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Evolutionary computation of geodesic paths in CAD/CAM /Xue, Feng, January 2001 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 138-147).
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Evolutionary synthesis of time-optimal control policies /Yiu, Chun-fan. January 2002 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 381-406).
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Evolutionary design of fuzzy-logic controllers with minimal rule sets for manufacturing systems /Tong, Ching-mun. January 2002 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 379-401).
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Evolutionary design of fuzzy-logic controllers for manufacturing systems with production time-delays /Kwong, Sai-ling. January 2002 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 354-392).
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Artificial intelligence based hybrid systems for financial forecastingCastorina, Giovanni January 2001 (has links)
Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision making flows for stock market forecasting, with particular emphasis on stock selection and timing. To achieve this purpose, a number of stock selection and timing computational 'modules' are investigated. From a computational point of view, the investigation performed in this work encompass techniques such as artificial neural networks, genetic algorithms, chaos theory and fractal geometry, as well as more traditional methods such as clustering, screening, ranking, and statistics based models. From a financial data point of view, this research takes advantage of both fundamental and technical information to enhance the stock selection and timing processes and to cover several investment horizons. Three computational modules are proposed. First, a multivariate stock ranking module which uses fundamental information and is optimised through genetic algorithms. Second, a multivariate forecasting module which uses technical information and is based on artificial neural networks. Third, a univariate price time series forecasting module based on artificial neural networks. In addition, an integrated flow that takes advantage of some synergies and complementary properties of the devised modules is proposed. The effectiveness of the developed modules and the viability of the proposed integrated flow are evaluated over a number of investment horizons using (out-of-sample) historical data.
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A multi-criterion genetic algorithm for supply chain collaborationChung, Sai-ho., 鍾世豪. January 2003 (has links)
published_or_final_version / abstract / toc / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy
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Improving the performance of lifts using artificial intelligence techniquesWong, King-sau, 黃敬修 January 2003 (has links)
(Uncorrected OCR)
Abstract of thesis entitled
Improving the Performance of Lifts Using Artificial Intelligence Techniques
submitted by
Wong King Sau
for the degree of Doctor of Philosophy
at the University of Hong Kong
in August 2003
An elevator group control system manages multiple elevators to serve hall
calls in a building. Most elevator group control systems need to recognize the traffic
pattern of the building and then change their control algorithms to improve the
efficiency of the elevator system. However, the traffic flow in a building is very
difficult to be classified into distinct patterns. Traffic recognition systems can
recognize certain traffic patterns, but mixed traffic patterns are difficult to be
recognized.
The aim of this study was therefore to develop improved duplex elevator
group control systems that do not need to recognize the traffic pattern. A fuzzy logic.
control unit and genetic algorithms control unit were used. A fuzzy logic control unit
integrates with the conventional duplex elevator group control system to improve
performance especially in mixed traffic patterns with intermittent heavy traffic
demand. This system will send more than one elevator to a floor with heavy demand, . according to the overall passenger traffic conditions in the building.
The genetic algorithms control unit divides the building into three zones and assigns an appropriate number of elevators to each zone. The floors covered by each zone are adjusted every five minutes. This control unit optimizes elevator group control by equalizing the number of hall calls in each zone, the total elevator door opening time in each zone, and the number of floors served by each elevator.
Both of the control units were tested by a simulator in a computer. The performance of the elevator system is given by indices such as average waiting time, wasted man-hour, and long waiting time percentage. The new performance index "wasted man-hour" indicates the total time spent by passengers in a building waiting for the lift service. Both proposed systems perform better than the conventional duplex control system.
(An abstract of 297 words.)
~
Signed _
Wong King Sau / abstract / toc / Mechanical Engineering / Doctoral / Doctor of Philosophy
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An iterative genetic algorithm-based approach to machine assignment problemsWong, Tse-chiu., 黃資超. January 2004 (has links)
published_or_final_version / abstract / toc / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy
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Evolutionary synthesis of time-optimal control policies姚濬帆, Yiu, Chun-fan. January 2002 (has links)
published_or_final_version / abstract / toc / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy
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Learning and aggregation of Fuzzy Cognitive Maps - an evolutionary approachStach, Wojciech J Unknown Date
No description available.
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