基于Takagi-Sugeno-Kang模糊集合的噪声干扰检测方法

为识别混合在接收机热噪声中的人为噪声干扰信号,提出了基于 TSK ( Takagi-Sugeno-Kang)模糊集合的干扰检测方法。首先将无干扰环境下信道热噪声数据和有人为噪声干扰下的混合噪声数据组合成训练数据序列,利用训练序列对TSK模糊集合模型进行训练,调节模型中规则的多项式系数,使TSK模糊模型对接收信号中的噪声特性与干扰判决之间建立确定函数关系,实现对噪声干扰的检测。通信电台的实验验证表明：尽管接收机的自动增益控制将外部噪声干扰缩小到与本机噪声相当水平,所提方法仍能有效检测出信道中是否有人为噪声干扰存在。     

基于迭代模糊聚类算法与K近邻和数据字典的集成TSK模糊分类器

该文提出一种新型的集成TSK模糊分类器(IK-D-TSK),首先通过并行学习的方式组织所有0阶TSK模糊子分类器,然后每个子分类器的输出被扩充到原始(验证)输入空间,最后通过提出的迭代模糊聚类算法(IFCM)作用在增强验证集上生成数据字典,从而利用KNN对测试数据进行快速预测。IK-D-TSK具有以下优点:在IK-D-TSK中,每个0阶TSK子分类器的输出被扩充到原始入空间,以并行方式打开原始(验证)输入空间中存在的流形结构,根据堆栈泛化原理,可以保证提高分类精度;和传统TSK模糊分类器相比,IK-D-TSK以并行方式训练所有的子分类器,因此运行速度可以得到有效保证;由于IK-D-TSK是在以IFCM & KNN所获得的数据字典的基础上进行分类的,因此具有强鲁棒性。理论和实验验证了模糊分类器IK-D-TSK具有较高的分类性能、强鲁棒性和高可解释性。     

基于模糊子空间聚类的〇阶L2型TSK模糊系统

经典数据驱动型TSK(Takagi-Sugeno-Kang)模糊系统在获取模糊规则时,会考虑数据的所有特征空间,其带来一个重要缺陷:如果数据的特征空间维数过高,则系统获取的模糊规则繁杂,使系统复杂度增加而导致解释性下降。该文针对此缺陷,探讨了一种基于模糊子空间聚类的〇阶L2型TSK模糊系统(Fuzzy Subspace Clustering based zero-order L2- norm TSK Fuzzy System, FSC-0-L2-TSK-FS)构建新方法。新方法构建的模糊系统不仅能缩减模糊规则前件的特征空间,而且获取的模糊规则可对应于不同的特征子空间,从而具有更接近人类思维的推理机制。模拟和真实数据集上的建模结果表明,新方法增强了面对高维数据所建模型的解释性,同时所建模型得到了较之于一些经典方法更好或可比较的泛化性能。     

Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control

The paper describes an approach to generating optimal adaptive fuzzy neural models from I/O data. This approach combines structure and parameter identification of Takagi-Sugeno-Kang (TSK) fuzzy models. We propose to achieve structure determination via a combination of modified mountain clustering (MMC) algorithm, recursive least squares estimation (RLSE), and group method of data handling (GMDH). Parameter adjustment is achieved by training the initial TSK model using the algorithm of an adaptive network based fuzzy inference system (ANFIS), which employs backpropagation (BP) and RLSE. Further, a procedure for generating locally optimal model structures is suggested. The structure optimization procedure is composed of two phases: 1) locally optimal rule premise variables subsets (LOPVS) are identified using MMC, GMDH, and a search tree (ST); and 2) locally optimal numbers of model rules (LONOR) are determined using MMC/RLSE along with parallel simulation mean square error (PSMSE) as a performance index. The effectiveness of the proposed approach is verified by a variety of simulation examples. The examples include modeling of a nonlinear dynamical process from I/O data and modeling nonlinear components of dynamical plants, followed by tracking control based on a model reference adaptive scheme (MRAC). Simulation results show that this approach is fast and accurate and leads to several optimal models     

TSK-based decision feedback equalizer using an evolutionary algorithm applied to QAM communication systems

A Takagi-Sugeno-Kang (TSK) fuzzy-based decision feedback equalizer (DFE) applied to detecting a quadrature-amplitude-modulation (QAM) signal is presented. The structure of a TSK fuzzy system may produce many local minimum of involved cost functions for detecting received signals. An evolutionary algorithm is adopted for the proposed TSK fuzzy equalizer to overcome the difficulty for locating global minimum in weight space. Results showed that the performance of the newly designed equalizer is much improved for DFEs with multilayer perceptron or least mean square scheme.     

Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm

This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot arm will be given.     

具备视角协同学习能力的多视角TSK型模糊系统

传统模糊系统建模方法本质上是一种单视角学习模式,面向适合多视角处理的场景时,它们通常只能将每一视角割裂开来进行独立建模,这导致其所得系统泛化性能往往不令人满意。针对此缺陷,该文探讨具备多视角学习能力的模糊系统建模方法。为此,基于经典的L2型TSK模糊系统,通过引入具备多视角学习能力的协同学习项,该文提出了核心的多视角TSK型模糊系统(MV-TSK-FS)建模方法。MV-TSK-FS不仅能有效地利用各视角不同特征构成的独立样本信息,还能充分地利用各视角间由于相互关联而存在内在信息,以最终达到提高系统泛化性能的效果。在模拟数据集与真实数据集上的实验结果验证了较之于传统单视角模糊建模方法该多视角模糊系统有着更好的泛化性和适用性。     

A QoS-guaranteed fuzzy channel allocation controller forhierarchical cellular systems

This paper proposes a fuzzy channel allocation controller (FCAC) for hierarchical cellular systems. The FCAC mainly contains a fuzzy channel allocation processor (FCAP) which is designed to be in a two-layer architecture that consists of a fuzzy admission threshold estimator in the first layer and a fuzzy channel allocator in the second layer. The FCAP chooses the handoff failure probability, defined as the quality-of-service (QoS) index, and the resource availability as input linguistic variables for the fuzzy admission threshold estimator, where the Sugeno's (1985) position gradient-type reasoning method is applied to adaptively adjust the admission threshold for the fuzzy channel allocator. The FCAP takes the mobility of user, the channel utilization, and the resource availability as input variables for the fuzzy channel allocator so that the channel allocation is finally determined, further based upon the admission threshold. Simulation results show that FCAC can always guarantee the QoS requirement of handoff failure probability for all traffic loads. Also it improves the system utilization by 31.2% while it increases the handoff rate by 12.94 over the overflow channel allocation (OCA) scheme; it enhances the system utilization by 6% and still reduces the handoff rate by 6.746 as compared to the combined channel allocation (CCA) scheme, under a defined QoS constraint     

A fuzzy logic approach to detect hotspots with NOAA/AVHRR image using multi-channel information fusion technique

This paper proposes a novel approach to detect hotspots using NOAA advanced very high resolution radiometer (AVHRR) for the Jharia, Jharkhand (India) region. Jharia coalfield in Jharkhand is the richest coal bearing area in India that contains a large number of mine fires which have been burning for several decades. In this paper, a fuzzy based methodology has been applied for the determination of hotspots to Jharia AVHRR images based on a theoretical model that establishes relationship among AVHRR channel 4, channel 5 and different vegetation indices. The algorithm consists of four stages: data preprocessing, multi-channel information fusion, hotspot detection using fuzzy logic approach and validation of result. The most commonly used existing algorithms like contextual algorithms, multi-thresholding, entropy based thresholding, and genetic algorithms have limitation that they need some mathematical model for training in order to get the required result. The employed fuzzy logic approach overcomes this requirement and in addition, it is flexible, tolerant of imprecise data and is based on natural language. The results were compared with the results obtained by ground survey and a good agreement has been obtained between observed and predicted hotspots.     

Deregulated electricity market data representation by fuzzyregression models

In this paper, the authors present a fuzzy set-based model that represents the relation of electricity demand and price in a recently deregulated electricity market. A simple regression analysis shows the price data's nonlinear trend as the demand volume increases. We have divided the data cluster into two overlapping regions: low demand and high demand. Regression curves, obtained for the two clusters, are smoothly connected by a Takagi-Sugeno-Kang (TSK)-fuzzy model. The fuzzy model is further expanded to encompass the volatile data region by introducing fuzzy numbers in regression parameters. The developed model can indicate the possibility distribution of electricity prices for a given demand value. The model also has the flexibility of narrowing its focus by modifying the fuzzy numbers. California Power Exchange market data are analyzed as a numerical example     

A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control

Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if--then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified     

A neural fuzzy resource manager for hierarchical cellular systems supporting multimedia services

Using intelligent techniques to perform radio resource management is an effective method. The paper proposes neural fuzzy control for radio resource management in hierarchical cellular systems supporting multimedia services. A neural fuzzy resource manager (NFRM) is designed, which mainly contains a neural fuzzy channel allocation processor (NFCAP). The NFCAP has a two-layer architecture: a fuzzy cell selector (FCS) in the first layer and a neural fuzzy call-admission and rate controller (NFCRC) in the second layer. The FCS chooses not only the handoff failure probabilities and the resource availabilities in both microcell and macrocell, but also the user mobility, as input linguistic variables. The NFCRC takes the handoff failure probability and the resource availability of the selected cell as input variables to perform call admission control and rate control for the call. Simulation results show that the NFRM can always guarantee the quality of service (QoS) requirement for handoff failure probability for all traffic loads. Also, the NFRM improves the system utilization by 31.1% while increasing the handoff rate by 2% over the overflow channel allocation (OCA) scheme; it enhances the system utilization by 6.3% and 1.4%, and still reduces the handoff rate by 14.9% and 6.8%, as compared to the combined channel allocation (CCA) and fuzzy channel allocation control (FCAC) schemes, respectively, under a predefined QoS constraint.     

Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm

Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization (SPSO) is proposed. In SPSO, each RFC corresponds to a particle, and all the free parameters in RFC are optimally searched. We use the PSO to find a good solution globally, and the incorporation of the Simplex method helps find a better solution around the local region of the best solution found by PSO so far. Then, online adaptive temperature control with ARFC chip implemented by FPGA is proposed. In the ARFC chip, the consequent parameters of all rules are all tuned online using gradient descent. To verify the performance of the ARFC chip, experiments on a water bath temperature system are performed.     

A fuzzy PIC multiuser detection/vector channel estimation scheme in Rayleigh fading channels

We propose a fuzzy parallel interference cancellation (PIC) multiuser detection/vector channel estimation (VCE) scheme in Rayleigh fading channels. The VCE is based on a first‐order auto‐regressive model and an expectation‐maximization algorithm. The signal‐to‐interference ratio and signal‐to‐noise ratio are estimated from the vector channel model's parameters, and we adapt the weight of each interference cancellation path via fuzzy inference mechanism. The proposed fuzzy PIC and VCE cooperate in a way such that some of the fuzzy PIC input parameters come from the channel predictor and the fuzzy PIC makes the channel predictor be more accurate at the next PIC stage. Computing weights via fuzzy adaptive method adds insignificant complexity because it involves only table lookup. The simulation results show that the proposed fuzzy PIC/VCE scheme performs better than the improved PIC/vector channel estimation scheme with optimal parameter in fast‐varying Rayleigh fading channels. Copyright © 2007 John Wiley & Sons, Ltd.     

Fuzzy channel-optimized vector quantization

A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data     

Fuzzy neural control of voice cells in ATM networks

This paper presents the design of a fuzzy controller for managing cells generated by voice sources in asynchronous transfer mode (ATM) networks. Typical voice cells, characterized by a high degree of burstiness, complicate any attempt to use classical control theory in the design of an ATM cell rate controller. The fuzzy control approach presented in this paper overcomes this limitation by appealing to the linguistic ability of fuzzy set theory and logic to handle the complexity. Specifically, the cell rate control problem is linguistically stated but treated mathematically via fuzzy set manipulation. In particular, the ATM voice cell controller being proposed is an improved and intelligent implementation of the leaky bucket cell rate control mechanism extensively studied in the literature. This intelligent implementation of the leaky bucket mechanism uses a channel utilization feedback via the QoS parameters to improve its performance. This ATM fuzzy controller takes the form of an organized set of linguistic rules quantitatively expressed and manipulated by means of fuzzy set theory and fuzzy logic. The fuzzy control rules are stored in fuzzy associative memory to permit parallel executions     

Critical Path Analysis With Fuzzy Activity Times

Fuzzy logic has been proposed as an alternate approach for quantifying uncertainty relating to project activity duration. The fuzzy logic approach may be suitable in the situations where past data are either unavailable or not relevant, the definition of the activity itself is somewhat unclear, or the notion of the activity's completion is vague. The purpose of this paper is to present a new methodology for fuzzy critical path analysis that is consistent with the extension principle of fuzzy logic. It is the direct generalization of critical path analysis to the fuzzy domains, and resolves some of the problems expressed in the fuzzy critical path literature, especially in computing the fuzzy backward pass of the project network and fuzzy activity criticality. Here the uncertainty is represented by three possible time estimates in a way that is similar to the well-known program evaluation and review technique (PERT) approach. Another important advantage of the proposed approach is that one integrated procedure determines both the fuzzy set of critical path lengths and fuzzy activity criticality. The proposed approach constructs the membership function for the fuzzy set of critical path lengths and the fuzzy activity criticality by solving a series of mathematical programming problems. The proposed method is successfully tested on well-known problem sets, and is shown to offer computation times that should allow it to help project managers better understand and manage project schedule uncertainty.     

TSK网络结构设计:一个基于二阶运算的局部反馈网络

按照局部反馈和易于解释两个原则,本文设计了一个带反馈的二阶TSK网络。网络的结论部分以输入成分的二项式[1,2]作为神经元的导出域。二项式是神经网络领域少见的运算模式,导出域通常为输入与权向量的内积。网络的结论部分借助于延迟反馈引入动态特性,属于局部反馈,所以动态导数计算简洁。TSK网络表征一组If-Then规则,本文网络较采用独立变换[3]和基于G—FGS神经元[4]的网络易于理解。系统辨识的仿真证明本文网络可以很好地辨识动态系统,表明TSK网络拓扑结构设计中使用二阶运算是可行的。     

On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.     

Fuzzy adaptive decision feedback equaliser

As a new approach to the equalisation of nonlinear channels, a fuzzy adaptive decision feedback equaliser based on a fuzzy adaptive filter is proposed. With perfect knowledge of the channel, the proposed method not only improves equalisation performance but also reduces the computational complexity compared with the conventional fuzzy adaptive filter