Report for Type 2 Bayes-Fuzzy Estimation in No-Data Problem

It is well known that the system (1 + 1) can be unequal to 2, because this system has both observation error and system error. Furthermore, we must provide our mustered service within our cool head and warm heart, where two states of nature are existing upon us. Any system is regarded as the two-dimensional variable error model. On the other hand, we consider that the fuzziness is existing in this system. Though we can usually obtain the fuzzy number from the possibility theory, it is not fuzzy but possibility, because the possibility function is as same as the likelihood function, and we can obtain the possibility measure by the maximal likelihood method (i.e. max product method proposed by Dr. Hideo Tanaka). Therefore, Fuzzy is regarded as the only one case according to Vague, which has both some state of nature in this world and another state of nature in the other world. Here, we can consider that Type 1 Vague Event in other world can be obtained by mapping and translating from Type 1 fuzzy Event in this world. We named this estimation as Type 1 Bayes-Fuzzy Estimation. When the Vague Events were abnormal (ex. under War), we need to consider that another world could exist around other world. In this case, we call it Type 2 Bayes-Fuzzy Estimation. Where Hori et al. constructed the stochastic different equation upon Type 1 Vague Events, along with the general following probabilistic introduction method from the single regression model, multi-regression model, AR model, Markov (decision) process, to the stochastic different equation. Furthermore, we showed that the system theory approach is Possibility Markov Process, and that the making decision approach is Sequential Bayes Estimation, too. After all, Type 1 Bays-Fuzzy estimation is the special case in Bayes estimation, because the pareto solutions can exist in two stochastic different equations upon Type 2 Vague Events, after we ignore one equation each other (note that this is Type 1 case), we can obtain both its system solution and its decision solution. Here, it is noted that Type 2 Vague estimation can be applied to the shallow abnormal decision problem with possibility reserved judgement. However, it is very important problem that we can have no idea for possibility reserved judgement under the deepest abnormal envelopment (ex. under War). Expect for this deepest abnormal decision problem, Bayes estimation can completely cover fuzzy estimation. In this paper, we explain our flowing study and further research object forward to this deepest abnormal decision problem.

Intention Estimation of Adversarial Spatial Target Based on Fuzzy Inference

Estimating the intention of space objects plays an important role in air-craft design,aviation safety,military and otherfields,and is an important refer-ence basis for air situation analysis and command decision-making.This paper studies an intention estimation method based on fuzzy theory,combining prob-ability to calculate the intention between two objects.This method takes a space object as the origin of coordinates,observes the target’s distance,speed,relative heading angle,altitude difference,steering trend and etc.,then introduces the spe-cific calculation methods of these parameters.Through calculation,values are input into the fuzzy inference model,andfinally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability.Ver-ified by simulation experiment,the target intention inferred by this method is roughly the same as the actual behavior of the target,which proves that the meth-od for identifying the target intention is effective.

基于BP神经网络的Smith-Fuzzy-PID算法在阀门定位中的应用研究

为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。

Pricing European Call Currency Option Based on Fuzzy Estimators

In this paper we present an application of fuzzy estimators method to price European call currency option. We make use of fuzzy estimators for the volatility of exchange rate which based on statistical data to obtain the fuzzy pattern of G-K model. A numerical example is presented to get the -level closed intervals of the European call currency option fuzzy price.

SAR Images Despeckling Based on Bayesian Estimation and Fuzzy Shrinkage in Wavelet Domains

An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance is modeled with a mixture density of two zero-mean Gaussian distributions. A fuzzy shrinkage factor is derived based on the minimum mean square error （MMSE） criteria with Bayesian estimation. In the case above, the ideas of region division and fuzzy shrinkage arc adopted according to the interscale dependencies among wavelet coefficients. The noise-free wavelet coefficients are estimated accurately. Experimental results show that the algorithm proposed is superior to the refined Lee filter, wavelet soft thresbolding shrinkage and SWT shrinkage algorithms in terms of smoothing effects and edges preservation.

Fuzzy Sliding Mode Control Based on Longitudinal Force Estimation for Electro-mechanical Braking Systems using BLDC Motor

This paper focuses on the controller design using fuzzy sliding mode control(FSMC)with application to electro-mechanical brake(EMB)systems using BLDC Motor.The EMB controller transmits the control signal to the motor driver to rotate the motor.The torque distribution of motors is studied in this paper actually.Firstly,the model of the EMB system is established.Then the state observer is developed to estimate the vehicle states including the vehicle velocity and longitudinal force.Due to the fact that the EMB system is nonlinear and uncertain,a FSMC strategy based on wheel slip ratio is proposed,where both the normal and emergency braking conditions are taken into account.The equivalent control law of sliding mode controller is designed on the basis of the variation of the front axle and rear axle load during the brake process,while the switching control law is adjusted by the fuzzy corrector.The simulation results illustrate that the FSMC strategy has the superior performance,better adaptability to various types of roads,and shorter braking distance,as compared to PID control and traditional sliding mode control technologies.Finally,the hardware-in-loop(HIL)experimental results have exemplified the validation of the developed methodology.

State Estimation for Fuzzy Sound Environment System with Finite Amplitude Fluctuation

Internal physical mechanism of actual sound environment system is often difficult to recognize analytically, and it contains unknown structural characteristics. Furthermore, the observation data often contain fuzziness due to several causes and exhibit level saturation owing to the existence of a finite dynamic range. Therefore, it is necessary to propose a new state estimation method by considering fuzziness and finite amplitude fluctuation of observation data. In this paper, a method for estimating the specific signal for sound environment system with unknown structure is proposed in an appropriate form for the finite level range of the measured fuzzy observation data by introducing an expansion expression of probability distribution with Bata distribution in the first term and new type of membership function. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem in the sound environment.

A State Estimation Method for Sound Environment System with Unknown Observation Mechanism by Introducing Fuzzy Inference

The observed phenomena in real sound environment system often contain uncertainty such as the additional external noise with unknown statistics. Furthermore, there is complex nonlinear relationship between the specific signal and the observations, and it cannot be exactly expressed in any definite functional form. In these situations, it is one of reasonable analysis methods to treat the objective sound environment system as a fuzzy system. In this study, a state estimation method for a specific signal under the existence of an unknown observation mechanism and external noise of unknown statistics is proposed by introducing fuzzy inference. The effectiveness of the proposed theoretical method is experimentally confirmed by applying it to the actually observed data in the sound environment.

Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

By constructing a mcan-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts： a real-valued non-random recurrence equation and the standard Kalman filtering.

Reliability Estimation of Services Oriented Systems Using Adaptive Neuro Fuzzy Inference System

In order to make system reliable, it should inhibit guarantee for basic service, data flow, composition of services, and the complete workflow. In service-oriented architecture (SOA), the entire software system consists of an interacting group of autonomous services. Some soft computing approaches have been developed for estimating the reliability of service oriented systems (SOSs). Still much more research is expected to estimate reliability in a better way. In this paper, we proposed SoS reliability based on an adaptive neuro fuzzy inference system (ANFIS) approach. We estimated the reliability based on some defined parameter. Moreover, we compared its performance with a plain FIS (fuzzy inference system) for similar data sets and found the proposed approach gives better reliability estimation.

A Noise Suppression Method for Speech Signal by Jointly Using Bayesian Estimation and Fuzzy Theory

Speech recognition systems have been applied to inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult. In these actual circumstances, some countermeasure methods for surrounding noise are indispensable. In this study, a new method to remove the noise for actual speech signal was proposed by using Bayesian estimation with the aid of bone-conducted speech and fuzzy theory. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal was theoretically derived. In the proposed noise suppression method, bone-conducted speech signal with the reduced high-frequency components was regarded as fuzzy observation data, and a stochastic model for the bone-conducted speech was derived by applying the probability measure of fuzzy events. The proposed method was applied to speech signals measured in real environment with low SNR, and better results were obtained than an algorithm based on observation of only air-conducted speech.

A TSK Fuzzy Approach to Channel Estimation for OFDM Systems

This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which is updated by the pilot symbols. The proposed approach can trace the variation of channel and it is computationally simple. Its performance is tested via simulations. Results show that it is comparable to that of ideal Minimum Mean-Square-Error （MMSE） method, especially at the low Signal to Noise Ratio （SNR）.

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.

2013年和2022年芦山地震序列断层面花状构造及其滑动特性研究

芦山地区在2013年和2022年发生两次6.0级以上地震,本研究收集两次地震的余震序列,研究两次芦山地震发震断层的几何参数和滑动特性,并分析汶川地震和2013年芦山地震对2022年芦山地震的应力影响.得到以下结论:(1)基于收集到的两次芦山地震序列的2531个地震事件的精确定位结果,采用模糊聚类方法,求解得到芦山地震序列的5个发震断层面位置及其走向和倾角,5个断层面在空间上呈花状构造分布.(2)基于收集和计算得到的该地区的127个震源机制中心解,求解得到该地区的构造应力场,得到芦山地区形成了SEE—NWW向挤压,垂直向拉张的以逆冲为主的应力状态.(3)通过将构造应力场投影到两次芦山地震发震断层上,得到这些发震断层面上均容易发生逆冲型破裂事件.(4)将求解的2022年芦山地震序列发震断层面形状和滑动特性作为接收断层参数,计算汶川地震和2013年芦山地震对2022年芦山地震序列的应力影响,结果显示汶川地震对2022年芦山地震序列发震断层面的活动性起到促进作用,而汶川地震和2013年芦山地震的共同作用,对2022年芦山地震序列发震断层面的活动性起到抑制作用,即2013年芦山地震的发生推迟了2022年芦山地震序列的发生.基于GPS观测的应变率得到,在汶川地震和2013年芦山地震的共同作用下,2022年芦山地震被推迟的时间约为5.2~10.5年.这些研究对研究龙门山推覆构造带南段的断层活动特性和地震危险性评估有一定意义.

高量测丢包率下基于共享模糊等价关系的配电网状态感知

配电网分布式新能源渗透率的不断增加,对实时监测的要求不断提高,大量量测数据在集中传输过程中常常出现延迟、丢包等现象导致配电网状态感知精度降低甚至不可观测。针对上述问题,提出基于共享模糊等价关系的状态感知方法。首先,将采集得到的多时间断面历史量测数据集作为源数据集,实时采集得到的高量测丢包率下的数据作为目标数据集。利用三角形隶属度函数分别将两组数据集写成模糊集的形式。然后采用一种向量间距离度量方法,分别计算得到两组模糊集的模糊等价关系矩阵。在此基础上,采用共享模糊等价关系的聚类方法,将知识从源数据集转移到目标数据集,减小两组数据集特征分布之间的差异。最后,利用深度神经网络对知识迁移后低维特征空间中高量测丢包率对应的数据进行标记,得到配电网实时状态。通过对IEEE标准算例和某实际地市公司配电网算例进行仿真测试,结果表明所提出的基于共享模糊等价关系的配电网状态估计方法能在高量测丢包率下获得准确的配电网实时运行状态,实现不同量测数据缺失程度下的配电网实时监测。

基于BIM的高速铁路设计概算智能预测方法研究

针对现行高速铁路定额预测存在的固定性、滞后性和预测方法的二维、线性等问题,在分析设计概算和影响因素之间的非线性、不确定性等演变趋势和机理基础上,以已完工程项目的历史数据为基础,构建类似已完工程设计概算历史数据库。为提高智能预测的精确度,采用余弦相似度方法在数据库中筛选相似案例,对拟建高速铁路项目进行类似度分类,采用非线性反向传播神经网络、模糊C均值聚类、模糊推理等方法集成优化组合,构建高速铁路拟建工程设计概算智能预测模型和BIM三维可视化模型。研究结果表明,建立设计概算非线性集成方法预测模型,实现不同量级数据下预测方法的优势互补,保证预测精度;通过BIM技术建立的三维可视化模型,有效提升设计概算预测的智能化水平与可视化效果。

基于参数估计-滑模阻抗控制的SEA性能分析

重载四足机器人的足部与地面接触过程和步态转换过程中会受到不确定的冲击载荷作用,易导致足部机构载荷过大从而造成结构的冲击损坏。因此,针对使用液压串联弹性执行器(series elastic actuators,SEA)作为足部末端在非结构环境下动态性能差的问题,提出了基于环境参数估计的滑模阻抗控制方法(environmental parameter estimation sliding mode,EPESM)。以阀控液压缸的活塞位移传递函数为基础建立了基于位置内环的SEA阻抗控制模型,并以PID作为基础控制器;为改善SEA阻抗控制的动态性能,根据Lyapunov第二法构建稳定的自适应环境参数估计方法对SEA期望位置进行前馈补偿;为提升自适应环境参数估计方法在SEA工作过程中不同阶段的动态性能和环境变化适应性,使用模糊控制方法对自适应环境参数估计方法中的自适应参数进行寻优;以SEA状态方程为基础构建滑模控制器与PID控制器进行动态性能对比分析。仿真结果表明:在变SEA弹簧刚度工况和变环境刚度下,EPESM阻抗控制的响应速度明显更快,可将调节时间从平均5 s缩短到1 s内,能更快地达到预期位移和预期接触力,且能略微降低稳态误差,使接触力误差保持在±6 N内。在动态跟踪工况下,EPESM阻抗控制的动态性能更好,在快速进入跟踪状态后,可以长时间保持0.2 s以内的相位滞后和5.2%的幅值误差。

基于微形变分析的电容式MEMS加速度计温漂误差精密估计方法

针对传统的电容式微机电系统(micro-electro-mechanical system,MEMS)加速度计(capacitive MEMS accelerometers,CMA)温漂误差(temperature drift error,TDE)补偿方法存在非精准溯源TDE致使TDE估计精度低、反复尝试估计模型构型导致构建过程复杂繁琐的问题,提出基于微形变分析的CMA TDE精密补偿方法。首先,通过微形变分析内部结构精准溯源TDE,基于径向基函数神经网络(radial basis function neural network,RBFNN)构建改进型TDE精密估计模型;其次,基于专家经验和模糊理论提出Expert-Fuzzy辅助决策下TDE估计模型辨识方法,为改进模型提供有效的构型指导;然后,设计升温试验测试CMA,构建传统模型和改进模型并通过对比其输出偏置稳定性评估TDE估计性能。实验结果表明,改进模型构建过程大大简化,补偿后CMA偏置稳定性提升约35%。本方法能够更加精准地估计TDE,有效解耦硅基材料的温度依赖性并提升CMA的环境适应性。