This paper investigates the stress-strength reliability in the presence of fuzziness. The fuzzy membership function is defined as a function of the difference between stress and strength values, and the fuzzy reliability of single unit and multicomponent systems are calculated. The inclusion of fuzziness in the stress-strength interference enables the user to make more sensitive analysis. Illustrations are presented for various stress and strength distributions, (C) 2015 Elsevier B.V. All rights reserved.
In this paper, we propose a method to construct a polygonal rough-fuzzy set from a set of polygonal fuzzy sets representing the aggregation of multiple experts' opinions and propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the ratio of fuzziness of the constructed polygonal rough-fuzzy sets, where the values of the antecedent variables and the consequence variable appearing in the fuzzy rules are represented by the constructed polygonal rough-fuzzy sets. The proposed fuzzy interpolative reasoning method can overcome the drawbacks of the existing method due to the fact that it can deal with fuzzy interpolative reasoning using polygonal rough-fuzzy sets and it gets more reasonable fuzzy interpolative reasoning results than the existing method. (C) 2014 Elsevier Inc. All rights reserved.
This paper studies the pricing and retail service decisions of a product in a supply chain with one manufacturer and two retailers. It is assumed that the supply chain is operated in fuzzy uncertainty environments. The fuzziness is associated with the customer demands, manufacturing costs and service cost coefficients. Three different game structures are considered, i.e., Manufacturer-leader Stackelberg, Retailer-leader Stackelberg, and Vertical Nash. Expected value models are developed to determine the optimal pricing and retail service strategies. The corresponding analytical equilibrium solutions are obtained by solving the models. Finally, numerical examples are presented to illustrate the effectiveness of the theoretical results, and to gain various marketing strategies employed under different situations. (C) 2014 Elsevier Inc. All rights reserved.
5] has shown that the intersection of any two fuzzy subgroups is also a fuzzy subgroup and we now show that the intersection of any two anti fuzzy subgroups is also an anti fuzzy subgroup. More over, we state and prove the anti fuzzy version the work of 6] in characterizing union of fuzzy subgroups. Besides, 2] and 7] have worked on the set of all fuzzy symmetric subgroup of the symmetric group F (S_n).
Aiming at the weakness of the existing cloud neural network on training and practicality, a new improved structure of cloud neural network is designed. A hidden layer is added prior to the inverse cloud layer. Threshold level is set to zero and a simple training method is designed. In addition, considering the ignorance of signal randomness and fuzziness in the existing method of the flatness signal recognition, the cloud neural network combines the advantages of the fuzziness and randomness of cloud model and the learning and memory ability of neural network. Thus it is applied in the flatness signal recognition. The simulation contrast results demonstrate that the improved structure is able to identify common defects in shape with higher identity precision.
In this paper, we first review the existing entropy measures for hesitant fuzzy elements (HFEs) and demonstrate that the existing entropy measures for HFEs fail to effectively distinguish some apparently different HFEs in some cases. Then, we propose a new axiomatic framework of entropy measures for HFEs by taking fully into account two facets of uncertainty associated with an HFE (i.e., fuzziness and nonspecificity). We adopt a two-tuple entropy model to represent the two types of uncertainty associated with an HFE. Additionally, we discuss how to formulate each kind of uncertainty. For each of fuzziness and nonspecificity, some simple methods are provided to construct measures, which can well handle the problems in the existing entropy measures for HFEs. Several examples are given to illustrate each method, and comparisons with the existing entropy measures are also offered.
Wheeler's 'spacetime-foam' 1 picture of quantum gravity (QG) suggests spacetime fuzziness (fluctuations leading to non-deterministic effects) at distances comparable to the Planck length, L-Pl approximate to 1.62 x 10(-33) cm, the inverse (in natural units) of the Planck energy, E-Pl approximate to 1.22 x 10(19) GeV. The resulting non-deterministic motion of photons on the Planck scale is expected to produce energy-dependent stochastic fluctuations in their speed. Such a stochastic deviation from the well-measured speed of light at low photon energies, c, should be contrasted with the possibility of an energy-dependent systematic, deterministic deviation. Such a systematic deviation, on which observations by the Fermi satellite set Planck-scale limits for linear energy dependence(2), is more easily searched for than stochastic deviations. Here, for the first time, we place Planck-scale limits on the more generic spacetime-foam prediction of energy-dependent fuzziness in the speed of photons. Using high-energy observations from the Fermi Large Area Telescope (LAT) of gamma-ray burst GRB090510, we test a model in which photon speeds are distributed normally around c with a standard deviation proportional to the photon energy. We constrain the model's characteristic energy scale beyond the Planck scale at >2.8E(Pl)(>1.6E(Pl)), at 95% (99%) confidence. Our results set a benchmark constraint to be reckoned with by any QG model that features spacetime quantization.
How to carry out the fuzzy signal processing to an image is a problem to be solved urgently in many departments. For fuzziness on image processing, this paper studies a variety of fuzzy signal, implements the denoising fuzziness processing, presents some methods and algorithms for fuzzy signal processing, and compares with other methods on image processing. At the same time, this paper uses the wavelet analysis to carry out feature extraction of target for the first time, extracts the coefficient feature and energy feature of image decomposition, gives the matching and recognition methods, compares with the existing target recognition methods by experiment, and presents a target recognition method based on region of interest. Using the combining method of simulation and instance experiments, this paper systematically analyzes the validity of the model and algorithms. Moreover, using the wavelet transform to carry out the image decomposition, this paper extracts the coefficient feature of wavelet transform, gives the matching and recognition methods, and compares with the existing target recognition methods by experiment. Through experiment results, the proposed recognition method has the high precision, fast speed, and its correct recognition rate is improved by an average 5.16% than that of existing recognition methods. These researches in this paper can provide a new way of thinking for the researchers in pattern recognition field.
In this paper a toy model of quantum topology is reviewed to study effects of matter and gauge fields on the topology fluctuations. In the model a collection of N one-dimensional manifolds is considered where a set of boundary conditions on states of Hilbert space specifies a set of all topologies perceived by quantum particle and probability of having a specific topology is determined by a partition function over all the topologies in the context of noncommutative spectral geometry. In general the topologies will be fuzzy with the exception of a particular case which is localized by imposing a specific boundary condition. Here fermions and bosons are added to the model. It is shown that in the presence of matter, the fuzziness of topology will be dependent on N, however for large N the dependence is removed similar to the case without matter. Also turning on a particular background gauge field can overcome the fuzziness of topology to reach a localized topology with classical interpretation. It can be seen that for large N more opportunities can be provided for choosing the background gauge field to localize the fuzzy topology.