Values Of Fuzzy Membership Functions Research Articles

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust value of each node is estimated by using each node’s reputation values which are determined based on the fuzzy membership function values and utilizing QoS parameters such as residual energy, bandwidth, node mobility, and reliability. This mechanism prevents only the black hole attack in the network during transmission. But, the gray hole attacks are not identified which in turn increases the packet drop rate significantly. Hence, the gray hole attack is also detected based on the Kullback-Leibler (KL) divergence method used for estimating the statistical measures. Additional QoS metrics are considered to prevent the gray hole attack, such as packet loss, packet delivery ratio, and delay for each node. Thus, the proposed mechanism prevents both black hole and gray hole attacks simultaneously. Finally, the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

Construction and Application of Groundwater Impact Analysis Model Based on Random Matrix Model

Traditional groundwater models do not consider the influence of random factors, so their results of simulation and prediction cannot reflect the actual dynamics of ongoing groundwater changes. By comparing the statistical properties of random multidimensional time series, the random matrix theory can reflect the degree of deviation from randomness in actual data and reveal the behavioral characteristics of the overall correlation in actual data. On the basis of summarizing and analyzing the previous literature, this study expounded the research status and significance of groundwater impact analysis models, elaborated on the development background, current status, and future challenges of the random matrix model, introduced the methods and principles of the finite isometric properties and observation feature dimension, conducted the spectral analysis of the random matrix of groundwater impact analysis data, analyzed the eigenvalue and the eigenvector distribution of the random matrix of the groundwater impact analysis data, constructed a groundwater impact analysis model based on the random matrix model, designed the hydrodynamic model for the groundwater impact analysis, discussed the parameter variation mechanism of the groundwater impact analysis, and finally carried out a case application and its result analysis. The study results show that in the membership calculation formula of the random matrix model, it is found that there is an inverse relationship between the expected value of the target index and its membership value. According to the initial fuzzy membership function value, the random matrix model uses the normalization formula and evaluation criteria of the catastrophe model to carry out a comprehensive quantitative recursive operation and finally determines the concept, characteristics, influencing factors, evaluation content, purpose, and scope of the groundwater impact analysis model. The random matrix model can not only reflect the spatial and temporal differences of groundwater-influencing factors but also reflect the dynamic mechanism of aquifer or groundwater system or the inherent law of groundwater movement, so it is more applicable and convenient to evaluate groundwater resources.

An Evolutionary Neuro-Fuzzy C-means Clustering Technique

One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy C-means (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a Neuro-Fuzzy C-Means Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current state-of-the-art clustering methods to demonstrate the efficacy of the proposed algorithm.

Spatially Constrained Fuzzy c-Means Clustering Algorithm for Image Segmentation

The fuzzy c-means (FCM) clustering is an unsupervised clustering method, which has been widely used in image segmentation. In this paper, a spatially constrained fuzzy c-means clustering algorithm for image segmentation is proposed to overcome the sensitivity of the FCM clustering algorithm to noises and other imaging artifacts. Firstly, the local prior probabilities of pixel classification are defined according to the fuzzy membership function values of neighbouring pixels, and then those local prior probabilities are incorporated into the objective function of the standard FCM. Thus, the local spatial information embedded in the image is incorporated into the FCM algorithm. Experimental results on the synthetic and real images are given to demonstrate the robustness and validity of the proposed algorithm.

Design of a Hybrid ANFIS–PSO Model to Estimate Sediment Transport in Open Channels

In the present study, a new hybrid evolutionary method is presented for estimating the minimum densimetric Froude number required for sediment transport with no solid substance deposition in channel pipes. This method is based on a combination of particle swarm optimization (PSO) algorithm with adaptive neuro-fuzzy inference system (ANFIS). The PSO algorithm is utilized for optimization of fuzzy membership function value. Three different sets of data measured at different times as well as a vast range of parameters are used to test and train the suggested models. The results of ANFIS–PSO (R2 = 0.976, RMSE = 0.260, MAPE = 5.743, BIAS = − 0.004, SI = 0.059) are compared with the results of ANFIS (R2 = 0.929, RMSE = 0.452, MAPE = 9.107, BIAS = − 0.077, SI = 0.101) and regression-based equations obtained from the literature.

An accurate assessment tool based on intelligent technique for suitability of soybean cropland: case study in Kebumen Regency, Indonesia

In the last few years, the decrease in the land use of soybean affected the reduced of soybean production. Land suitability assessment is an effort to increase the soybean production since the manual method was less accurate. This study aims to apply fuzzy sets and AHP to improve the accuracy of the assessment process of land suitability for soybean crops. The value of five sub-criteria converted into fuzzy sets for standardization process. The weighting by AHP performed to determine the importance level of sub-criteria. Suitability index and final land suitability classes were obtained from the calculation of the fuzzy membership function values and the weights of each sub-criteria before being overlaid with spatial data to produce land suitability map for soybeans. The results of this work showed that 81.42% of the total area was moderate suitability (S2), 11.25% was marginally suitable (S3) and of 7.33% was not suitable (N). From the results of land suitability assessment for soybean crops has been tested and has good correlation with the yield conditions. This study showed that the proposed tool based on Fuzzy sets and AHP were accurate to assess the land suitability of soybean and can be used as the basis for agricultural planning to optimize the land use and soybean production.

The investigation into the influence of the features of furniture product design on consumers’ perceived value by fuzzy semantics

This study employed fuzzy theory to investigate the influence of the features of furniture product design on the fuzzy semantics of Taiwanese consumers’ perceived value in new product design and development in hopes of providing product designers or developers with a reference to new product design and development. Furniture designers or manufacturers can use u(x), the fuzzy membership function value of the model for investigating the fuzzy semantic relation between the features of furniture product design and consumers’ perceived value, to evaluate the fuzzy semantics of consumers’ perceived value, and they can also employ the fuzzy semantic mean equation, namely to transform the research data into fuzzy semantics. In this study, all the consumers agreed that the features of furniture product design were significantly correlated to the fuzzy semantics of consumers’ perceived value. In terms of the fuzzy semantics of the perceived value, the consumers with different genders, ages, education backgrounds, and incomes had different opinions about the features of furniture product design, and there were significant differences between the variables. In short, this study is helpful to understand consumers’ demand for products as well as the development of consumer-oriented products, enhance their purchase intention, and improve corporate performance and market competitiveness.

Adaptive control of discrete-time state-space T–S fuzzy systems with general relative degree

This paper develops a new solution framework for adaptive control of general discrete-time single-input single-output (SISO) state-space Takagi–Sugeno (T–S) fuzzy systems with a relative degree ρ (1≤ρ≤n). A new procedure is proposed to construct a normal form of a global T–S fuzzy system model from local state-space models in non-canonical form, and such a normal form system has an explicit relative degree structure and a specific input–output signal causality relationship in the sense that it does not include any future values of fuzzy membership functions. An adaptive feedback control scheme is designed based on the global normal form T–S fuzzy model, to ensure desired closed-loop stability and output tracking properties. A comparison is given to adaptive state tracking designs seen in the literature, which require much more restrictive matching conditions and do not take into account the high relative degree cases. As an illustrative example, a T–S fuzzy system is constructed based on the linearized local models of a transport airplane. Simulation results have demonstrated the developed new concepts and verified the desired performance of the new type of adaptive fuzzy control systems.

Optimization of multiple input–output fuzzy membership functions using clonal selection algorithm

A clonal selection algorithm (CLONALG) inspires from clonal selection principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed CLONALG program for a multiple input–output (MI–O) fuzzy system. In this study, GA and binary particle swarm optimization (BPSO) are used for implementing the proposed method as well and they are compared. It has been shown that using clonal selection algorithm is advantageous for finding optimum values of fuzzy membership functions

Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran

The northwestern Fars province of Iran is prone to landslides. In order to help the planners in the selection of suitable locations to implement development projects, a landslide susceptibility map has been produced for the Kakan catchment area using the gamma aggregation operation of the fuzzy approach. Lithology, slope angle, slope aspect, land cover, weathering depth, proximity to roads, topographical elevation, and soil depth were considered as landslide causal factors for the study area. Three values for gamma were examined and output maps were evaluated using the known landslides. Performing fuzzy gamma operation with 0.94 for gamma and classifying the area yielded an output susceptibility map with four zones: non‐susceptible, moderate susceptibility, high susceptibility, and very high susceptibility. Gamma values <0.94 decreased the fuzzy membership functions to low susceptibility zones, and eliminated the high and very high classes. Gamma values >0.94 moved the output fuzzy membership function values to high and very high landslide susceptibilities and eliminated the moderate susceptible zones. Comparing the known landslides of the area with the output susceptibility map showed that 93.9% of the identified landslides were located in the high susceptible zone and 6.1% in very high susceptible zone.