex chemical process, the number of rules required to ensure adequate control in all operating regions may be extremely large. Eliciting all of these rules and ensuring their consistency and completeness can be a daunting task. An alternative to modelling the operator's response is to model the process and then to incorporate the process model into some sort of model-based control scheme. The concept of Model Based Predictive Control (MB PC) has been heralded as one of the most significant control developments in recent years. It is now widely used in the chemical and petrochemical industry and it continues to attract a considerable amount of research. Its popularity can be attributed to its many remarkable features and its open methodology. The wide range of choice of model structures, prediction horizon and optimisation criteria allows the control designer to easily tailor MBPC to his application. Features sought from such controllers include better performance, ease of tuning, greater robustness, ability to handle process constraints, dead time compensation and the ability to control nonminimum phase and open loop unstable processes. The concept of MBPC is not restricted to single-input single-output (SISO) processes. Feedforward action can be introduced easily for compensation of measurable disturbances and the use of state-space model formulation allows the approach to be generalised easily to multi-input multi-output (MIMO) systems. Although many different MBPC schemes have emerged, linear process models derived from input-output data are often used either explicitly to predict future process behaviour and/or implicitly to calculate the control action even though many chemical processes exhibit nonlinear process behaviour. It is well-recognised that the inherent nonlinearity of many chemical processes presents a challenging control problem, especially where quality and/or economic performance are important demands. In this thesis, MBPC is incorporated into a nonlinear fuzzy modelling framework. Even though a control algorithm based on a 1-step ahead predictive control strategy has initially been examined, subsequent studies focus on determining the optimal controller output using a long-range predictive control strategy. The fuzzy modelling method proposed by Takagi and Sugeno has been used throughout the thesis. This modelling method uses fuzzy inference to combine the outputs of a number of auto-regressive linear sub-models to construct an overall nonlinear process model. The method provides a more compact model (hence requiring less computations) than fuzzy modelling methods using relational arrays. It also provides an improvement in modelling accuracy and effectively overcomes the problems arising from incomplete models that characterise relational fuzzy models. Difficulties in using traditional cost function and optimisation techniques with fuzzy models have led other researchers to use numerical search techniques for determining the controller output. The emphasis in this thesis has been on computationally efficient analytically derived control algorithms. The performance of the proposed control system is examined using simulations of the liquid level in a tank, a continuous stirred tank reactor (CSTR) system, a binary distillation column and a forced circulation evaporator system. The results demonstrate the ability of the proposed system to outperform more traditional control systems. The results also show that inspite of the greatly reduced computational requirement of our proposed controller, it is possible to equal or better the performance of some of the other fuzzy model based control systems that have been proposed in the literature. It is also shown in this thesis that the proposed control algorithm can be easily extended to address the requirements of time-varying processes and processes requiring compensation for disturbance inputs and dead times. The application of the control system to multivariable processes and the ability to incorporate explicit constraints in the optimisation process are also demonstrated.

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The idea for this research work is to use the fuzzy logic as a novel technique for modelling infantry tactics and doctrine that are currently being documented in flow charts. In flow charts we can only have yes-no decisions where one query branches either to another query (if the answer is no) or branches to an action (if the answer is yes). In such methods there are no various degrees of reasoning, just crisp yes or crisp no. This is not the way that humans usually reason. Hence, the introduction of fuzzy logic gives more flexibility in modelling human decision making process. / On the other hand, the knowledge-based systems are designed to mimic the performance of a human expert by transferring his/her expertise in a specific field to a computer-based model most oftenly in the form of a software package. This knowledge is often imprecise, or not all facts are available. Still humans are capable of making good decisions within such uncertain environment. / It was decided to use fuzzy logic for modelling infantry tactics and doctrine because fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning and decision making in such incompletely defined systems. Therefore the fuzzy logic seems a suitable choice in modelling infantry tactics and doctrine. / In this research work we first show how selected infantry tactics could be modelled utilising MATLAB® Fuzzy Toolbox. Then we develop a software package in Java programming language, which is then used to model the same infantry tactics. Using Java is necessary as the final aim of this project is to implement the developed models in the intelligent agent software (Jack). / The first part of this thesis is an overview of the fuzzy logic as one of the artificial intelligence paradigms. This part also briefly introduces intelligent agents, what they are and what they are not. The second part of this thesis shows the implementation of fuzzy reasoning in modelling selected infantry tactics and doctrine. The simulation results from all applications are also presented. / Thesis MEng(ElectronicEngineering byResearch)--University of South Australia, 2055.