Membership function

About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.

Reliability assessment for fuzzy multi-state systems

Abstract: Fuzzy multi-state system (FMSS) is defined as a multi-state system (MSS) consisting of multi-state elements (MSE) whose performance rates and transition intensities are presented as fuzzy values Due to the lack, inaccuracy or fluctuation of data, it is oftentimes impossible to evaluate the performance rates and transition intensities of MSE with precise values This is true especially in continuously degrading elements that are usually simplified to MSE for computation convenience To overcome these challenges in evaluating the behaviour of MSS, fuzzy theory is employed to facilitate MSS reliability assessment Given the fuzzy transition intensities and performance rates, the state probabilities of MSE and MSS are also fuzzy values A fuzzy continuous-time Markov model with finite discrete states is proposed to assess the fuzzy state probability of MSE at any time instant The universal generating function with fuzzy state probability function and performance rate is applied to evaluate fuzzy state probability of MSS in accordance with the system structure A modified FMSS availability assessment approach is introduced to compute the system availability under the fuzzy user demand In order to obtain the membership functions of the indices of interest, parametric programming technique is employed according to Zadeh's extension principle The effectiveness of the proposed method is illustrated and verified via reliability assessment of a multi-state power generation system

More on Intuitionistic Fuzzy Soft Sets

Abstract: New operations on intuitionistic fuzzy soft sets have been introduced in this paper. Some results relating to the properties of these operations have been established. An example has also been introduced as an application of these operations.

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.

Fuzzy logic control with genetic membership function parameters optimization for the output regulation of a servomechanism with nonlinear backlash

Abstract: The paper presents a hybrid architecture, which combines Type-1 or Type-2 fuzzy logic system (FLS) and genetic algorithms (GAs) for the optimization of the membership function (MF) parameters of FLS, in order to solve to the output regulation problem of a servomechanism with nonlinear backlash. In this approach, the fuzzy rule base is predesigned by experts of this problem. The proposed method is far from trivial because of nonminimum phase properties of the system. The simulation results illustrate the effectiveness of the optimized closed-loop system.