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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.

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.

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.

Made available in DSpace on 2015-03-03T15:47:46Z (GMT). No. of bitstreams: 1 AndersonPC_DISSERT.pdf: 1402040 bytes, checksum: 960b15bc1392a94fb7ba8ba980e3a0b4 (MD5) Previous issue date: 2012-12-20 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Atualmente, h? diferentes defini??es de implica??es fuzzy aceitas na literatura. Do ponto de vista te?rico, esta falta de consenso demonstra que h? discord?ncias sobre o real significado de "implica??o l?gica" nos contextos Booleano e fuzzy. Do ponto de vista pr?tico, isso gera d?vidas a respeito de quais "operadores de implica??o" os engenheiros de software devem considerar para implementar um Sistema Baseado em Regras Fuzzy (SBRF). Uma escolha ruim destes operadores pode implicar em SBRF's com menor acur?cia e menos apropriados aos seus dom?nios de aplica??o. Uma forma de contornar esta situa??o e conhecer melhor os conectivos l?gicos fuzzy. Para isso se faz necess?rio saber quais propriedades tais conectivos podem satisfazer. Portanto, a m de corroborar com o significado de implica??o fuzzy e corroborar com a implementa??o de SBRF's mais apropriados, v?rias leis Booleanas t?m sido generalizadas e estudadas como equa??es ou inequa??es nas l?gicas fuzzy. Tais generaliza??es s?o chamadas de leis Boolean-like e elas n?o s?o comumente v?lidas em qualquer sem?ntica fuzzy. Neste cen?rio, esta disserta??o apresenta uma investiga??o sobre as condi??es suficientes e necess?rias nas quais tr?s leis Booleanlike ?like ? y ? I(x, y), I(x, I(y, x)) = 1 e I(x, I(y, z)) = I(I(x, y), I(x, z)) ?? se mant?m v?lidas no contexto fuzzy, considerando seis classes de implica??es fuzzy e implica??es geradas por automorfismos. Al?m disso, ainda no intuito de implementar SBRF's mais apropriados, propomos uma extens?o para os mesmos / There are more than one acceptable fuzzy implication definitions in the current literature dealing with this subject. From a theoretical point of view, this fact demonstrates a lack of consensus regarding logical implication meanings in Boolean and fuzzy contexts. From a practical point of view, this raises questions about the implication operators" that software engineers must consider to implement a Fuzzy Rule Based System (FRBS). A poor choice of these operators generates less appropriate FRBSs with respect to1 their application domain. In order to have a better understanding of logical connectives, it is necessary to know the properties that they can satisfy. Therefore, aiming to corroborate with fuzzy implication meaning and contribute to implementing more appropriate FRBSs to their domain, several Boolean laws have been generalized and studied as equations or inequations in fuzzy logics. Those generalizations are called Booleanlike laws and a lot of them do not remain valid in any fuzzy semantics. Within this context, this dissertation presents the investigation of sucient and necessary conditions under which three Boolean-like laws | y I(x; y), I(x; I(y; x)) = 1 and I(x; I(y; z)) = I(I(x; y); I(x; z)) | hold for six known classes of fuzzy implications and for implications generated by automorphisms. Moreover, an extension to FRBSs is proposed

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.

Wireless sensor networks are application-specific networks that necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. The most important challenge related to wireless sensor networks is the limited energy and computational resources of the battery powered sensor nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted and the total energy consumed by sensor nodes, but also produces scalable and fault-tolerant networks. Another important challenge associated with wireless sensor networks is that the possibility of sensory data being imperfect and imprecise is high. The requirement of precision necessitates employing expensive mechanisms such as redundancy or use of sophisticated equipments. Therefore, approximate computing may need to be used instead of precise computing to conserve energy. This thesis presents two schemes that distribute information processing for event-driven reactive applications, which are interested in higher-level information not in the raw sensory data of individual nodes, to appropriate nodes in sensor networks. Furthermore, based on these schemes, a fuzzy rule-based system is proposed that handles imprecision, inherently present in sensory data.

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.