Dynamic Fuzzy Research Articles

A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation

Selection of appropriate subcontractors for outsourcing is very important for the success of construction projects. This can improve the overall quality of projects and promote the qualification and reputation of the main contractors. The evaluation of subcontractors can be made by some experts or decision-makers with respect to some criteria. If this process is done in different time periods, it can be defined as a dynamic multi-criteria group decision-making (MCGDM) problem. In this study, we propose a new fuzzy dynamic MCGDM approach based on the EDAS (Evaluation based on Distance from Average Solution) method for subcontractor evaluation. In the procedure of the proposed approach, the sets of alternatives, criteria and decision-makers can be changed at different time periods. Also, the proposed approach gives more weight to newer decision information for aggregating the overall performance of alternatives. A numerical example is used to illustrate the proposed approach and show the application of it in subcontractor evaluation. The results demonstrate that the proposed approach is efficient and useful in real-world decision-making problems.

A Secure and Dynamic Fuzzy Multi-Keyword Ranked Search over Encrypted Cloud Data Without Access Pattern Leakage

Recent years, Searchable Encryption (SE) has become one of the hottest topics in cloud computing, because of its flexibility and availability. Most of existing SE schemes focus on the improvement of its efficiency, accuracy, and availability. However, the majority of them leak data access patterns due to their schemes constructions. In this paper, we present a secure symmetric searchable encryption scheme without access pattern leakage, based on Fu’s scheme. By constructing two cloud servers to replace the original server, our scheme protects data users from access pattern leakage.

Artificial Life Environment Modeled by Dynamic Fuzzy Cognitive Maps

This paper presents an artificial life environment based on dynamic fuzzy cognitive maps (DFCMs) and inspired by multiagent systems, machine learning, and concepts from classical fuzzy cognitive map theory. The proposed architecture includes features such as a reinforcement learning algorithm to dynamically fine-tune the weights of the DFCM, a finite states machine, governing the behavior of the creatures by adding/removing concepts into the DFCM, and others. These features are used to add adaptability to the artificial creatures (agents) in a simulated hunter-prey environment with synthetic data. Some experiments carried out in a simulated virtual environment have shown promising results for further research in the subject of this paper.

RETRACTED ARTICLE: Multi-level Dynamic Fuzzy Evaluation and BP Neural Network Method for Performance Evaluation of Chinese Private Enterprises

Based on the fact that the current neural network technology is difficult to find the high quality training sample data in the process of the private enterprise application, aiming at the present situation of the performance grading of the private enterprise, this paper puts forward the performance evaluation of the private enterprise, which is combined with the multilevel dynamic fuzzy evaluation and the BP neural Network, The evaluation model is established by artificial neural theory, and a decision support system for performance evaluation of private enterprises is established by using the multiple hidden layer neural network structure and the reverse propagation (BP) algorithm training network.

FRC: A New Dynamic Fuzzy Clustering Alogorithm Based on Random Forest Algorithm

FRC: A New Dynamic Fuzzy Clustering Alogorithm Based on Random Forest Algorithm

A Novel Software Tool for Detection of Meniscus Injury using Dynamic Fuzzy Cognitive Networks

Objective: The main objective of this study is the construction of a tool to diagnose a specific type of knee injuries, the meniscus injury, without the aid of imaging modalities. This tool exploits physician’s knowledge and experience and extracts all information needed for a diagnostic procedure. Methods: A specific type of Fuzzy Cognitive Maps, which is named Dynamic Fuzzy Cognitive Networks, is used for the implementation of diagnostic process. This method simulates the physician’s way of thinking and making decisions in order to conclude to a final diagnosis and will constitute the background of a user interface platform, named “KneeD,” designed especially as a supportive tool for the physician. It exploits information about patient history and clinical examination and the arisen symptoms and risk factors constitute the main attributes that contribute to injury identification. Results: A pilot sample of 17 patients with knee injuries who arrived at the University Hospital of Patras, in Greece, was examined by the orthopedic clinician and the diagnoses confirmed with MRI were compared to our platform’s outcomes. Results concerned a) the initial distinction between “meniscus injury” and “other disease” and b) discrimination between acute and degenerative injury for patients positive to meniscal tear from the first level. Both levels provided us with very satisfactory results in total agreement with MRI outcomes. Conclusion: The outcome of a close collaboration between engineers and medical doctors was a platform for real time diagnosing of knee injuries, which is simple, user friendly, real-time, easily accessible, fast, reliable, and low-cost. Furthermore, it could also be used as a patient storage database. The obtained results have been evaluated by orthopedic surgeons who found them very satisfactory.

Dynamic Fuzzy Reliability Analysis of Multistate Systems Based on Universal Generating Function

Considering the fuzziness of load, strength, operational states, and state probability, reliability models of multistate systems are developed based on universal generating function (UGF). The fuzzy UGF of load and the fuzzy UGF of strength are proposed in this paper, which are used to derive the fuzzy component UGF and the fuzzy system UGF. By defining the decomposition operator and the inner product operator, failure dependence and effects of multiple load applications are taken into account in the established reliability models. Moreover, dynamic fuzzy reliability models of multistate systems are constructed considering the strength degradation of components. The results show that failure dependence and the effects of multiple load applications have significant impacts on system reliability, which considerably decrease system reliability and increase the fuzziness of system reliability under low performance requirements. Besides, in the dynamic reliability analysis of multistate systems, strength degradation dependence could lead to large computational error.

Random Seismic Response and Dynamic Fuzzy Reliability Analysis of Bedding Rock Slopes Based on Pseudoexcitation Method

Random Seismic Response and Dynamic Fuzzy Reliability Analysis of Bedding Rock Slopes Based on Pseudoexcitation Method

A Dynamic Fuzzy Expert System Based on Maintenance Indicators for Service Type Selection of Machinery

A Dynamic Fuzzy Expert System Based on Maintenance Indicators for Service Type Selection of Machinery

Fluid dynamic modeling and fuzzy proportional–integral–derivative–based ink-supply method for piezoelectric ink-jet printing

To resolve the problems of ink drop tailing, satellite drop, and ink ribbon of piezoelectric ink-jet printing, a fluid dynamic modeling method and an adaptive ink-supply method are proposed. First, based on the volume of fluid method, a fluid dynamic model for the jetting and forming of ink drop is set up. According to this model, the numerical analysis for the states of forming, fracture, and flight of ink drops is performed; referring to different boundary conditions of piezoelectric ink-jet process, the inherent relations between ink characteristics and ink drop motion are analyzed, and an optimal viscosity range for piezoelectric nozzle is confirmed. Then, a fuzzy proportional–integral–derivative–based ink-supply control method is developed to achieve the fast and smooth responses for ink temperature and to obtain the required ink characteristics and ink-supply effects. Finally, a microcontroller-based piezoelectric ink-jetting experiment platform is established, and the experiment results show that the fluid dynamic modeling method can simulate the flow field profiles of piezoelectric ink-jet; the fuzzy proportional–integral–derivative–based ink-supply system can improve the printing quality; the print stripes are decreased obviously, and the softer and cleaner images can be obtained.

A New Approach of Dynamic Fuzzy Cognitive Knowledge Networks in Modelling Diagnosing Process of Meniscus Injury

A new approach of Dynamic Fuzzy Cognitive Knowledge Networks is presented. This is an evolutionary type of Fuzzy Cognitive Maps (FCM) that arose from the need for updating classic methodology in order to overcome its drawbacks, concerning the single calculation rule, stability and real time problems and expand its use in a variety of applications. This new approach is being tested for its accuracy in Decision Support Systems in medicine, trying to model knee injuries by using 17 real cases of patients. The new proposed model is able to diagnose meniscus injuries and to distinguish between acute and degenerative injury. Subsequently we observe the evolution of the injury by administering a proposed treatment by the physician. Results of this new method, which are presented in detail, are very satisfactory for both two levels and treatment stage, and in total agreement with Magnetic Resonance Imaging outcomes. The whole methodology is the outcome of a close collaboration between engineers and medical doctors and is significant because it is a promising tool which sets aside the main disadvantages of Fuzzy Cognitive Maps and allows us a wide use in many real time problems.

A Dynamic Fuzzy Temporal Clustering for Imprecise Location Streams

The clustering has provided data analysis in many contexts of Computer Science. It is widely applied in Ambient Intelligence and Ubiquitous Computing for information processing, with geolocation data prominently. In this paper, we introduce a dynamic fuzzy temporal clustering algorithm (DFTC) to detect stays of users in urban environments based on locations from imprecise sensors. Our approach includes fuzzy evaluation of temporal and probabilistic data providing analysis in real time. As results, we have developed a mobile application which integrates the DFTC and detects satisfactorily user stays related to urban commerces from a real environment.

Dynamic Fuzzy Cognitive Maps Applied in Realibility Centered Maintanance of Electric Motors

The industry has systems and machines that need to operate within appropriate parameters to ensure quality in production. In this context it is necessary to maintain, through maintenance, the conditions necessary for proper operation. Thus, through the Reliability Centered Maintenance with quantitative feedback by Fuzzy Cognitive Maps applied to electric motors, can suggest a better reliability, proposal of this research. This paper discusses the Reliability Centered Maintenance (RCM) with reference to a generic Check list of electric motors' maintenance. Through the maintenance actions for correction of faults and/or defects, it can be modeled a critical and qualitative FCM that will present a quantitative diagnosis aimed at a proposal for a computational tool to assist in the maintenance management, adding improvements to the system

An Interactive Dynamic Fuzzy Goal Programming for Bi-level Multiobjective Linear Fractional Programming Problems.

This paper presents an interactive dynamic fuzzy goal programming (DFGP) approach for solving bi-level multiobjective linear fractional programming (BL MOLFP) problems with the characteristics of dynamic programming (DP). In the proposed approach, the membership function of the objective goals of a problem with fuzzy aspiration levels are defined first as the membership function for vector of fuzzy goals of the decision variables controlled by first–level decision maker are developed first in the model formulation of the problem. The method of variable change, on the under and over deviational variables of the membership goals associated with the fuzzy goals of the model, is introduced to solve the problem efficiently by using linear goal programming (LGP) methodology. Then, under the framework of preemptive priority based GP, a multi stage DP model of the problem is used for achievement of the highest degree (unity) of each of the membership functions. In the decision process, the goal satisficing philosophy of GP is used recursively to arrive at the most satisfactory solution and the suggested algorithm to simplify the solution procedure by DP at each stage is proposed. This paper is considered as an extension work of Mahmoud A. Abo-Sinna and Ibrahim A. Baky (2010) by using dynamic approach. Finally, this approach is illustrated by a given numerical example.

Analysis of C2C Website Competitiveness Based on Text Sentiment Analysis and Dynamic Fuzzy Computation

With the global step into the era of large data, online review data show an explosive growth trend, and the resulting information overload has affected the consumer’s decision making. In terms of consumers, to tap the purchase decision-making information from the vast amount of information is a must-do before the tedious process; how to find the best service from a variety of shopping sites and find satisfied products from thousands of Products and how to help companies improve themselves, has become the forefront of e-commerce research topics. In this paper, data acquisition, data filtering, theme analysis, correlation analysis, sentiment analysis and dynamic fuzzy statistics are introduced to the user-generated content (UGC) fuzzy comprehensive evaluation. Firstly, collect the data, we use the correlation analysis and the stopped words to reduce the dimensionality of the data, and generate the revised candidate sets of themes. Then, we use emotion extraction, emotion polarity judgment and comment analysis to divide the UGC into several evaluation grades; then we get the fuzzy comprehensive evaluation matrix by the fuzzy statistic method. Finally, the comprehensive evaluation model is constructed to give users product score and the C2C website ranking.

A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps

This work presents a cooperative architecture for navigation of a swarm of robots based on Dynamic Fuzzy Cognitive Maps (DFCM). This architecture is used to develop homogeneous autonomous robots without any type of global controllers. The developed autonomous navigation system has skills for learning, self-adaptation, behavior management and cooperative data share. We adopt a multi-agent approach, inspired by Brooks subsumption architecture, which allows hierarchical management of actions and parallel processing. Reinforcement learning is used to self-tune the navigation system parameters allowing the DFCM model to be self-adaptive. Two strategies are analyzed for data and experience exchange between agents (robots). The first one is based on a navigation memory sharing and the other is a bio inspired by ant's behavior strategy. Results are presented with both strategies and comparisons of their performance are carried out in a virtual environment.

Scheduling Semiconductor Wafer Fabrication Using a New Fuzzy Association Classification Rules Based on Dynamic Fuzzy Partition

This paper proposes a dynamic fuzzy partition method of attribute domain suitable for the scheduling problem of Semiconductor wafer fabrication (SWF). Then, based on the above partition method, this paper gives a new Fuzzy association classification rules (FACRs) for scheduling SWF. Also, this paper presents a corresponding simple mining method used to obtain the effective FACRs based on the Apriori algorithm. Furthermore, a Harmony search (HS) algorithm is designed to determine the rule parameters including the minimum fuzzy support and the total number of linguistic values of each condition attribute for the simple fuzzy partition. At last, computational simulations and comparisons based on the practical data are provided. It is shown that the proposed FACRs can generate better results for almost all problem instances.

Fault Diagnosis of Coal Equipment Based on Dynamic Fuzzy Neural Network and BP Neural Network

Fault Diagnosis of Coal Equipment Based on Dynamic Fuzzy Neural Network and BP Neural Network

Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation

Artificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.

Approximation of Series of Expert Preferences by Dynamical Fuzzy Numbers

In this paper, we consider a method of formalization of time series of arbitrary expert assessments under uncertainty conditions using both approximation and fuzzy arithmetic. We present basic definitions and a numerical example of calculation of fuzzy measures and propose comparative analysis of advantages and disadvantages of this method.