This dissertation presents new fuzzy logic techniques for designing control systems for a wide class of complex systems. The methods are developed in detail for a crane system which contains one rigid-body and one oscillation mode. The crane problem is to transfer the rigid body a given distance such that the pendulation of the oscillation mode is regulated at the final time using a single control input. The investigations include in-depth studies of the time-optimal crane control problem as an integral part of the work. The main contributions of this study are: (1) Development of rule-based systems (both fuzzy and crisp) for the design of optimal controllers. This development involves control variable parametrization, rule derivation with parameter perturbation methods, and the design of rule based controllers, which can be combined with model-based feedback control methods. (2) A thorough investigation and analysis of the solutions for time-optimal control problems of oscillation mode systems, with particular emphasis on the use of phase-plane interpretation. (3) Development of fuzzy logic control system methodology using expert rules obtained through energy reducing considerations. In addition, dual mode control is a "spin-off" design method which, although no longer time optimal, can be viewed as a near-optimal control method which may be easier to implement. In both types of design optimization of the fuzzy logic controller can be used to improve performance. / Ph. D.

Linear programming is widely used to solve various complex problems with many variables, subject to multiple constraints. Expert systems are created to provide expertise on complex problems through the application of inference procedures and advanced expert knowledge on facts relevant to the problem. The diet problem is well-known for its contribution to the development of linear programming. Over the years many variations and facets of the diet problem have been solved by means of linear programming techniques and expert systems respectively. In this study the feasibility of combining expert system technology and linear programming techniques to solve a diet problem topical to South Africa, is examined. A computer application is created that incorporates goal programming- and multi-objective linear programming models as the inference engine of an expert system. The program is successfully applied to test cases obtained through knowledge acquisition. The system delivers an eating-plan for an individual that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of that individual, and includes the food items that result in the lowest total cost. It further allows prioritization of the food preference and least cost factors through the use of weights. Based on the results, recommendations and contributions to the linear programming and expert system fields are presented. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

Linear programming is widely used to solve various complex problems with many variables, subject to multiple constraints. Expert systems are created to provide expertise on complex problems through the application of inference procedures and advanced expert knowledge on facts relevant to the problem. The diet problem is well-known for its contribution to the development of linear programming. Over the years many variations and facets of the diet problem have been solved by means of linear programming techniques and expert systems respectively. In this study the feasibility of combining expert system technology and linear programming techniques to solve a diet problem topical to South Africa, is examined. A computer application is created that incorporates goal programming- and multi-objective linear programming models as the inference engine of an expert system. The program is successfully applied to test cases obtained through knowledge acquisition. The system delivers an eating-plan for an individual that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of that individual, and includes the food items that result in the lowest total cost. It further allows prioritization of the food preference and least cost factors through the use of weights. Based on the results, recommendations and contributions to the linear programming and expert system fields are presented. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

A clinical trial is defined as a study conducted on a group of patients to determine the effect of a treatment. Assignment of patients to clinical trials is a data and labor intensive task. Usually, medical personnel manually check the eligibility of a patient for a clinical trial based on the patient's medical history and current medical condition. According to studies, most clinical trials are under-enrolled which negatively affects their effectiveness. We have developed web-based agents that can test the eligibility of patients for many clinical trials at once. We have tested various heuristics for optimizing cost and data entry needed in assigning patients to clinical trials. Testing eligibility of a patient for many clinical trials is only feasible if it is cost and data entry efficient. Agents with different heuristics were then tested on data from current breast cancer patients at the Moffitt Cancer Center. Results with different heuristics are compared with each other and with that of the clinicians. It is shown that cost savings are possible in clinical trial assignment. Also, less data entry is needed when probabilistic agents are used to reorder questions.

The purpose of this work is to develop a web based decision support system, based onfuzzy logic, to assess the motor state of Parkinson patients on their performance in onscreenmotor tests in a test battery on a hand computer. A set of well defined rules, basedon an expert’s knowledge, were made to diagnose the current state of the patient. At theend of a period, an overall score is calculated which represents the overall state of thepatient during the period. Acceptability of the rules is based on the absolute differencebetween patient’s own assessment of his condition and the diagnosed state. Anyinconsistency can be tracked by highlighted as an alert in the system. Graphicalpresentation of data aims at enhanced analysis of patient’s state and performancemonitoring by the clinic staff. In general, the system is beneficial for the clinic staff,patients, project managers and researchers.

Title from PDF of title page. / Document formatted into pages; contains 57 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: A clinical trial is defined as a study conducted on a group of patients to determine the effect of a treatment. Assignment of patients to clinical trials is a data and labor intensive task. Usually, medical personnel manually check the eligibility of a patient for a clinical trial based on the patient's medical history and current medical condition. According to studies, most clinical trials are under-enrolled which negatively affects their effectiveness. We have developed web-based agents that can test the eligibility of patients for many clinical trials at once. We have tested various heuristics for optimizing cost and data entry needed in assigning patients to clinical trials. Testing eligibility of a patient for many clinical trials is only feasible if it is cost and data entry efficient. Agents with different heuristics were then tested on data from current breast cancer patients at the Moffitt Cancer Center. / ABSTRACT: Results with different heuristics are compared with each other and with that of the clinicians. It is shown that cost savings are possible in clinical trial assignment. Also, less data entry is needed when probabilistic agents are used to reorder questions. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

No / In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.

This research project aims to develop a framework to manage uncertainty in cloud manufacturing for small and medium enterprises (SMEs). The framework includes a cloud manufacturing taxonomy; guidance to deal with uncertainty in cloud manufacturing, by providing a process to identify uncertainties; a detailed step-by-step approach to managing the uncertainties; a list of uncertainties; and response strategies to security and privacy uncertainties in cloud manufacturing. Additionally, an online assessment tool has been developed to implement the uncertainty management framework into a real life context. To fulfil the aim and objectives of the research, a comprehensive literature review was performed in order to understand the research aspects. Next, an uncertainty management technique was applied to identify, assess, and control uncertainties in cloud manufacturing. Two well-known approaches were used in the evaluation of the uncertainties in this research: Simple Multi-Attribute Rating Technique (SMART) to prioritise uncertainties; and a fuzzy rule-based system to quantify security and privacy uncertainties. Finally, the framework was embedded into an online assessment tool and validated through expert opinion and case studies. Results from this research are useful for both academia and industry in understanding aspects of cloud manufacturing. The main contribution is a framework that offers new insights for decisions makers on how to deal with uncertainty at adoption and implementation stages of cloud manufacturing. The research also introduced a novel cloud manufacturing taxonomy, a list of uncertainty factors, an assessment process to prioritise uncertainties and quantify security and privacy related uncertainties, and a knowledge base for providing recommendations and solutions.

In the world of educational satellites, student teams manually conduct operations daily, sending commands and collecting downlinked data. Educational satellites typically travel in a Low Earth Orbit allowing line of sight communication for approximately thirty minutes each day. This is manageable for student teams as the required manpower is minimal. The international Global Educational Network for Satellite Operations (GENSO), however, promises satellite contact upwards of sixteen hours per day by connecting earth stations all over the world through the Internet. This dramatic increase in satellite communication time is unreasonable for student teams to conduct manual operations and alternatives must be explored. This thesis first introduces a framework for developing different Artificial Intelligences to conduct autonomous satellite operations for CubeSat satellites. Three different implementations are then compared using Cal Poly's CP6 CubeSat and the University of Tokyo's XI-IV CubeSat to determine which method is most effective.

Spatial Decision Making as a branch of decision making science deals with geographically related data in order to achieve complex spatial decision problems. Fuzzy set theory is one of the methods that can be used to come up with these types of problems. On the other hand, Geographical Information Systems (GIS) is one of the most powerful tools that we can use to accomplish spatial decision problems. Selection of the suitable site or land-use for the real estate is also a spatial decision making problem. When we consider the initial dynamics of the suitably located property from the point of view of value and potential we observe that the &ldquo / good location&rdquo / is the dominating factor. This study reports on the development of a kind of decision support system for locational suitability of settlements that integrates the fuzzy set (FZ) theory, a rule-based system (RBS) and GIS. This study is thought as the assistant for the property managers that are buyers and sellers. It can function as the property consultant for the buyers when they are looking for a property to buy and also it helps the real estate agencies to sell their properties. On the other hand, different scenarios of the potential areas according to the different user&rsquo / s preferences are depicted and they are joined and compared with the results of the vulnerability to earthquake hazards&rsquo / of the same area. Odunpazari - Eskisehir area is selected for implementation of the case study because of the data availability. As a result of this study, it can be said that most suitable property changes depending on the people&rsquo / s preferences. In addition, it is seen that most of the buildings that are locationally suitable are highly vulnerable to the earthquake hazards.