Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Reactive fuzzy controller design by Q-learning for mobile robot navigation

In this paper a learning mechanism for reactive fuzzy controller design of a mobile robot navigating in unknown environments is proposed. The fuzzy logical controller is constructed based on the kinematics model of a real robot. The approach to learning the fuzzy rule base by relatively simple and less computational Q-learning is described in detail. After analyzing the credit assignment problem caused by the rules collision, a remedy is presented. Furthermore, time-varying parameters are used to increase the learning speed. Simulation results prove the mechanism can learn fuzzy navigation rules successfully only using scalar reinforcement signal and the rule base learned is proved to be correct and feasible on real robot platforms.

Floyd-Warshall Algorithm Based on Picture Fuzzy Information

The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of nodes.It works well for crisp weights,but the problem arises when weights are vague and uncertain.Let us take an example of computer networks,where the chosen path might no longer be appropriate due to rapid changes in network conditions.The optimal path from among all possible courses is chosen in computer networks based on a variety of parameters.In this paper,we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path(APSP)in an uncertain situation of a network.In the proposed methodology,multiple criteria and theirmutual associationmay involve the selection of any suitable path between any two node points,and the values of these criteria may change due to an uncertain environment.We use trapezoidal picture fuzzy addition,score,and accuracy functions to find APSP.We compute the time complexity of this algorithm and contrast it with the traditional Floyd-Warshall algorithm and fuzzy Floyd-Warshall algorithm.

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.

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

A Genetic Based Fuzzy Q-Learning Flow Controller for High-Speed Networks

For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.

矿井带式输送机液压拉紧Fuzzy-PID控制技术研究

针对矿井传统带式输送机拉紧系统响应速度慢、调节能力差、拉紧控制时变性和非线性等问题,提出了一种矿井带式输送机液压拉紧系统Fuzzy-PID控制(基于模糊算法的PID控制)方法。首先,根据液压拉紧装置建立数学模型,其次通过Matlab内置的Simulink仿真库分别对Fuzzy-PID控制器和PID控制器的液压拉紧系统进行仿真,得出输送带拉紧张力启动响应阶段和张力突变的调节响应图,并做出对比分析。最后,通过试验测试来验证算法模型的有效性。仿真结果表明:矿井带式输送机液压拉紧Fuzzy-PID控制系统不仅在启动阶段还有张力突变过程中都具有更好的稳态性能、更快的响应速度。在拉紧装置启动响应阶段的张力超调量降低了13.5%、到达期望值的时间缩短了0.5 s。在拉紧装置张力突变即模拟拉紧和松带阶段,当张力增加时,Fuzzy-PID控制器的调节速度缩短了0.4 s,超调量下降了4%。当张力减少时,Fuzzy-PID控制器的调节速度缩短了0.3 s,超调量降低了2%。试验结果表明:采用Fuzzy-PID控制的效果更佳优异稳定,且损耗更小。对比于PID控制,Fuzzy-PID控制效果更为良好,平均时间缩短31%且总体趋于稳定。对于矿井带式输送机这种连续运输作业的设备,Fuzzy-PID控制技术为矿井带式输送带平稳运行提供了一定保障,不仅减少了电能浪费,也降低了维护保养带式输送机的保养成本。

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

ADAPTIVE GENETIC ALGORITHM BASED ON SIX FUZZY LOGIC CONTROLLERS

The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.

Multisensor Fuzzy Stochastic Fusion Based on Genetic Algorithms

To establish a parallel fusion approach of processing high dimensional information, the model and criterion of multisensor fuzzy stochastic data fusion were presented. In order to design genetic algorithm fusion, the fusion parameter coding, initial population and fitness function establishing, and fuzzy logic controller designing for genetic operations and probability choosing were completed. The discussion on the highly dimensional fusion was given. For a moving target with the division of 1 64 (velocity) and 1 75 (acceleration), the precision of fusion is 0 94 and 0 98 respectively. The fusion approach can improve the reliability and decision precision effectively.

Fuzzy traffic signal control with DNA evolutionary algorithm

In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method.

FUZZY GENETIC HYBRID ALGORITHM FOR MARKET TIMING PREDICTION OF STOCK INVESTMENT

Market timing prediction of stock investment is an important decision problem with uncertainty and risk in the financial activity.An algorithm for market timing prediction of stock investment is proposed in this paper.Considering the close relationship in the stock market and the economic data,we find the correlation of synthetical economic data and the equity returns with the help of the combination of fuzzy logic and genetic algorithm.Finally,the application of stock market is included to test the effectiveness of the algorithm.

Discrete Variable Structural Optimization based on Multidirectional Fuzzy Genetic Algorithm

Round method is the common method for discrete variable optimization in optimal design of complex mechanical structures;however,it has some disadvantages such as poor precision,simple model and lacking of working conditions＇ description,etc.To solve these problems,a new model is constructed by defining parameterized fuzzy entropy,and the rationality of parameterized fuzzy entropy is verified.And a new multidirectional searching algorithm is further put forward,which takes information of actual working conditions into consideration and has a powerful local searching capability.Then this new algorithm is combined with the GA by the fuzzy clustering algorithm（FCA）.With the application of FCA,the optimal solution can be effectively filtered so as to retain the diversity and the elite of the optimal solution,and avoid the structural re-analysis phenomenon between the two algorithms.The structure design of a high pressure bypass-valve body is used as an example to make a structural optimization by the proposed HGA and finite element method（FEM）,respectively.The comparison result shows that the improved HGA fully considers the characteristic of discrete variable and information of working conditions,and is more suitable to the optimal problems with complex working conditions.Meanwhile,the research provides a new approach for discrete variable structure optimization problems.

Intuitionistic fuzzy C-means clustering algorithms

Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.

FUZZY GLOBAL SLIDING MODE CONTROL BASED ON GENETIC ALGORITHM AND ITS APPLICATION FOR FLIGHT SIMULATOR SERVO SYSTEM

To alleviate the chattering problem, a new type of fuzzy global sliding mode controller （FGSMC） is presented. In this controller, the switching gain is estimated by fuzzy logic system based on the reachable conditions of sliding mode controller（SMC）, and genetic algorithm （GA） is used to optimize scaling factor of the switching gain, thus the switch chattering of SMC can be alleviated. Moreover, global sliding mode is realized by designing an exponential dynamic sliding surface. Simulation and real-time application for flight simulator servo system with Lugre friction are given to indicate that the proposed controller can guarantee high robust performance all the time and can alleviate chattering phenomenon effectively.

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.