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With the computerization of businesses, more and more data are generated and stored in databases for many business applications. Finding interesting patterns among those data may lead to useful knowledge that provides competitive advantage in business. Knowledge discovery in database has thus become an important issue to help business acquire knowledge that assists managerial and operational work. Among many types of knowledge, classification knowledge is widely used. Most classification rules learned by induction algorithms are in the crisp form. Fuzzy linguistic representation of rules, however, is much closer to the way human reasons. The objective of this research is to propose a method to mine classification knowledge from the database with fuzzy descriptions. The procedure contains five steps, starting from data preparation to rule pruning. A rule induction algorithm, RITIO, is employed to generate the classification rules. Fuzzy inference mechanism that includes fuzzy matching and output reasoning is specified to yield the output class. An experiment is conducted using several databases to show advantages of this work. The proposed method is justified with good system performance. It can be easily implemented in various business applications on classification tasks.

No description available.

This research identifies and investigates major issues in inducing accurate and comprehensible fuzzy rules from datasets. A review of the current literature on fuzzy rulebase induction uncovers two significant issues: A. There is a tradeoff between inducing accurate fuzzy rules and inducing comprehensible fuzzy rules; and, B. A common strategy for the induction of fuzzy rulebases, that of iterative rule learning where the rules are generated one by one and independently of each other, may not be an optimal one. FRANTIC, a system that provides a framework for exploring the claims above is developed. At the core lies a mechanism for creating individual fuzzy rules. This is based on a significantly modified social insect-inspired heuristic for combinatorial optimisation -- Ant Colony Optimisation. The rule discovery mechanism is utilised in two very different strategies for the induction of a complete fuzzy rulebase: 1. The first follows the common iterative rule learning approach for the induction of crisp and fuzzy rules; 2. The second has been designed during this research explicitly for the induction of a fuzzy rulebase, and generates all rules in parallel. Both strategies have been tested on a number of classification problems, including medical diagnosis and industrial plant fault detection, and compared against other crisp or fuzzy induction algorithms that use more well-established approaches. The results challenge statement A above, by presenting evidence to show that one criterion need not be met at the expense of the other. This research also uncovers the cost that is paid -- that of computational expenditure -- and makes concrete suggestions on how this may be resolved. With regards to statement B, until now little or no evidence has been put forward to support or disprove the claim. The results of this research indicate that definite advantages are offered by the second simultaneous strategy, that are not offered by the iterative one. These benefits include improved accuracy over a wide range of values for several key system parameters. However, both approaches also fare well when compared to other learning algorithms. This latter fact is due to the rule discovery mechanism itself -- the adapted Ant Colony Optimisation algorithm -- which affords several additional advantages. These include a simple mechanism within the rule construction process that enables it to cope with datasets that have an imbalanced distribution between the classes, and another for controlling the amount of fit to the training data. In addition, several system parameters have been designed to be semi-autonomous so as to avoid unnecessary user intervention, and in future work the social insect metaphor may be exploited and extended further to enable it to deal with industrial-strength data mining issues involving large volumes of data, and distributed and/or heterogeneous databases.

We develop a neuro-fuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for classification problems. Fuzzy clusters are generated incrementally from the training data set, and similar clusters are merged dynamically together through input-similarity, output-similarity, and output-variance tests. The associated membership functions are defined with statistical means and deviations. Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules can be further refined by a fuzzy neural network with a hybrid learning algorithm which combines a recursive SVD-based least squares estimator and the gradient descent method. The proposed technique has several advantages. The information about input and output data subspaces is considered simultaneously for cluster generation and merging. Membership functions match closely with and describe properly the real distribution of the training data points. Redundant clusters are combined and the sensitivity to the input order of training data is reduced. Besides, generation of the whole set of clusters from the scratch can be avoided when new training data are considered.

Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other than causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.

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.

時間數列的模式識別在近年來逐漸受到注意。因為根據時間數列所產生的走勢型態可以作為判斷事件發生與預測未來的基礎。雙線性模式是由ARMA模式所延伸，所以不易與ARMA做一區別。本文就針對這類的問題，提出解決的方法。 在本文中，我們應用統計檢定結合模糊理論，建構一個整合式的識別過程。由特徵擷取，找出各種模式之間的差異，再藉由其中的異同建立模糊規則庫。接下來計算出時間數列相對應的特徵屬性，最後由模糊規則庫做出判斷。我們以台積電與聯電的每日收盤價格與成交張數為例，識別的結果與一般的認知相同。 / Identification of time series model gets more and more attention, because we can analyze the events happened and forecast what will occur in the future based on the accurate model. Bilinear time is extended by ARMA model, so it is hard to distinguish bilinear model and ARMA model. This paper focuses on this type of subject and proposes some possible way to solve. In this paper, we combine statistical tests and fuzzy methods to build a "composite" identification process. First, we try to find out differences between each model by featuring and building the fuzzy rule bases by the differences. Then, we calculate the membership of feature according the time series data. Finally, we make our decision according to the fuzzy rule bases.

Over the past decade, fuzzy systems have gained remarkable acceptance in many fields including control and automation, pattern recognition, medical diagnosis and forecasting. The fuzzy system application has also been accepted as a promising approach to dealing with uncertainty in real estate valuation analysis. This is mainly due to the necessity of coping with a large number of qualitative and quantitative variables that affect the value of a real property. The appraisers use a great deal of judgment to identify both the characteristics that contribute to property values and the relationships among these characteristics in order to derive estimates of market values. This thesis uses the two widely-used fuzzy rule-based systems / namely the Mamdani and Takagi- Sugeno-Kang (TSK) type fuzzy models in an attempt to examine the main determinants of office rents in Istanbul Central Business District (CBD). The input variables of the fuzzy rule-based systems (FRBS) comprise: i) physical attributes of office spaces and office buildings, ii) lease contract terms, and iii) tenants&rsquo / perception of the office rent determinants, tenants&rsquo / location of residence, tenants&rsquo / transportation modes, etc and as the output the system proposes the office property&rsquo / s rental price. Obtaining office rent determinants is a significant issue for both practitioners and academics. While,practitioners use them directly in demand and sensitivity analyses, academics are more interested in the relative significance of these variables and their effect on the variation in office rent to forecast market behavior. Our data set includes a detailed survey of 500 office spaces located in Istanbul CBD. We have carried out two Mamdani-type FRBS and two TSK-type FRBS for the office space and office building data sets. In these FRBS analyses, firstly the so-called representative office spaces are determined, then the average office space rents are estimated. Finally, the spatial variation in the average office rents across the CBD sub-districts, along with the Office space rent variations with respect to different clusters, like number of workers, number of floors and so on, have been analyzed. We believe that presenting the spatial variation in office rents will make a noteworthy contribution both to the real estate investors and appraisers interested in Istanbul office market.

群體運動在現今的電玩、動畫或電影中，有十分重要的應用；透過群體性的運動，可以表現出故事背景設定的張力。在群體運動的模擬中，除了個體本身的運動行為模擬外，重要的是如何呈現出群體運動的整體效果。過去文獻中多數的群體運動模擬系統，要能在群體運動中呈現出特定形狀的效果，多需花費大量的時間反覆調整個體的模擬結果；個體本身的運動行為模型，則多採用虛擬力場的方式，被動的影響個體的運動，較缺乏直觀設定行為模型的方式。本論文的目標是建立一套人群模擬系統，此系統包括兩個部份：第一個部份是使用者可根據個人偏好設定群體運動理想中的外觀形狀，使此系統在模擬前能根據所輸入的環境資訊，利用運動計畫的方式，自動產生形體形變的路徑，以做為維持群體外形的參考目標。第二部份則是改進人群模擬時個體與群體的行為模型。我們利用模糊數學的特性，來表示行為模型以語意表達時的不確定性，使個體的行為能表現出貼近使用者所需求的結果。我們提出了三種類型的模糊行為模型與對應的原生動作，用以表現個體與群體的運動行為。根據我們實做出來的系統及實驗顯示，透過這樣的系統，我們可以利用程序化的方式為電腦動畫師產生具有特定群體外觀的群體模擬，減少其在製作相關動畫所需要的時間與技術成本，同時也提供了直觀的方式建立人群的行為模型，增加行為的豐富性。 / The effects of crowd behavior are becoming indispensable in computer games and computer animation. In crowd simulation, beside the issue of simulating the motion for individual agents, the more important one is how to simulate a specific behavior of a crowd based on the motion of individuals. In order for a crowd to conform to a specific shape, most simulation systems reported in the literature require the users to spend a great amount of time in tuning the behavior parameters of each individual, governed by virtual forces computed according to inter-agent relations. In this thesis, we aim to build a system of crowd simulation consisting two parts: a path planner for a flexible shape and a motion controller with fuzzy logic. The path planner can search for a feasible path for a region of flexible shape allowing the user to set his preference on the shape of the crowd. The local motion controller for each agent is based on fuzzy logic rules that can be used to present the uncertainty of linguistic behavior models. We have proposed three types of fuzzy behavior models and their corresponding primitive actions. Our experiments show that, with this simulation system, we allow a computer animator to use an intuitive way to create specific appearance and richer.