Sets out to provide an understanding of the theory of fuzzy logic by supplying background details concerning its evolution in mathematics and computer science. Once a basic understanding of the theory is obtained, then it is easier to understand the implications for computer applications. Fuzzy logic processors and compilers have facilitated the development of expert systems that typically use a lot of imprecise data. These expert systems have been used successfully as control units in industrial settings and as decision support systems in hospital settings. Fuzzy logic has been found to be a practical and viable form of artificial intelligence that mitigates the current drawbacks of other forms of artificial intelligence. But the really exciting development that is poised to emerge is the introduction of fuzzy logic appliances. These appliances employ an expert system on a chip that is able to mimic the range of flexibility of the human mind, while utilizing resources more efficiently.
Examines the growth of a new technology called fuzzy logic and its significance for microcontroller‐based embedded control solutions. Outlines the reasons for the emergence of fuzzy logic and explains the mathematic principles behind fuzzy set theory. Using the example of an oven temperature control system, describes how fuzzy logic is applied to the practical solution of a control problem rather than a conventional solution. Concludes that fuzzy logic has been used primarily in embedded control application as a software‐based methodology in closed‐loop control systems whilst a dedicated fuzzy hardware processor would optimally be based on a parallel architecture, allowing the entire rule base to be evaluated in a parallel fashion.
Manufacturing is a key to continuous economic growth. Fuzzy expert systems, fuzzy logics, fuzzy languages, fuzzy neural networks, and intelligent control are proposed as additional tools in manufacturing. Fuzzy logic is a new way to program computers and appliances to mimic the imprecise way humans make decisions. Fuzzy logic has been applied to cameras, subways, computers and air conditioners. Through the use of fuzzy logic, fuzzy expert systems can be built which add a new dimension in the technologies for intelligent factories.
Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems…
Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems (DSSs) within the organization is critical. Considering the imprecise or fuzzy nature of the data in real‐world problems, it becomes obvious that the ability to manage uncertainty turns out to be a crucial issue for a DSS. In this framework, this paper discusses the key role of fuzzy logic (FL) in the DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research.
Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of…
Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.
The purpose of this paper is to explore the application of fuzzy logic in real estate investment in Hong Kong. There have been sufficient debates on the literature…
The purpose of this paper is to explore the application of fuzzy logic in real estate investment in Hong Kong. There have been sufficient debates on the literature, providing the theoretical background on real estate investment decisions but there has been a lack of empirical support in this regard. This paper attempts to fill the gap between theorem and application.
The fuzzy logic system is adopted to evaluate the situation of a real estate market with imprecise and vague information. An indicator‐portfolio, rather than a specific indicator/index usually employed by practitioners, is explored to assist investors in risk management. The result derived from this framework is then compared to the property price index. This approach provides a framework in understanding the market without statistical and mathematical models. It tries to stimulate the complex human cognitive process involving decision making.
The housing‐indicator portfolio composition produces an outcome value which is able to reflect the complexities of both the real estate market and investors' expectations. An increase of this value implies that the investment condition is becoming more positive.
The paper reveals that fuzzy logic can provide some insights in an intuitive manner and is capable of obtaining information not found in market data. It is particularly useful to investors without experience in mathematical modeling.
This paper establishes a basic framework of fuzzy logic for real estate investment on which a base is formed as a reference for practitioners and investors. However, they should make references to the specific housing‐indicator portfolio composition in their own regions.
This paper has used a fuzzy logic system to assist practitioners as well as investors on decision making in real estate investment with imperfect market information. With the aid of the system, practitioners and investors are able to enhance their investment decision‐making quality by reducing the risk incurred by such uncertainties.
The set of fuzzy threshold functions is defined to be a fuzzy set over the set of functions. All threshold functions have full memberships in this fuzzy set. Defines and investigates a distance measure between a non‐linearly separable function and the set of all threshold functions. Defines an explicit expression for the membership function of a fuzzy threshold function through the use of this distance measure and finds three upper bounds for this measure. Presents a general method to compute the distance, an algorithm to generate the representation automatically, and a procedure to determine the proper weights and thresholds automatically. Presents the relationships among threshold gate networks, artificial neural networks and fuzzy neural networks. The results may have useful applications in logic design, pattern recognition, fuzzy logic, multi‐objective fuzzy optimization and related areas.
Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of…
Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies.
In this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other using t-tests and kernel density plots.
The results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy.
This paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.
We introduce a four‐valued logic that includes, in addition to true and false, the values unknown and non‐existent. We introduce the idea of presupposition in fuzzy logic and then use this to relate this four valued logic to the binary logic.