Tracking maneuvering target based on neural fuzzy network with incremental neural leaning

The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning （INL-SONFIN） can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids misstracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.

H_∞Control of Uncertain Fuzzy Networked Control Systems with State Quantization

The problem of robust H∞ control for uncertain discrete-time Takagi and Sugeno (T-S) fuzzy networked control systems (NCSs) is investigated in this paper subject to state quantization. By taking into consideration network induced delays and packet dropouts, an improved model of network-based control is developed. A less conservative delay-dependent stability condition for the closed NCSs is derived by employing a fuzzy Lyapunov-Krasovskii functional. Robust H∞ fuzzy controller is constructed that guarantee asymptotic stabilization of the NCSs and expressed in LMI-based conditions. A numerical example illustrates the effectiveness of the developed technique.

Simulation Modeling by Fuzzy Nets

Fuzzy technology is a newly developed discipline based on fuzzy mathematics. In the recent years, it has been successfully applied into many areas, such as process control, diagnosis, evaluation, decision making and scheduling, especially in simulation where accurate mathematical models can not or very hard be established. In this paper, to meet the demands of fuzzy simulation, two fuzzy nets will first be presented, which are quite suitable for modeling the parallel or concurrent systems with fuzzy behavior. Then, a concept of active simulation will be introduced, in which the simulation model not only can show its fuzzy behavior, but also has a certain ability which can actively perform many very useful actions, such as automatic warning, realtime monitoring, simulation result checking, simulation model self-adapting, error recovery, simulating path tracing, system states inspecting and exception handling, by a unified approach while some specified events occur. The simulation model described by this powerful simulation modeling tool is concurrently driven by a network interpreter and an event monitor that all can be implemented by software or hardware. Besides, some interesting applications are given in the paper.

Neural Fuzzy Networks in Computer Aided Process Planning

The Neural Fuuzy Network(NFN) has been utilized to more adequately capture and reuse the knowledge and experience of the process planner. Previous process plans are made use of to construct the initial 5 layered NFN. The NFN has the sigmoid function in the the fuzzification and output layers, the Product combines the conditions to a rule, and Summation integrates the fired rules. A Backward Propagation(BP) training algorithm has been developed to fine tune the network. The system trains and chooses manufacturing operations correctly.

A Novel Approach for Finding a Shortest Path in a Mixed Fuzzy Network

We present a novel approach for computing a shortest path in a mixed fuzzy network, network having various fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using -cuts. Then, we present a dynamic programming method for finding a shortest path in the network. For this, we apply a recently proposed distance function for comparison of fuzzy numbers. Four examples are worked out to illustrate the applicability of the proposed approach as compared to two other methods in the literature as well as demonstrate the novel feature offered by our algorithm to find a fuzzy shortest path in mixed fuzzy networks with various settings for the fuzzy arc lengths.

Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under uncertainty.The activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are probabilistic.This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)environment.The LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project network.The primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so on.The LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal decision.We also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity timings.When the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been discussed.The suggested techniques are demonstrated with two suitable numerical examples.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

Whole process prediction model of silicon steel strip on transverse thickness difference based on Takagi-Sugeno fuzzy network

The hot rolling and cold rolling control models of silicon steel strip were examined.Shape control of silicon steel strip of hot rolling was a theoretical analysis model,and the shape control of cold rolling was a data-based prediction model.The mathematical model of the hot-rolled silicon steel section,including the crown genetic model,inter-stand crown recovery model,and hot-rolled strip section prediction model,is used to control the shape of hot-rolled strip.The cold rolling shape control is mainly based on Takagi-Sugeno fuzzy network,which is used to simulate and predict the transverse thickness difference of cold-rolled silicon steel strip.Finally,a predictive model for the transverse thickness difference of silicon steel strips is developed to provide a new quality control method of transverse thickness of combined hot and cold rolling to improve the strip profile quality and increase economic efficiency.The qualified rate of the non-oriented silicon steel strip is finally obtained by applying this model,and it has been steadily upgraded to meet the needs of product quality and flexible production.

Jaya Honey Badger optimization- based deep neuro-fuzzy network structure for detection of (SARS- CoV) Covid-19 disease by using respiratory sound signals

Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

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.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

A Wireless Sensor Network(WSN)becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable.Concerning the energy produc-tion of the nodes,WSN has major issues that may influence the stability of the system.As a result,constructing WSN requires devising protocols and standards that make the most use of constrained capacity,especially the energy resources.WSN faces some issues with increased power utilization and an on going devel-opment due to the uneven energy usage between the nodes.Clustering has proven to be a more effective strategy in this series.In the proposed work,a hybrid meth-od is used for reducing the energy consumption among CHs.A Fuzzy Logic-based clustering protocol FLUC(unequally clustered)and Fuzzy Clustering with Energy-Efficient Routing Protocol(FCERP)are used.A Fuzzy Clustering with Energy Efficient Routing Protocol(FCERP)reduces the WSN power usage and increases the lifespan of the network.FCERP has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing capabilities.The technique creates uneven groups by using fuz-zy logic with a competitive range to choose the Cluster Head(CH).The input variables include the distance of the nodes from the ground station,concentra-tions,and remaining energy.The proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.

Fuzzy Logic Based Handover Authentication in 5g Telecommunication Heterogeneous Networks

Under various deployment circumstances,fifth-generation(5G)telecommunications delivers improved network compound management with fast communication channels.Due to the introduction of the Internet of Things(IoT)in data management,the majority of the ultra-dense network models in 5G networks frequently have decreased spectral efficiency,weak handover management,and vulnerabilities.The majority of traditional handover authentication models are seriously threatened,making them vulnerable to a variety of security attacks.The authentication of networked devices is the most important issue.Therefore,a model that incorporates the handover mechanism and authentication model must be created.This article uses a fuzzy logic model to create a handover and key management system that focuses on cloud handover management and authentication performance.In order to decrease delays in 5G networks,the fuzzy logic is built with multiple criteria that aim to reduce the number of executed handovers and target cell selection.The simulation is run to evaluate the model’s performance in terms of latency,spatial complexity,and other metrics related to authentication attack validation.

Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

Some types of software systems,like event‐based and non‐deterministic ones,are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules.However,when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables,they constitute a complex form that is difficult to understand and infer.A solution is to visualise the system specification with the capability of automatic rule inference.In this study,by representing a high‐level system specification,the authors visualise rule representation and firing using fuzzy coloured Petri‐nets.Already,several fuzzy Petri‐nets‐based methods have been presented,but they either do not support a large number of rules and variables or do not consider significant cases like(a)the weight of the premise's propositions in the occurrence of the rule conclusion,(b)the weight of conclusion's proposition,(c)threshold values for premise and conclusion's propositions of the rule,and(d)the certainty factor(CF)for the rule or the conclusion's proposition.By considering cases(a)-(d),a wider variety of fuzzy rules are supported.The authors applied their model to the analysis of attacks against a part of a real secure water treatment system.In another real experiment,the authors applied the model to the two scenarios from their previous work and analysed the results.

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing 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 deter-mined based on the fuzzy membership function values and utilizing QoS para-meters such as residual energy,bandwidth,node mobility,and reliability.This mechanism prevents only the black hole attack in the network during transmis-sion.But,the gray hole attacks are not identified which in turn increases the pack-et drop rate significantly.Hence,the gray hole attack is also detected based on the Kullback-Leibler(KL)divergence method used for estimating the statistical mea-sures.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.Final-ly,the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves,when the destination and source nodes are not in range of coverage.Because of its wireless type,it has lot of security concerns than an infrastructure networks.Wormhole attacks are one of the most serious security vulnerabilities in the network layers.It is simple to launch,even if there is no prior network experience.Signatures are the sole thing that preventive measures rely on.Intrusion detection systems(IDS)and other reactive measures detect all types of threats.The majority of IDS employ features from various network layers.One issue is calculating a huge layered features set from an ad-hoc network.This research implements genetic algorithm(GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks.For attack detection,the reduced feature set was put to a fuzzy logic system(FLS).The performance of proposed model was compared with principal component analysis(PCA)and statistical parametric mapping(SPM).Network performance analysis like delay,packet dropping ratio,normalized overhead,packet delivery ratio,average energy consumption,throughput,and control overhead are evaluated and the IDS performance parameters like detection ratio,accuracy,and false alarm rate are evaluated for validation of the proposed model.The proposed model achieves 95.5%in detection ratio with 96.8%accuracy and produces very less false alarm rate(FAR)of 14%when compared with existing techniques.

A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

The existing systems sustained with the investments made require more automation and digital transformation with the continuous advancement of tech-nology.The aviation industry is a sector that is open to more automation and digi-tal transformation,mainly because of the intense competition and the analysis of a large variety of data.The long duration of operations in current airline processes and some processflows cause customer dissatisfaction and cost increase.In this study,the boarding process,which is one of the operational processes of airline transportation and is open to improvement,was discussed.The classical boarding process has been redesigned using Internet of Things technology a model called Boarding 4.0 was created.With Boarding 4.0,it is aimed to design a process where passengers can take their time before boarding more efficiently.In the study,the sub-processes of the Boarding 4.0 model,other processes that the sub-processes interact with,their activities,and data exchange passenger move-ments during the activities are explained in detail.Compared to the classical boarding process and Boarding 4.0 with the fuzzy ahp technique,it has been shown that boarding 4.0 is more advantageous and passenger movement times can be reduced during boarding.As a result of the evaluation made with the fuzzy ahp,it was determined that boarding 4.0 is more advantageous than the classical boarding process.In addition,when the total time of the sub-activities in the board-ing process is calculated,boarding activities for a passenger take 50 min with the classic boarding process and 20 min with Boarding 4.0.Thus,when Boarding 4.0 is used,the passenger gains 30 min.Furthermore,when the calculation is made concerning the airport’s current capacity,two passengers are hosted with the clas-sical boarding process,whilefive passengers are hosted with Boarding 4.0.This acquisition is significant for airports in terms of efficient use of resources.