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Fuzzy Logic (fuzzy + logic)
Terms modified by Fuzzy Logic Selected AbstractsAdaptive critic design using non-linear network structuresINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2003Ognjen Kuljaca Abstract A neural net (NN)/fuzzy logic (FL) adaptive critic controller is described. This structure takes advantage of the decision-making properties of a FL system to critique and tune a NN action-generating network. The stability of the proposed structure is proven. NN and fuzzy weight tuning algorithms are given that do not require complicated initialization procedures or any off-line learning phase. Tracking and bounded NN weights and control signals are guaranteed. The adaptive fuzzy critic controller given here is a model-free controller' in the sense that it works for any system in a prescribed class without the need for extensive modeling and preliminary analysis to find a regression matrix'. There is no linearity in the parameter (LIP) requirement. Copyright © 2003 John Wiley & Sons, Ltd. [source] Knowledge-based Diagnosis Aiding in Regulation ThermographyPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2003Hagen Knaf Dr. Regulation Thermography is a diagnostic tool in the medical science based on the measurement of the body's thermoregulation ability , the so-called thermogram. The expert's rules for the interpretation of a thermogram can be modelled using Fuzzy Logic. In the present article this modelling process is briefly explained; it leads to a Fuzzy Inference System capable of evaluating thermograms with respect to e.g. signals for the presence of Breast Cancer. Some of the main points of a comparison between the expert rules and the result of a stepwise linear discriminant analysis performed on classified thermograms are presented. [source] Architectural Methodology Based on Intentional Configuration of BehaviorsCOMPUTATIONAL INTELLIGENCE, Issue 1 2001François Michaud Intelligence has been an object of study for a long time. Different architectures try to capture and reproduce these aspects into artificial systems (or agents), but there is still no agreement on how to integrate them into a general framework. With this objective in mind, we propose an architectural methodology based on the idea of intentional configuration of behaviors. Behavior-producing modules are used as basic control components that are selected and modified dynamically according to the intentions of the agent. These intentions are influenced by the situation perceived, knowledge about the world, and internal variables that monitor the state of the agent. The architectural methodology preserves the emergence of functionality associated with the behavior-based paradigm in the more abstract levels involved in configuring the behaviors. Validation of this architecture is done using a simulated world for mobile robots, in which the agent must deal with various goals such as managing its energy and its well-being, finding targets, and acquiring knowledge about its environment. Fuzzy logic, a topologic map learning algorithm, and activation variables with a propagation mechanism are used to implement the architecture for this agent. [source] Fuzzy Monte Carlo Simulation and Risk Assessment in ConstructionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2010N. Sadeghi However, subjective and linguistically expressed information results in added non-probabilistic uncertainty in construction management. Fuzzy logic has been used successfully for representing such uncertainties in construction projects. In practice, an approach that can handle both random and fuzzy uncertainties in a risk assessment model is necessary. This article discusses the deficiencies of the available methods and proposes a Fuzzy Monte Carlo Simulation (FMCS) framework for risk analysis of construction projects. In this framework, we construct a fuzzy cumulative distribution function as a novel way to represent uncertainty. To verify the feasibility of the FMCS framework and demonstrate its main features, the authors have developed a special purpose simulation template for cost range estimating. This template is employed to estimate the cost of a highway overpass project. [source] Some applications of fuzzy logic in rule,based expert systemsEXPERT SYSTEMS, Issue 4 2002Trung T. Pham Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision,making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems. [source] Application of fuzzy logic to forecast seasonal runoffHYDROLOGICAL PROCESSES, Issue 18 2003C. Mahabir Abstract Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeastern Alberta, are two basins that require this type of late winter forecast of potential spring runoff. Historically, the forecast has been based upon a combination of regression equations. These results are then interpreted by a forecaster and are modified based on the forecaster's heuristic knowledge of the basin. Unfortunately, this approach has had limited success in the past, in terms of the accuracy of these forecasts, and consequently an alternative methodology is needed. In this study, the applicability of fuzzy logic modelling techniques for forecasting water supply was investigated. Fuzzy logic has been applied successfully in several fields where the relationship between cause and effect (variable and results) are vague. Fuzzy variables were used to organize knowledge that is expressed ,linguistically' into a formal analysis. For example, ,high snowpack', ,average snowpack' and ,low snowpack' became variables. By applying fuzzy logic, a water supply forecast was created that classified potential runoff into three forecast zones: ,low', ,average' and ,high'. Spring runoff forecasts from the fuzzy expert systems were found to be considerably more reliable than the regression models in forecasting the appropriate runoff zone, especially in terms of identifying low or average runoff years. Based on the modelling results in these two basins, it is concluded that fuzzy logic has a promising potential for providing reliable water supply forecasts. Copyright © 2003 John Wiley & Sons, Ltd. [source] Fuzzy logic-based networks: A study in logic data interpretationINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2006Xiaofeng Liang Fuzzy neurons may have outstanding learning abilities and are endowed with significant interpretation capabilities. In this study, we are concerned with the development of logic networks composed of fuzzy neurons. The main phase of the design includes the granulation of the output space (via triangular fuzzy sets) being realized with the use of fuzzy equalization. In the sequel these fuzzy sets are used to guide the construction of a family of fuzzy sets in the input space. Further processing of the resulting fuzzy sets deals with some additional aggregation of those that are not sufficiently distinct. This helps reduce the size of the logic network. We include comprehensive experimentation and offer a thorough interpretation of the networks. Experiments concerning real-world continuous data help evaluate the network's appealing properties: transparent interpretability and practical feasibility. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1249,1267, 2006. [source] ,-Cut fuzzy control charts for linguistic dataINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2004Murat Gülbay The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations that are ambiguous or not well defined. In the literature, there exist some fuzzy control charts developed for linguistic data that are mainly based on membership and probabilistic approaches. In this article, ,-cut control charts for attributes are developed. This approach provides the ability of determining the tightness of the inspection by selecting a suitable ,-level: The higher , the tighter inspection. The article also presents a numerical example and interprets and compares other results with the approaches developed previously. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1173,1195, 2004. [source] A FUZZY LOGIC APPROACH TO ESTIMATING HYDRAULIC FLOW UNITS FROM WELL LOG DATA: A CASE STUDY FROM THE AHWAZ OILFIELD, SOUTH IRANJOURNAL OF PETROLEUM GEOLOGY, Issue 1 2009A. Kadkhodaie-Ilkhchi Porosity-permeability relationships in the framework of hydraulic flow units can be used to characterize heterogeneous reservoir rocks. Porosity is a volumetric parameter whereas permeability is a measure of a rock's flow properties and depends on pore distribution and connectivity. Thus zonation of a reservoir using flow zone indicators and the identification of flow units can be used to evaluate reservoir quality based on porosity-permeability relationships. In the present study, we attempt to make a quantitative correlation between flow units and well log responses using fuzzy logic in the mixed carbonate-clastic Asmari Formation at the Ahwaz oilfield, South Iran. A hybrid neuro-fuzzy approach was used to verify the results of fuzzy modelling. For this purpose, well log and core data from three wells at Ahwaz were used to make an intelligent formulation between core-derived flow units and well log responses. Data from a separate well was used for evaluation and validation of the results. The results of this study demonstrate that there is a good agreement between core-derived and fuzzy-logic derived flow units. Fuzzy logic was successful in modelling flow units from well logs at well locations for which no core data was available. [source] Fuzzy logic and the semantic WebJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 14 2007Xiaomin Zhu [source] Interaction-Dependent Semantics for Illustrative Volume RenderingCOMPUTER GRAPHICS FORUM, Issue 3 2008Peter Rautek In traditional illustration the choice of appropriate styles and rendering techniques is guided by the intention of the artist. For illustrative volume visualizations it is difficult to specify the mapping between the 3D data and the visual representation that preserves the intention of the user. The semantic layers concept establishes this mapping with a linguistic formulation of rules that directly map data features to rendering styles. With semantic layers fuzzy logic is used to evaluate the user defined illustration rules in a preprocessing step. In this paper we introduce interaction-dependent rules that are evaluated for each frame and are therefore computationally more expensive. Enabling interaction-dependent rules, however, allows the use of a new class of semantics, resulting in more expressive interactive illustrations. We show that the evaluation of the fuzzy logic can be done on the graphics hardware enabling the efficient use of interaction-dependent semantics. Further we introduce the flat rendering mode and discuss how different rendering parameters are influenced by the rule base. Our approach provides high quality illustrative volume renderings at interactive frame rates, guided by the specification of illustration rules. [source] Enhancing Neural Network Traffic Incident-Detection Algorithms Using WaveletsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2001A. Samant Researchers have presented freeway traffic incident-detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet-based feature-extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature-extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident-detection rate, reduces the false-alarm rate and the incident-detection time, and improves the convergence of the neural network training algorithm substantially. [source] Applying fuzzy logic and genetic algorithms to enhance the efficacy of the PID controller in buffer overflow elimination for better channel response timeliness over the InternetCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 7 2006Wilfred W. K. Lin Abstract In this paper two novel intelligent buffer overflow controllers: the fuzzy logic controller (FLC) and the genetic algorithm controller (GAC) are proposed. In the FLC the extant algorithmic PID controller (PIDC) model, which combines the proportional (P), derivative (D) and integral (I) control elements, is augmented with fuzzy logic for higher control precision. The fuzzy logic divides the PIDC control domain into finer control regions. Every region is then defined either by a fuzzy rule or a ,don't care' state. The GAC combines the PIDC model with the genetic algorithm, which manipulates the parametric values of the PIDC as genes in a chromosome. The FLC and GAC operations are based on the objective function . The principle is that the controller should adaptively maintain the safety margin around the chosen reference point (represent by the ,0' of ) at runtime. The preliminary experimental results for the FLC and GAC prototypes indicate that they are both more effective and precise than the PIDC. After repeated timing analyses with the Intel's VTune Performer Analyzer, it was confirmed that the FLC can better support real-time computing than the GAC because of its shorter execution time and faster convergence without any buffer overflow. Copyright © 2005 John Wiley & Sons, Ltd. [source] Considering safety issues in minimum losses reconfiguration for MV distribution networksEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 5 2009Angelo Campoccia Abstract This paper offers a new perspective over the traditional problem of the multiobjective optimal reconfiguration of electrical distribution systems in regular working state. The issue is indeed here formulated including also safety issues. Indeed, dimensioning the earth electrodes of their own secondary substations, distribution companies take into account the probable future configurations of the network due to transformations of overhead lines into cable lines or realization of new lines. On the contrary, they do not consider that, during normal working conditions, the structure of the network can be modified for long periods as a consequence of reconfiguration manoeuvres, with differences between the design current of the earthing systems and the fault current in certain substations significant. As a consequence, often distribution companies limit the implementation of the optimal reconfiguration layouts because they are unable to suitably evaluate the safety issue. In the paper, the problem is formulated including a further objective in order to account for the safety. A suitable constrained multiobjective formulation of the reconfiguration problem is therefore used aiming at: the minimal power losses operation, the verification of safety at distribution substations, the load balancing among the HV/MV transformers while keeping the voltage profile regular. The application carried out uses an NSGA-II algorithm whose performance is compared to that of a fuzzy logic-based multiobjective evolutionary algorithm. In the considered automated network, the remote control of tie-switches is possible and their layout is the optimization variable. After a brief description of the optimal reconfiguration problem for automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, test results on a large MV distribution network are reported and discussed. Copyright © 2008 John Wiley & Sons, Ltd. [source] Fuzzy-based multiuser detector for impulsive CDMA channelEUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, Issue 7 2007Adel M. Hmidat A new fuzzy multiuser detector for non-Gaussian synchronous direct sequence code division multiple access (DS-CDMA) is proposed for jointly mitigating the effects of impulsive noise and multiple access interference (MAI). The proposed scheme combines a linear decorrelator and antenna array with a nonlinear preprocessor based on fuzzy logic and rank ordering. The fuzzy rule is incorporated to combat impulsive noise by eliminating outliers from the received signal. The performance of the proposed scheme is assessed by Monte Carlo simulations and the obtained results demonstrate that the proposed fuzzy detector outperforms other reported schemes in terms of bit error rate (BER) and channel capacity. Copyright © 2007 John Wiley & Sons, Ltd. [source] Hybrid expert,fuzzy approach for evaluation of complex systemsEXPERT SYSTEMS, Issue 3 2009Veysi Öztürk Abstract: Evaluation of complex systems is generally complicated and time consuming. Evaluation is needed for nearly all engineering tasks and the obstacles related to evaluation increase in proportion to complexity. New techniques can be used to automate manual evaluation and to overcome the obstacles related to evaluation that cannot be solved (or can only be solved with great difficulty) with conventional computing. In this study, a methodology was developed to handle the heuristic knowledge of experts for evaluation purposes. In this method, knowledge was represented as a reference model of evaluation objectives, production rules, measures, methods and parameters. A ,common evaluation process' and ,common evaluation model', which simplify and speed up the evaluation process and decrease evaluation cost, were proposed and developed. A hybrid expert,fuzzy system, called ,intelligent evaluation system' (INES), which can be used for evaluation of complex systems was developed. To define a process and develop a system that simplifies and speeds up evaluation can save time, decrease cost and provide reusability. As the evaluation of complex systems includes uncertainty in some aspects, fuzzy logic was incorporated with an expert system for reasoning. INES was implemented successfully for the evaluation of an air defence system, which is a complex system used to protect a region from all air threats. [source] Developments of fuzzy PID controllersEXPERT SYSTEMS, Issue 5 2005H. B. Kazemian Abstract: This paper describes the development and tuning methods for a novel self-organizing fuzzy proportional integral derivative (PID) controller. Before applying fuzzy logic, the PID gains are tuned using a conventional tuning method. At supervisory level, fuzzy logic readjusts the PID gains online. In the first tuning method, fuzzy logic at the supervisory level readjusts the three PID gains during the system operation. In the second tuning method, fuzzy logic only readjusts the proportional PID gain, and the corresponding integral and derivative gains are readjusted using the Ziegler,Nichols tuning method while the system is in operation. For the compositional rule of inferences in the fuzzy PID and the self-organizing fuzzy PID schemes two new approaches are introduced: the min implication function with the mean of maxima defuzzification method, and the max-product implication function with the centre of gravity defuzzification method. The fuzzy PID controller, the self-organizing fuzzy PID controller and the PID controller are all applied to a non-linear revolute-joint robot arm for step input and path tracking experiments using computer simulation. For the step input and path tracking experiments, the novel self-organizing fuzzy PID controller produces a better output response than the fuzzy PID controller; and in turn both controllers exhibit better process output than the PID controller. [source] Some applications of fuzzy logic in rule,based expert systemsEXPERT SYSTEMS, Issue 4 2002Trung T. Pham Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision,making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems. [source] A fuzzy analytic hierarchy process approach in modular product designEXPERT SYSTEMS, Issue 1 2001W.B Lee Product development stages. The analytic hierarchy process (AHP), which breaks down a complex problem into simple hierarchical decision-making processes, can be incorporated with fuzzy logic to suggest the relative strength of the factors in the corresponding criteria, thereby enabling the construction of a fuzzy judgement matrix to facilitate decision-making. This paper proposes a fuzzy AHP approach in modular product design complemented with a case example to validate its feasibility in a real company. Test findings indicate that the approach is helpful for providing key decision support information in terms of product module selection during product development stages. The significance of the contribution of this paper is the suggestion of a novel approach in modular product design, embracing a combination of computational intelligence and traditional techniques, thereby providing more alternatives and ideas for those researchers who are interested in this field of study. [source] A study of creative tension of engineering students in KoreaHUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES, Issue 6 2007Yoon Chang The aim of this research was to study the nature of creative tension of engineering students in South Korea. The creative tension was analyzed according to relevant competences in project managers' work role. Most of the subjects who participated in this study were part-time students who worked as managers in manufacturing and industrial companies. The application used for collecting and analyzing data was the project managers' work-role,based competence application, Cycloid. Data were collected on the Internet by self-evaluation. The constructed competence model of the Cycloid application was added into the Evolute self-evaluation system utilizing fuzzy logic. The application was able to identify students' current state and personal aims and the creative tension essential for their personal development. The Cycloid application can be utilized in developing the professional competencies of individuals, teams, and organizations. © 2007 Wiley Periodicals, Inc. Hum Factors Man 17: 511,520, 2007. [source] Rule reduction in fuzzy logic for better interpretability in reservoir operationHYDROLOGICAL PROCESSES, Issue 21 2007C. Sivapragasam Abstract Decision-making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (,low', ,high', etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering-based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine-tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd. [source] Application of fuzzy logic to forecast seasonal runoffHYDROLOGICAL PROCESSES, Issue 18 2003C. Mahabir Abstract Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeastern Alberta, are two basins that require this type of late winter forecast of potential spring runoff. Historically, the forecast has been based upon a combination of regression equations. These results are then interpreted by a forecaster and are modified based on the forecaster's heuristic knowledge of the basin. Unfortunately, this approach has had limited success in the past, in terms of the accuracy of these forecasts, and consequently an alternative methodology is needed. In this study, the applicability of fuzzy logic modelling techniques for forecasting water supply was investigated. Fuzzy logic has been applied successfully in several fields where the relationship between cause and effect (variable and results) are vague. Fuzzy variables were used to organize knowledge that is expressed ,linguistically' into a formal analysis. For example, ,high snowpack', ,average snowpack' and ,low snowpack' became variables. By applying fuzzy logic, a water supply forecast was created that classified potential runoff into three forecast zones: ,low', ,average' and ,high'. Spring runoff forecasts from the fuzzy expert systems were found to be considerably more reliable than the regression models in forecasting the appropriate runoff zone, especially in terms of identifying low or average runoff years. Based on the modelling results in these two basins, it is concluded that fuzzy logic has a promising potential for providing reliable water supply forecasts. Copyright © 2003 John Wiley & Sons, Ltd. [source] FLSAC: A new scheme to defend against greedy behavior in wireless mesh networksINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 10 2009Soufiene Djahel Abstract The most commonly used medium access mechanism in wireless mesh networks is based on the CSMA/CA protocol. This protocol schedules properly the access to the medium among all the competing nodes. However, in a hostile environment, such as wireless mesh networks (WMNs), selfish or greedy behaving nodes may prefer to decline the proper use of the protocol rules in order to increase their bandwidth shares at the expense of the well-behaving nodes. In this paper, we focus on such misbehavior and in particular on the adaptive greedy misbehavior of a node in the context of WMN environment. In such environment, wireless nodes compete to gain access to the medium and communicate with a mesh router (MR). In this case, a greedy node may violate the protocol rules in order to earn extra bandwidth share upon its neighbors. In order to avoid its detection, this node may adopt different techniques and switch dynamically between each of them. To counter such misbehavior, we propose to use a fuzzy logic-based detection scheme. This scheme, dubbed FLSAC, is implemented in the MR/gateway to monitor the behavior of the attached wireless nodes and report any deviation from the proper use of the protocol. The simulation results of the proposed FLSAC scheme show robustness and its ability to detect and identify quickly any adaptive cheater. Copyright © 2009 John Wiley & Sons, Ltd. [source] Interval type-2 fuzzy logic for edges detection in digital imagesINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2009Olivia Mendoza Edges detection in a digital image is the first step in an image recognition system. In this paper, we show an efficient edges detector using an interval type-2 fuzzy inference system (FIS-2). The FIS-2 uses as input the original images after applying Sobel filters and attenuation filters, then the fuzzy rules infer normalized values for the edges images, especially useful to enhance the performance of neural networks. To illustrate the results, we built frequency histograms of some images and compare the results of the FIS-2 edge's detector with the gradient magnitude method and a type-1 fuzzy inference system (FIS-1). The FIS-2 results are better than the gradient magnitude and FIS-1, because the edges preserve more detail of the original images, and the backgrounds are more homogeneous than with FIS-1 and the gradient's magnitude method. © 2009 Wiley Periodicals, Inc. [source] On the difference of fuzzy setsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 3 2008Claudi Alsina We formulate and solve a collection of functional equations arising in the framework of fuzzy logic when modeling the concept of a difference operation between couples of fuzzy sets. © 2008 Wiley Periodicals, Inc. [source] A fuzzy logic approach to experience-based reasoningINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2007Zhaohao Sun Experience-based reasoning (EBR) is a reasoning paradigm that has been used in almost every human activity such as business, military missions, and teaching activities since early human history. However, EBR has not been seriously studied from either a logical or mathematical viewpoint, although case-based reasoning (CBR) researchers have paid attention to EBR to some extent. This article will attempt to fill this gap by providing a unified fuzzy logic-based treatment of EBR. More specifically, this article first reviews the logical approach to EBR, in which eight different rules of inference for EBR are discussed. Then the article proposes fuzzy logic-based models to these eight different rules of inference that constitute the fundamentals for all EBR paradigms from a fuzzy logic viewpoint, and therefore will form a theoretical foundation for EBR. The proposed approach will facilitate research and development of EBR, fuzzy systems, intelligent systems, knowledge management, and experience management. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 867,889, 2007. [source] Multicriteria group decision making under incomplete preference judgments: Using fuzzy logic with a linguistic quantifierINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 6 2007Duke Hyun Choi In the face of increasing global competition and complexity of the socioeconomic environment, many organizations employ groups in decision making. Inexact or vague preferences have been discussed in the decision-making literature with a view to relaxing the burden of preference specifications imposed on the decision makers and thus taking into account the vagueness of human judgment. In this article, we present a multiperson decision-making method using fuzzy logic with a linguistic quantifier when each group member specifies incomplete judgment possibly both in terms of the evaluation of the performance of different alternatives with respect to multiple criteria and on the criteria themselves. Allowing for incomplete judgment in the model, however, makes a clear selection of the best alternative by the group more difficult. So, further interactions with the decision makers may proceed to the extent to compensate for the initial comfort of preference specifications. These interactions, however, may not guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfactory solution by the use of a linguistic-quantifier-guided aggregation that implies the fuzzy majority. This is an approach that combines a prescriptive decision method via mathematical programming and a well-established approximate solution method to aggregate multiple objects. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 641,660, 2007. [source] On Elkan's theorems: Clarifying their meaning via simple proofsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 2 2007Radim B, lohlávek This article deals with the claims that "a standard version of fuzzy logic collapses mathematically to two-valued logic" made by Charles Elkan in two papers [Proc 11th National Conf on AI, Menlo Park, CA: AAAI Press, 1993, pp 698,703; IEEE Expert 1994;9:3,8]. Although Elkan's effort to trivialize fuzzy logic has been questioned by numerous authors, our aim is to examine in detail his formal arguments and make some new observations. We present alternative, considerably simpler proofs of Elkan's theorems and use these proofs to argue that Elkan's claims are unwarranted. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 203,207, 2007. [source] A majority model in group decision making using QMA,OWA operatorsINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 2 2006J.I. Peláez Group decision-making problems are situations where a number of experts work in a decision process to obtain a final value that is representative of the global opinion. One of the main problems in this context is to design aggregation operators that take into account the individual opinions of the decision makers. One of the most important operators used for synthesizing the individual opinions in a representative value of majority in the OWA operator, where the majority concept used aggregation processes, is modeled using fuzzy logic and linguistic quantifiers. In this work the semantic of majority used in OWA operators is analyzed, and it is shown how its application in group decision-making problems does not produce representative results of the concept expressed by the quantifier. To solve this type of problem, two aggregation operators, QMA,OWA, are proposed that use two quantification strategies and a quantified normalization process to model the semantic of the linguistic quantifiers in the group decision-making process. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 193,208, 2006. [source] Incremental learning of collaborative classifier agents with new class acquisition: An incremental genetic algorithm approachINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2003Sheng-Uei Guan A number of soft computing approaches such as neural networks, evolutionary algorithms, and fuzzy logic have been widely used for classifier agents to adaptively evolve solutions on classification problems. However, most work in the literature focuses on the learning ability of the individual classifier agent. This article explores incremental, collaborative learning in a multiagent environment. We use the genetic algorithm (GA) and incremental GA (IGA) as the main techniques to evolve the rule set for classification and apply new class acquisition as a typical example to illustrate the incremental, collaborative learning capability of classifier agents. Benchmark data sets are used to evaluate proposed approaches. The results show that GA and IGA can be used successfully for collaborative learning among classifier agents. © 2003 Wiley Periodicals, Inc. [source] | |||||||||||||||||||