基于模糊逻辑的多终端协同的垂直切换决策算法

针对异构网下多终端协同的垂直切换决策问题,提出一种基于模糊逻辑和层次分析法的垂直切换决策算法,它分为2部分:切换时机的判断和虚拟终端的构建.首先采用模糊逻辑判断切换的时机,然后采用层次分析算法与简单加权求和结合的方法选择虚拟终端的最优构成方式.仿真结果表明,算法能够降低切换次数和不必要切换率,并且能够充分体现应用需求和用户偏好.     

A fuzzy logic control for antilock braking system integrated in the IMMa tire test bench

The use of fuzzy control strategies has recently gained enormous acknowledgement for the control of nonlinear and time-variant systems. This article describes the development of a fuzzy control method for a tire antilock system in vehicles while braking, integrated in a tire test bench, thereby allowing us to imitate the functioning and to understand the behavior of these systems in a reliable way. One of the inconveniences found in the development of these systems has been the difficulty of adjustment to the real conditions of a functioning vehicle. The main advantage obtained when using the tire test bench is the possibility of being able to reproduce the conditions established as fundamental to the operation of the antilock brake system (ABS) in a reliable and repetitive way, and to adjust these systems until optimal performance is obtained. The fuzzy control system has been developed and tested in the tire test bench to be able to refine its fundamental parameters, obtaining adequate results in all the studied conditions. The ease of the bench for the development and verification of new control systems for ABS has been demonstrated.     

Vertical current components in integrated injection logic

Analytic expressions representing a double diffused transistor impurity profile are used to calculate the current components in IIL structures. The expression for the hole current is given for IIL structures with the epitaxial layer grown on a wide n⁺substrate and for buried layer structures. It was found that an equivalent recombination velocity at the n-n⁺interface,S_{nn+}, is of order 10²higher in buried layer structures than in structures with the epitaxial layer grown on a wide n⁺substrate for comparable doping levels. Results obtained using the analytic expressions are compared with those obtained using a computer program which includes heavy doping effects and doping level mobility dependence. Both calculated and computed results are also compared with measured currents for a given IIL structure with the epitaxial layer grown on a wide n⁺substrate. The calculated and the computed results are in good agreement with the experimental results.     

A knowledge-based system for breast cancer classification using fuzzy logic method

Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.     

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.     

Efficient algorithm for logic design using multiplexers

An algorithmic procedure has been developed for the realisation of any Boolean function F(n) of n variables with a single multiplexer of minimum size. This procedure gives the choice of control variables to be used for the realisation of the function. The algorithm is iterative in nature and very suitable for machine implementation     

A hybrid fuzzy logic and neural network algorithm for robot motioncontrol

Robotic manipulators are multivariable nonlinear coupling dynamic systems. Industrial robots were controlled by using a traditional controller, the control performance of which may change with respect to operating conditions. Since the robotic manipulators have complicated nonlinear mathematical models, control systems based on the system model are difficult to design. In this paper, a model-free hybrid fuzzy logic and neural network algorithm was proposed to control this multi-input/multi-output (MIMO) robotic system. First, a fuzzy logic controller was designed to control individual joints of this 4-degree-of-freedom (DOF) robot. Secondly, a coupling neural network controller was introduced to take care of the coupling effect among joints and refine the control performance of this robotic system. The experimental results showed that the application of this control strategy effectively improved the trajectory tracking precision     

MCFL: an energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network

Wireless Networks - In this study, a multi-clustering algorithm based on fuzzy logic (MCFL) with an entirely different approach is presented to carry out node clustering in wsn. This approach...     

Tunnel lane‐positioning system for autonomous driving cars using LED chromaticity and fuzzy logic system

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three‐lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.     

采用阴影投射法实现的光学并行模糊逻辑

本文提出了一种新的空间编码方法并利用阴影投射系统实现了光学并行模糊逻辑门。本系统可全光学并行实现模糊逻辑中的七种基本逻辑运算,并给出了实验结果。     

基于分布式模糊逻辑推理的WSN分簇算法

针对无线传感器网络(WSN)中均衡分簇问题,提出一种基于模糊逻辑推理的WSN分布式分簇算法(DFLC)。利用分布式模糊逻辑控制器选择根节点,以能量大小、中心性、距基站的距离、跳数和节点密度5个参数作为分布式模糊逻辑控制算法的输入。为网络中的中间节点分配模糊逻辑推理引擎,根据自身和相邻节点的信息进行判断,选择发送质量最高子节点的回复消息给根节点,减少了消息传输数量。仿真实验表明,在产生消息数量、能源消耗、存活节点数、容错性、负载平衡等方面,DFLC算法都优于LEACH,ACAWT,Gupta和CHEF算法。     

Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO

This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network(WSN) which aims to balance the node power consumption and prolong the network lifetime as long as possible.Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol.On the one hand,considering each node’s local information such as energy level,distance to base station and local density,we use fuzzy logic system to determine one node’s chance of becoming cluster head and estimate the corresponding competence radius.On the other hand,adaptive max-min ant colony optimization is used to construct energy-aware inter-cluster routing between cluster heads and base station(BS),which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent.The confirmation experiment results have indicated the proposed clustering algorithm has more superior performance than other methods such as low energy adaptive clustering hierarchy(LEACH) and energy efficient unequal clustering(EEUC).     

Computer-aided tuning of microwave filters using fuzzy logic

This paper introduces an algorithm based on fuzzy logic for tuning microwave filters. The approach is demonstrated by considering two filters: a four-pole Chebyshev filter and an eight-pole elliptic filter. Each filter is then detuned to perform two examples: one is slightly detuned and the other is highly detuned. In both cases, the approach has proven to be very efficient in identifying the filter elements that cause the detuning. The fuzzy rules are extracted from sampled data. The expert rules could also be added. The algorithm can be applied to any microwave circuit tuning problem.     

On using fuzzy logic to integrate learning mechanisms in an electro-hydraulic system. I. Actuator's fuzzy modeling

Drive systems are usually modeled using a mathematical characterization of their physical phenomena. However, the difficulty in establishing a relevant model representation, in particular for electro-hydraulic systems, makes important the search for other modeling mechanisms that allow the combination of previously compiled system's knowledge with acquired experimental information. This paper, divided into two parts, describes the potential and possible drawbacks of integrating fuzzy learning mechanisms into a drive system that includes an electro-hydraulic actuator. First, experimental verification of the actuator's fuzzy modeling is presented in Part I of the paper, where the variable selection problem and the performance of the learning algorithm are discussed. In Part II, extensive experimental results employing the extracted fuzzy model and associated learning algorithm are presented. The feasibility and effectiveness of integrating fuzzy learning mechanisms into the actuator's control is also discussed.     

An integrated m-out-of-n detection circuit using threshold logic

The design of the integrated 4-out-of-9 detector is based on a threshold logic approach. A differential current-switching circuit configuration is used, and the detector is fully compatible with conventional emitter-coupled logic (ECL). The circuit has a propagation delay of 16 ns and dissipates only 100 mW. The functional power-delay product of 1600 pJ is an order of magnitude below that achieved with an efficient gate design.     

Vertical injection logic

Vertical injection logic (VIL) is a novel form of integrated injection logic (I/SUP 2/L). A vertical p-n-p transistor is used in place of a lateral p-n-p transistor to obtain an improved performance at the same packing density as conventional I/SUP 2/L. The current gain of the p-n-p transistor can be increased, which leads to the excellent power-delay product. The intrinsic delay time is also improved by the action of the bottom injector as a hole sink. The fabrication process and electrical characteristics of VIL are described and contrasted with conventional I/SUP 2/L. A tentative hole sink model is also proposed. The experimental results showed the minimum delay time of 8.8 ns and the power-delay product of 0.07 pJ at low power level below 1 /spl mu/W for VIL compared to 25 ns and 0.18 pJ for standard I/SUP 2/L.     

基于模糊逻辑的降水粒子相态识别技术

为提高双偏振雷达在水凝物相态识别方面的能力,利用双偏振气象雷达目标回波中的反射率因子Z_H、差分反射率因子Z_DR、差分相位常数K_DP、共偏相关系数ρ_HV,构造反射率标准差SD (Z_H)和差分相位标准差SD(?_DP)。将这6个极化参数作为输入量,通过统计美国国家海洋和大气管理局(NOAA)提供的大量水凝物样本数据,根据样本数随雷达回波参量分布,提出了以β型函数为隶属函数的模糊逻辑分类算法。该算法能够有效识别小到中雨(RA)、大雨(HR)、大滴(BD)、干雪(DS)、湿雪(WS)、冰晶(IC)、霰(GR)、雨雹(RH)、大雹(LH)9类气象回波和1类地物回波(GC)。选取美国S波段的科罗拉多州丹佛地区的气象雷达KFTG在不同时间点的观测资料进行测试,识别结果与NOAA提供的LevelⅢ数据中的水凝物分类结果基本一致,验证了本文提出的模糊逻辑算法具有良好的分类效果。     

ATM traffic management and congestion control using fuzzy logic

The paper focuses on the use of fuzzy logic in network management, ATM congestion control, and ATM switching. The ATM network management ensures traffic agreement at the source level in order to prevent congestion situations from occurring. The paper reviews work in the area of traffic policing and introduces a scheme for improving the performance of a fuzzy policer. The ATM fuzzy switch introduced, enhances the capabilities of the ATM switch, one of the most critical elements in the ATM network     

Deadzone compensation of an XY-positioning table using fuzzy logic

A deadzone compensator is designed for an XY-positioning table using fuzzy logic. The classification property of fuzzy-logic systems makes them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy-logic parameters, so that the deadzone-compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded-parameter estimates. Formal nonlinear-stability proofs are given to show that the tracking error is small. The fuzzy-logic deadzone compensator is implemented on an XY-positioning table to show its efficacy.