Fuzzy Similarity Degree Research Articles

A HRGO approach for resilience enhancement service composition and optimal selection in cloud manufacturing.

Cloud manufacturing (CM) establishes a collaborative manufacturing services chain among dispersed producers, which enables the efficient satisfaction of personalized manufacturing requirements. To further strengthen this effect, the manufacturing service composition and optimal selection (SCOS) in CM, as a NP-hard combinatorial problem, is a crucial issue. Quality of service (QoS) attributes of manufacturing services, as the basic criterion of functions and capabilities, are decisive criterions of SCOS. However, most traditional QoS attributes of CM ignore the dynamic equilibrium of manufacturing services and only rely on initial static characterizations such as reliability and availability. In a high uncertainty and dynamicity environment, a major concern is the equilibrium of manufacturing services for recovering their functions after dysfunctional damage. Therefore, this paper proposes a hybrid resilience-aware global optimization (HRGO) approach to address the SCOS problem in CM. This approach helps manufacturing demanders to acquire efficient, resilient, and satisfying manufacturing services. First, the problem description and resilience measurement method on resilience-aware SCOS is modeled. Then, a services filter strategy, based on the fuzzy similarity degree, is introduced to filter redundant and unqualified candidate services. Finally, a modified non-dominated sorting genetic algorithm (MNSGA-III) is proposed, based on diversity judgment and dualtrack parallelism, to address combination optimization step processing in SCOS. A series of experiments were conducted, the results show the proposed method is more preferable in optimal services searching and more efficient in scalability.

Analytic expression of Mamdani fuzzy system constructed by fuzzy similarity degree and its output algorithm

Analytic expression of Mamdani fuzzy system constructed by fuzzy similarity degree and its output algorithm

A Hybrid Energy-Aware Resource Allocation Approach in Cloud Manufacturing Environment

Cloud manufacturing (CMfg) is a new service-oriented production paradigm from the wide application of cloud computing for the manufacturing industry. The aim of this manufacturing mode is to provide resource-sharing and on-demand manufacturing mode to improve operation efficiency. Resource allocation is considered as a crucial technology to implement CMfg. Traditional resource allocation approaches in CMfg mainly focus on the optimal resource selection process, but the energy consumption for manufacturing resources is rarely considered. In response, this paper proposes a hybrid energy-aware resource allocation approach to help requestors acquire energy-efficient and satisfied manufacturing services. The problem description on energy-aware resource allocation in CMfg is first summarized. Then a local selection strategy based on fuzzy similarity degree is put forth to obtain appropriate candidate services. A multi-objective mathematical model for energy-aware service composition is established and the nondominated sorting genetic algorithm (NSGA-II) is adopted to conduct the combinatorial optimization process. Furthermore, a technique for order preference by similarity to an ideal solution is used to determine the optimal composite services. Finally, a case study is illustrated to validate the effectiveness of the proposed approach.

A New Definition of Intuitionistic Fuzzy Similarity Degree

As far as the problem of intuitionistic fuzzy cluster analysis is concerned, this paper proposes a new formula of similarity degree with attribute weight of each index. We conduct a fuzzy cluster analysis based on the new intuitionistic fuzzy similarity matrix, which is constructed via this new weighted similarity degree method and can be transformed into a fuzzy similarity matrix. Moreover, an example is given to demonstrate the feasibility and validity of this method.

HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems

Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users’ demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method.

A New Approach to the Similarity Analysis in Bionic Engineering and its Applications

The similarity phenomenon exists widely in nature and similarity theory is of importance in various types of engineering problems, especially in bionic engineering. In this work,a conception of fuzzy similarity was introduced. An effective mathematical method was put forward to identify the extent of morphological similarity between two living things of the homogenous species or the heterogeneous species and quantitative analysis of the similarity degree was carried out. A computer program based on the genetic algorithm was developed to solve and optimize the numerical scaling coefficient between an original profile data and a target one. To seek the maximum fuzzy similarity degree, an object function which is the core idea in this method was built to compute the similarity coefficient according to the corresponding points in these two sample profiles. Moreover, a series of arrays from the profiles of the foreleg of praying mantis (Mantis religiosa Linnaeus) was imported into the program. The results indicated the fuzzy similarity degree of samples was considerably high, which means the shape of the praying mantiss apical claw is quite uniform. Therefore, this computation method of similarity degree is effective.

A netting clustering analysis method under intuitionistic fuzzy environment

In this paper, we investigate the technique for clustering objects with intuitionistic fuzzy information. We first propose a formula to derive the intuitionistic fuzzy similarity degree between two intuitionistic fuzzy sets and develop an approach to constructing an intuitionistic fuzzy similarity matrix. Then, we present a netting method to make clustering analysis of intuitionistic fuzzy sets via intuitionistic fuzzy similarity matrix. Two numerical examples are given to illustrate and verify our method.

Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

Based on fuzzy similarity degree, entropy, relative entropy and fuzzy entropy, the symmetric fuzzy relative entropy is presented, which not only has a full physical meaning, but also has succinct practicability. The symmetric fuzzy relative entropy can be used to measure the divergence between different fuzzy patterns. The example demonstrates that the symmetric fuzzy relative entropy is valid and reliable for fuzzy pattern recognition and classification, and its classification precision is very high.

Measuring complexity using FuzzyEn, ApEn, and SampEn

This paper compares three related measures of complexity, ApEn, SampEn, and FuzzyEn. Since vectors’ similarity is defined on the basis of the hard and sensitive boundary of Heaviside function in ApEn and SampEn, the two families of statistics show high sensitivity to the parameter selection and may be invalid in case of small parameter. Importing the concept of fuzzy sets, we developed a new measure FuzzyEn, where vectors’ similarity is defined by fuzzy similarity degree based on fuzzy membership functions and vectors’ shapes. The soft and continuous boundaries of fuzzy functions ensure the continuity as well as the validity of FuzzyEn at small parameters. The more details obtained by fuzzy functions also make FuzzyEn a more accurate entropy definition than ApEn and SampEn. In addition, similarity definition based on vectors’ shapes, together with the exclusion of self-matches, earns FuzzyEn stronger relative consistency and less dependence on data length. Both theoretical analysis and experimental results show that FuzzyEn provides an improved evaluation of signal complexity and can be more conveniently and powerfully applied to short time series contaminated by noise.