New Configurations of the Fuzzy Fractional Differential Boussinesq Model with Application in Ocean Engineering and Their Analysis in Statistical Theory

The fractional-order Boussinesq equations(FBSQe)are investigated in this work to see if they can effectively improve the situation where the shallow water equation cannot directly handle the dispersion wave.The fuzzy forms of analytical FBSQe solutions are first derived using the Adomian decomposition method.It also occurs on the sea floor as opposed to at the functionality.A set of dynamical partial differential equations(PDEs)in this article exemplify an unconfined aquifer flow implication.Thismethodology can accurately simulate climatological intrinsic waves,so the ripples are spread across a large demographic zone.The Aboodh transform merged with the mechanism of Adomian decomposition is implemented to obtain the fuzzified FBSQe in R,R^(n) and(2nth)-order involving generalized Hukuhara differentiability.According to the system parameter,we classify the qualitative features of the Aboodh transform in the fuzzified Caputo and Atangana-Baleanu-Caputo fractional derivative formulations,which are addressed in detail.The illustrations depict a comparison analysis between the both fractional operators under gH-differentiability,as well as the appropriate attributes for the fractional-order and unpredictability factorsσ∈[0,1].A statistical experiment is conducted between the findings of both fractional derivatives to prevent changing the hypothesis after the results are known.Based on the suggested analyses,hydrodynamic technicians,as irrigation or aquifer quality experts,may be capable of obtaining an appropriate storage intensity amount,including an unpredictability threshold.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture

The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.

Research on Maintainability Evaluation Model Based on Fuzzy Theory

Maintainability influencing attributes are analyzed, their weight and value calculating methods are given, and the maintainability fuzzy evaluation method is proposed based on the relative closeness. According to the maintenance task simulation operated in virtual environment, the maintainability virtual evaluation model is built by analyzing the maintenance task for each replaceable unit of product. At last, a case study is given based upon the main landing gear system of a certain type civil aircraft, and the result indicates that the model is suitable for maintainability qualitative evaluation and can support maintainability concurrent design.

ESTIMATION METHOD FOR AIRCRAFT SIMILARITY BASED ON FUZZY THEORY AND GREY INCIDENCE ANALYSIS

An estimation method for aircraft similarity based on fuzzy theory and grey incidence analysis is presented. This estimation method is made up of the triangular fuzzy transforming model of linguistic variables and the method of grey incidence analysis. Nine feature attributes of aircraft are selected to estimate the similarity between the new aircraft and the existing aircraft. A new aircraft X and other six existing aircrafts are taken as examples. Analyses show that similarity estimation results obtained from the method are in accordance with practice.

Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine

A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares(FLS)-support vector machine(SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.

Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic ele- ment is the test-diagnosis fault unit. Every underlying fault cause＇s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.

Ecological suitability evaluation for urban growth boundary in red soil hilly areas based on fuzzy theory

The fuzziness exists in spatial distribution of geographic data of land suitability evaluation processes,which makes it difficult to quantify land boundaries by using traditional binary logic-based overlay model.Aiming at this limitation,an ecological suitability evaluation analysis model was presented based on fuzzy theory and a research on urban growth boundary(UGB) of the Great-Hexi Leading District(GHLD) of Changsha was conducted.With the support of GIS,RS and MATLAB,slope,elevation,vegetation,soil productivity,soil permeability,water body and land use are selected as the input of model according to the characteristic properties of soil and terrain in red soil hilly areas.The running result of this model indicates that the ratios of highly suitable land,suitable land,moderately suitable land and unsuitable land in GHLD are 18.75%,10.31%,64.16%,6.78%,respectively.This result accords with spatial structure worked out by Space Development Strategy Planning of GHLD,Based on this result,several suggestions are made to guide UGB developments in future.

Landslides susceptibility mapping in Guizhou province based on fuzzy theory

The purpose of this study was to assess the susceptibility of landslides around the area of Guizhou province based on fuzzy theory.In first instance, slope, elevation, lithology, proximity to tectonic lines, proximity to drainage and annual precipitation were taken as independent, causal factors in this study.A landslide hazard evaluation factor system was established by classifying these factors into more subclasses according to some rules.Secondly, a trapezoidal fuzzy number weighting(TFNW) approach was used to assess the importance of six causal factors to landslides in an ArcGIS environment.Thirdly, a landslide susceptibility map was created based on a weighted linear combination model.According to this susceptibility map, the study area was classified into four categories of landslide susceptibility:low, moderate, high and very high.Finally, in order to verify the results obtained, the susceptibility map and the landslide inventory map were combined in the GIS.In addition, the weighting procedure showed that TFNW is an efficient method for weighting causal landslide factors.

Improved de-interleaving algorithm of radar pulses based on dual fuzzy vigilance ART

As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory(fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART(DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating;then, the fuzzy vigilance models(FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region;finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy(quantization error dropped60%) and the de-interleaving performance(clustering quality increased by 10%) while suppressing the excessive proliferation of categories.

Using Fuzzy Theory and Variable Weights for Water Quality Evaluation in Poyang Lake, China

Achieving water purity in Poyang Lake has become a major concern in recent years, thus appropriate evaluation of spatial and temporal water quality variations has become essential. Variations in 11 water quality parameters from 15 sampling sites in Poyang Lake were investigated from 2009 to 2012. An integrative fuzzy variable evaluation(IFVE) model based on fuzzy theory and variable weights was developed to measure variations in water quality. Results showed that: 1) only chlorophyll-a concentration and Secchi depth differed significantly among the 15 sampling sites(P < 0.01), whereas the 11 water quality parameters under investigation differed significantly throughout the seasons(P < 0.01). The annual variations of all water quality variables except for temperature, electrical conductivity, suspended solids and total phosphorus were considerable(P < 0.05). 2) The IFVE model was reasonable and flexible in evaluating water quality status and any possible ′bucket effect′. The model fully considered the influences of extremely poor indices on overall water quality. 3) A spatial analysis indicated that anthropogenic activities(particularly industrial sewage and dredging) and lake bed topography might directly affect water quality in Poyang Lake. Meanwhile, hydrological status and sewage discharged into the lake might be responsible for seasonal water quality variations.

Fuzzy Model for Trust Evaluation

Based on fuzzy set theory, a fuzzy trust model is established by using membership function to describe the fuzziness of trust. The trust vectors of subjective trust are obtained based on a mathematical model of fuzzy synthetic evaluation. Considering the complicated and changeable relationships between various subjects, the multi-level mathematical model of fuzzy synthetic evaluation is introduced. An example of a two-level fuzzy synthetic evaluation model confirms the feasibility of the multi-level fuzzy synthesis evaluation model. The proposed fuzzy model for trust evaluation may provide a promising method for research of trust model in open networks.

Effects analysis on catalytic combustion characteristic of hydrogen/air in micro turbine engine by fuzzy grey relation method

In order to enhance catalytic combustion efficiency, a premixed hydrogen /air combustion model of the micro turbine engine is established under different excess air ratio, inlet velocity and heat transfer coefficient. And effects of inlet velocity, excess air coefficient and heat transfer coefficient on the catalytic combustion efficiency of the hydrogen have been analyzed by the FLUENT with CHEMKIN reaction mechanisms and the fuzzy grey relation theory. It is showed that inlet velocity has a more intuitive influence on the catalytic combustion efficiency of the hydrogen. A higher efficiency can be obtained with a lower inlet velocity. The optimum excess air coefficient is in the range of 0.94 to 1.0, the catalytic combustion efficiency of the hydrogen will be declined if the excess air coefficient exceeded 1.0. The effect of heat transfer coefficient on the catalytic combustion efficiency of the hydrogen mainly embodies in the case of the excess air coefficient exceeded 1.0, however, the effect will be declined if the heat transfer coefficient exceeded 4.0. The fuzzy grey relation degrees of the inlet velocity, heat transfer coefficient and excess air coefficient on the catalytic combustion efficiency of the hydrogen are 0.640945, 0.633214 and 0.547892 respectively.

Designing and Optimization of Fuzzy Sliding Mode Controller for Nonlinear Systems

For enhancing the control effectiveness,we firstly design a fuzzy logic based sliding mode controller(FSMC)for nonlinear crane systems.On basis of overhead crane dynamic characteristic,the sliding mode function with regard to trolley position and payload angle.Additionally,in order to eliminate the chattering problem of sliding mode control,the fuzzy logic theory is adopted to soften the control performance.Moreover,aiming at the FSMC parameter setting problem,a DE algorithm based optimization scheme is proposed for enhancing the control performance.Finally,by implementing the computer simulation,the DE based FSMC can effectively tackle the overhead crane sway problem and avoid unexpected accident greatly.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Fuzzy Nash equilibrium of fuzzy n-person non-cooperative game

The fuzzy non-cooperative game with fuzzy payoff function is studied. Based on fuzzy set theory with game theory, the fuzzy Nash equilibrium of fuzzy non-cooperative games is proposed. Most of researchers rank fuzzy number by its center of gravity or by the real number with its maximal membership. By reducing fuzzy number into a real number, we lose much fuzzy information that should be kept during the operations between fuzzy numbers. The fuzzy quantities or alternatives are ordered directly by Yuan＇s binary fuzzy ordering relation. In doing so, the existence of fuzzy Nash equilibrium for fuzzy non-cooperative games is shown based on the utility function and the crisp Nash theorem. Finally, an illustrative example in traffic flow patterns of equilibrium is given in order to show the detailed calculation process of fuzzy Nash equilibrium.

Modelling spatial vagueness based on type-2 fuzzy set

The modelling and formal characterization of spatial vagueness plays an increasingly important role in the imple- mentation of Geographic Information System (GIS). The concepts involved in spatial objects of GIS have been investigated and acknowledged as being vague and ambiguous. Models and methods which describe and handle fuzzy or vague (rather than crisp or determinate) spatial objects, will be more necessary in GIS. This paper proposes a new method for modelling spatial vagueness based on type-2 fuzzy set, which is distinguished from the traditional type-1 fuzzy methods and more suitable for describing and implementing the vague concepts and objects in GIS.

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

Composition ship collision risk based on fuzzy theory

Despite of modern navigation devices, there are problems in navigation of vessels in waterways due to the geographical structures, disturbances in water, dynamic nature, and heavily environmental influenced sea traffic. Even though all vessels are equipped with modern navigation devices, the accidents are reported caused by various reasons and mainly by human factor according to investigation. We propose an effective and efficient composition collision risk calculation method for finding the collision probability and avoiding the collision between ships in possible collision situations. The proposed composition collision risk calculation method at ship's position using combination of fuzzy and fuzzy comprehensive evaluation methods. The algorithm is straightforward to implement and is shown to be effective in automatic ship handling for ships involved in complex navigation situations. Experiments are carried out with indigenous data and the results show the effectiveness of the proposed approach.

Spherical Fuzzy WASPAS-based Entropy Objective Weighting for International Payment Method Selection

In international trade,exporters prefer to receive payments as quickly as possible,and importers want to make payments as late as possible.In this respect,the payment field,an essential condition for trade transactions,also represents the positions of exporters and importers conflict.In addition,there are many cases in which various variables must be considered rather than only one specific variable representatively affecting payment,particularly in the case of import-export Small and Medium-Sized Enterprises(SMEs)from emerging countries.A selection of proper payment methods can be categorized as a Multi-Criteria Decision-Making(MCDM)issue.Therefore,this study aims to propose a novel and efficient Spherical Fuzzy Weighted Aggregated Sum Product Assessment based Entropy Objective Weighting method(SF-EW and WASPAS-SF)to evaluate international payment methods with uncertain information.First,SF-EW model is applied to determine the relative weights of critical factors.Second,international payment method alternatives are prioritized by the WASPAS-SF approach.Five essential factors for four international payment methods are proposed based on experts’opinions and the existing literature.A real-world case study from Vietnamese import-export SMEs is presented to validate the applicability of the proposed framework.The results indicated that“Characteristics of payment method(PA)”had the most significant impact on international payment method selection.In comparison,“Letters of Credit(L/C)”was the most reliable payment method with the highest ranking available to international traders.Furthermore,a sensitivity analysis was performed to examine the validity and robustness of the proposed decision support model.Consequently,this study could contribute to international payment services and in the context of globalization and international trade.