Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems

This paper presents a connection admission control (CAC) method that uses a type-2 fuzzy logic system (FLS). Type-2 FLSs can handle linguistic uncertainties. The linguistic knowledge about CAC is obtained from 30 computer network experts. A methodology for representing the linguistic knowledge using type-2 membership functions and processing surveys using type-2 FLS is proposed. The type-2 FLS provides soft decision boundaries, whereas a type-1 FLS provides a hard decision boundary. The soft decision boundaries can coordinate the cell loss ratio (CLR) and bandwidth utilization, which is impossible for the hard decision boundary.     

Knowledge bounded least squares method for the identification of fuzzy systems

This paper presents the knowledge bounded least squares method that uses both linguistic information (i.e., human knowledge and experience) and numerical data to identify fuzzy models. Based on the concept of fuzzy interval systems, the basic idea of this method is: first, to utilize all the available linguistic information to obtain a fuzzy interval system and then use the obtained fuzzy interval system to give the admissible model set (i.e., the set of all fuzzy models which are acceptable and reasonable from the point of view of linguistic information); second, to find a fuzzy model in the admissible fuzzy model set which best fits the available numerical data. It is shown that such a fuzzy model can be obtained by a quadratic programming approach. By comparing this method with the least squares method, it is proved that the fuzzy model obtained by the proposed method fits the real model better than the fuzzy model obtained by the least squares method.     

Implementation of a fuzzy controller for DC-DC converters using aninexpensive 8-b microcontroller

This paper presents an implementation of a fuzzy controller for DC-DC power converters using an inexpensive 8-bit microcontroller. An “on-chip” analog-to-digital (A/D) converter and PWM generator eliminate the external components needed to perform these functions. Implementation issues include limited on-chip program memory of 2 kB, unsigned integer arithmetic and computational delay. The duty cycle for the DC-DC power converter can only be updated every eight switching cycles because of the time required for the A/D conversion and the control calculations. However, it is demonstrated here that stable responses can be obtained for both buck and boost power converters under these conditions. Another important result is that the same microcontroller code, without any modifications, can control both power converters because their behavior can be described by the same set of linguistic rules. The contribution shows that a nonlinear controller such as fuzzy logic can be inexpensively implemented with microcontroller technology     

Efficient establishment of a fuzzy logic model for process modelingand control

Fuzzy logic modeling using internal and membership functions is a promising technique for the modeling and control of semiconductor manufacturing and packaging processes. To simplify its implementation procedure, a fuzzy logic model needs to be established with the minimum user interference. An algorithm with two major steps has been proposed and demonstrated for the efficient model establishment The first step develops intermediate fuzzy logic models with different numbers of membership functions assigned to each input variable. The number is one for the simplest model, and is increased one by one according to the pre-defined sequence and pathfinding criteria for more complex models, The second step stops the incremental procedure when the stopping criteria are met. The criteria are the multiple correlation factors R ² based on the training and the testing data. The algorithm's accuracy and efficiency have been demonstrated by testing it with five two-variable, nonlinear functions     

A QoS-provisioning neural fuzzy connection admission controller formultimedia high-speed networks

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract     

Two-stage fuzzy inference system for symbolic simplification of analog circuits

This paper presents a knowledge-based fuzzy approach to symbolic circuit simplification in an effort to imitate human reasoning and knowledge of circuit designer experts. The fuzzy approach differs from the conventional simplification techniques in that it can efficiently combine different input variables to obtain optimal simplified expressions. Additionally, this method was chosen due to its adjustability and interpretability, as well as its ability to manage very complex symbolic expressions. The proposed algorithm uses fuzzy logic to simplify the symbolic circuit transfer functions in two stages. In the first stage, a fuzzy system is applied to directly eliminate nonessential circuit components, resulting simplified circuit topology which also yields simpler transfer function. In the second stage, another fuzzy system is used to further simplify the symbolic transfer function from the already simplified circuit, such that deeper insight into the circuit behavior can be obtained. Symbolic and numerical results show that the fuzzy approach outperforms the conventional techniques in terms of accuracy, expression complexity, and CPU running time.     

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     

Hybrid control system design using a fuzzy logic interface

A hybrid control system is proposed for regulating an unknown nonlinear plant. The interface between the continuous-state plant and the discrete-event supervisor is designed using a fuzzy logic approach. The fuzzy logic interface partitions the continuous-state space into a finite number of regions. In each region, the original unknown nonlinear plant is approximated by a fuzzy logic-based linear model, then state-feedback controllers are designed for each linear model. A high-level supervisor coordinates (mode switching) the set of closed-loop systems in a stable and safe manner. The stability of the system is studied using nonsmooth Lyapunov functions. For illustration and verification purposes, this technique has been applied to the well-known inverted pendulum balancing problem.     

Current-mode linguistic hedge circuit for adaptive fuzzy logiccontrollers

A CMOS current-mode linguistic hedge `very' circuit which can be applied to adjust the membership function of a fuzzy set for obtaining adaptive fuzzy logic control is proposed. The design constraints of the proposed circuit are also discussed. Simulation results show that this circuit has high speed, large dynamic range and high accuracy     

模糊逻辑及其在数据融合中的应用

自从１９６５年Ｚａｄｅｈ发表关于模糊集理论的文章以来,模糊集理论已在工业控制、医疗诊断、经济决策、模式识别等领域得到广泛应用,随着模糊逻辑和可能性理论的提出和深入研究,它们在不确定推理模型的设计和多传感器信息融合中显示出越来越强大的优势,文中探讨它们在多传感器数据融合中的应用。     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

Knowledge-based resource management for multifunction radar: a look at scheduling and task prioritization

In this article, we consider two related aspects of radar resource management, scheduling and task prioritization. Two different methods of scheduling are examined and compared and their differences and similarities highlighted. The comparison suggests that prioritization of tasks plays a dominant role in determining performance. A prioritization scheme based on fuzzy logic is subsequently contrasted and compared with a hard logic approach as a basis for task prioritization. The setting of priorities is shown to be critically dependent on prior expert knowledge. By assessing the priorities of targets and sectors of surveillance according to a set of rules it is attempted to imitate the human decision-making process such that the resource manager can distribute the radar resources in a more effective way. Results suggest that the fuzzy approach is a valid means of evaluating the relative importance of the radar tasks; the resulting priorities have been adapted by the fuzzy logic prioritization method, according to how the radar system perceived the surrounding environment.     

Combined heuristic knowledge and limited measurement based fuzzylogic antiskid control for railway applications

In modern railway applications, the prevention of wheel skid is very important. This is because wheel skid can lead to an increase in noise and vibration from wheels with flat points, as well as an increased braking distance. However, conventional antiskid control has problems because the train wheel adhesion and skid characteristics are difficult and time consuming to accurately model. In addition, adequate measured numerical data describing wheel skid is difficult and expensive to obtain from actual railway systems. Therefore, a fuzzy logic based antiskid controller was implemented, where both linguistic and numerical system information could be used. In this paper, the design and implementation of the fuzzy logic controller is described. Results show that the antiskid controller has a very good performance, and performs better than a conventional controller. The described controller is currently running in Mitsubishi Electric railway brake sets in both Japan and overseas     

一种基于开放式网络环境的模糊主观信任模型研究

 在开放网络中,信息安全是至关重要的,而信任管理是信息安全的前提与基础.结合模糊理论在开放式网络环境下重新给出了信任的描述机制.针对主观信任的模糊性,将模糊逻辑引入主观信任研究中,对信任的传递进行了推理,提出了一种具有很强描述能力的形式化的信任推理机制.鉴于主观信任的动态性,结合模糊理论中的近似度,提出了一种新的信任更新机制,能够更有效防止恶意推荐.     

Image enhancement with a fuzzy logic approach

An image enhancement technique is presented. The technique first segments the image based on a fuzzy logic function. A generalised median filter (GMF) is then used for the noise suppression and edge preservation in the membership function. The final image is derived with a set of nonlinear grey scale mapping techniques     

Current-Mode Fuzzy Linguistic Hedge Circuits

In this paper, a current-mode design methodology to implement a set of fuzzy linguistic hedge circuits is proposed. The so-called fuzzy linguistic hedge is a fuzzy operation applied to adjust the membership function of a fuzzy set. The fuzzy membership function of control variable and the control rules are very important in a fuzzy logic controller because they dominate the control strategies. If the control results fail to meet the system requirements, the control objective can still be achieved by adjusting the membership function of the fuzzy set or the control rules. Moreover, the adjustment effect of the control strategies through the modifications of the fuzzy membership function is the same as that of the system control rules. In this paper, we propose a set of fuzzy linguistic hedge circuits, including absolutely, very, much more, more, plus minus, more or less, slightly, and contrast intensification, which has been fabricated in 0.8 m CMOS process. Experimental results show that the average error of the circuits is within 1% of the full scale current. Under the power supply voltage of 3.3 V, the operating dynamic range is 50 A. Furthermore, these circuits still work well even when the power supply voltage is down to 2.5 V. In addition, in real world application, we can incorporate a membership function generator, a fuzzification unit, a multi-input maximum/minimum circuit, and a defuzzification unit with the linguistic hedge used to modify the membership function in order to develop a real-time adaptive fuzzy logic controller.     

一种启发式变阶直觉模糊时间序列预测模型

论文针对已有高阶模糊时间序列模型在预测精度和预测范围上的限制,结合直觉模糊集理论,提出一种启发式变阶直觉模糊时间序列预测模型。模型首先应用直接模糊聚类算法对论域进行非等分划分;然后,针对直觉模糊时间序列的数据特性,改进现有直觉模糊集隶属度和非隶属度函数的建立方法;最后,采用阶数随序列实时变化的高阶预测规则进行预测,并将历史数据发展趋势的启发知识引入解模糊过程,使模型的预测范围得到扩展。在Alabama大学入学人数和北京市日均气温两组数据集上分别与典型方法进行对比实验,结果表明该模型有效克服了传统模型的缺点,拥有较高的预测精度,证明了模型的有效性和优越性。     

一种新的多传感器故障分类诊断方法研究

本文在分析了现有故障诊断方法的特点及其不适于多传感器故障分类诊断的原因基础上,结合网络形式多输出模糊逻辑系统的单输出模糊逻辑系统传感器故障分类诊断方法,提出了一种新的适合于多传感器故障分类诊断的模糊逻辑系统方法、仿真实验难证了此方法的有效性。