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.     

Improving group decision making: a fuzzy GSS approach

Group decision-making methods have been developed extensively, but their adaptation for use in organizations has been problematic. According to Arrow's (1963) Impossibility theorem, one conceivable reason is that a group decision outcome could never satisfy all decision makers' individual preferences. In order to accommodate individual decision maker's preferences in a group decision-making task, this correspondence presents a fuzzy multiperson multicriteria decision making (MMCDM) model and a structured group decision-making process. The fuzzy MMCDM model includes fuzzy individual preference generation and group preference aggregation. The structured decision-making process keeps the group interaction on track, so that the fuzzy MMCDM model can be effectively applied to the group decision-making task. Based on the proposed model and the decision-making process, a fuzzy group support system (GSS) has been developed and applied to a group assessment task. An empirical study was conducted and the experiment results showed that use of the fuzzy GSS enhanced individual understanding, consensus, and satisfaction of the group decision outcome     

A fuzzy logic cooperative MAC for MANET

In both wireless local area networks（WLAN） and mobile ad hoc networks（MANET）, the 1EEE 802.11e medium access control （MAC） protocol is proposed for an effective quality of service （QoS） solution. A number of studies have been done to enhance the performance of 802.11e in MANET by independently adjusting contention window （CW） size of each access category （AC） in every node. However, without the cooperation between the high priority flows and lower priority flows, the QoS goal of high priority flows cannot achieve effectively. In this article, a fuzzy logic based cooperative MAC protocol （FLCMAC） is proposed to cooperate amongst network flows and dynamically adjust access probability of each low priority flow affecting the high priority flows to satisfy their QoS requirement. The simulation results indicate that compared to the enhanced distributed channel access （EDCA） scheme of 802.11e, the FLCMAC consistently excels, in terms of throughput and delay under moderate and heavy background traffic both in single-hop and multi-hop scenarios.     

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 inference processor

A mixed analog-digital fuzzy logic inference engine chip fabricated in an 0.8 μm CMOS process is described. Interface to the processor behaves like a static RAM, and computation of the fuzzy logic inference is performed between memory locations in parallel by an array of analog charge-domain circuits. Eight inputs and four outputs are provided, and up to 32 rules may be programmed into the chip. The results of the inference over all rules, including a center-of-mass defuzzification, may be computed in 2 μs     

Intelligent wireless mesh path selection algorithm using fuzzy decision making

Wireless mesh networks (WMNs) are expected to be widely deployed due to their ability to provide ubiquity, convenience, cost-efficiency, and simplicity for both service providers and end-users. Recently, the IEEE 802.11s standard introduces the hybrid wireless mesh protocol (HWMP) which is inspired by a combination of on-demand and tree-based pro-active routing algorithms. In this paper, we argue that the proposed unimetric path selection algorithm in the standard is not reliable. We introduce and examine a novel multimetric wireless mesh path selection algorithm using fuzzy decision making under realistic wireless channel conditions. The proposed path selection algorithm is designed to improve the performance of both re-active and pro-active routing protocols of HWMP for not only single-channel but also multi-channel WMNs. The reported results show the superior performance of the proposed path selection algorithm in terms of delay and packet delivery ratio without increasing overhead significantly. Although some fuzzy-based routing algorithms have been defined in literature recently, to the best of our knowledge, this paper is the first one to introduce and examine the use of fuzzy logic in the path selection of single- and multi-channel wireless local area network-based WMNs under realistic wireless channel conditions.     

一种模糊时态描述逻辑

针对现实生活中信息的时间性和模糊性,在模糊描述逻辑和时态逻辑的基础上,提出了一种模糊时态描述逻辑FTDL,并给出了其语法和语义的相关说明。与模糊描述逻辑FALC相比,FTDL的提出为语义Web服务的建模和推理提供了一种有效的途径。     

A two-layered fuzzy logic controller for systems with deadzones

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a usual "fuzzy PD" controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this paper, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a usual fuzzy PD controller. Our proposed controller exhibits superior transient and steady-state performance compared to usual fuzzy PD controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples.<>     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps

The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.     

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     

Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user’s signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.     

Research on dynamic handover decision algorithm based on fuzzy logic control in mobile FSO networks

Because of multiple advantages such as high bandwidth, high security, and flexible networking, the free space optical communication (FSO) has attracted more and more attention. However, the narrow beam of laser and the constrained access degree of optical node limit the development of mobile FSO networks, especially in the aspect of handover. In this paper, we propose a fuzzy logic control-based handover decision (FLC-HD) algorithm to ensure real-time and reliable communication. First, according to the predictive position and the motion information of mobile nodes obtained by the error correction extend Kalman filter algorithm, we obtain the received signal strength and the relative motion angles at next transmission. Second, we put forward the concept of threshold to ensure the completion of handover operations before the current link breaking. Third, the fuzzy logic control method is designed to select the optimal access point (AP) with three critical parameters, i.e., the received signal strength, the relative motion angles, and the access degrees of nodes. Simulation results show that the FLC-HD algorithm can improve the handover efficiency and relieve the ping-pong effect in mobile FSO networks.

     

Network selection based on multiple attribute decision making and group decision making for heterogeneous wireless networks

One of the key issues for radio resources management is network selection strategy in heterogeneous scenarios.In order to provide ubiquitous service,the paper puts forward a network selection algorithm based on multiple attribute decision making(MADM) and group decision making(GDM).Firstly,the proposed algorithm acquires attribute weights’ vectors of the subjective and objective decision makers based on MADM,and then the two attribute weights’ vectors are synthesized to be a new attribute weights’ vector by using GDM.Considering that the results of GDM should be reasonable and convincible,the criterion of consistency is adopted for judging the compatibility of group judgments.More specifically,the algorithm takes into account not only objective attributes of networks but also the preference of subscribers and traffic class.Hence it guarantees that the subscribers can not select the networks with poor performance depending on their preference.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide subscribers with satisfactory quality of service(QoS).     

A fuzzy logic classification scheme for selecting and blendingsatellite ocean color algorithms

An approach for selecting and blending bio-optical algorithms is demonstrated using an ocean color satellite image of the northwest Atlantic shelf. This approach is based on a fuzzy logic classification scheme applied to the satellite-derived water-leaving radiance data, and it is used to select and blend class-specific algorithms. Local in situ bio-optical data were used to characterize optically-distinct water classes a priori and to parameterize algorithms for each class. Although the algorithms can be of any type (empirical or analytical), this demonstration involves class-specific semi-analytic algorithms, which are the inverse of a radiance model. The semi-analytic algorithms retrieve three variables related to the concentrations of optically active constituents. When applied to a satellite image, the fuzzy logic approach involves three steps. First, a membership function is computed for each pixel and each class. This membership function expresses the likelihood that the measured radiance belongs to a class, with a known reflectance distribution. Thus, for each pixel, class memberships are assigned to the predetermined classes on the basis of the derived membership functions. Second, three variables are retrieved from each of the class-specific algorithms for which the pixel has membership. Third, the class memberships are used to weight the class specific retrievals to obtain a final blended retrieval for each pixel. This approach allows for graded transitions between water types, and blends separately tuned algorithms for different water masses without suffering from the “patchwork quilt” effect associated with hard-classification schemes     

Multiobjective decision making for traffic assignment

The use of traffic assignment methods with multiobjective decision making to remedy the shortcomings of conventional traffic assignment methods is discussed. The optimal flow patterns are determined using three objectives: total travel time for road users; air pollution for nonusers; and travel distance for government. By using multiobjective decision making and nonlinear programming techniques, a series of noninferior solutions is generated. By combining an eigenvector weighting method with pairwise comparison, a compromise solution for the flow pattern is obtained. As an application example the Taipei network system is discussed. The results show that if other nontraffic-related factors are taken into account, the multiobjective traffic assignment approach is more reasonable and suitable than conventional approaches     

一种基于辩论的决策方法研究

为了解决动态开放环境下决策方案可行性论证的难题,将辩论理论与实用推理结合提出了一种新的适用于多属性决策的辩论框架,定义了论据的类型和论据之间可能存在的攻击关系。将基于辩论的多属性决策过程分为用户需求获取、方案可行性论证和可行方案选优3个阶段,通过论据及其攻击关系对方案的可行性进行辩论论证。最后采用一个医疗决策问题对该框架的应用进行了示例说明,结果表明,该框架能够较好的处理信息不完整、不一致条件下的决策问题。     

Knowledge engineering for medical decision making: A review of computer-based clinical decision aids

Computer-based models of medical decision making account for a large portion of clinical computing efforts. This article reviews representative examples from each of several major medical computing paradigms. These include 1) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that considerable basic research in medical computing remains to be done and that powerful new approaches may lie in the melding of two or more established techniques.     

A neuro fuzzy logic approach to material processing

A new application of fuzzy systems to the processing of materials is presented. The relationships between temperature, time, and the impact strength of an austempered ductile iron (ADI) part are adaptively modeled. Four fuzzy and neuro fuzzy approaches have been used to build predictive models. These are: a fuzzy based model, a backpropagation based neuro fuzzy model, a clustering based model, and a clustering backpropagation based neuro fuzzy model. The clustering approach, using the subclustering method, yielded the best predictive results when all models had been given the same input-output training data. The backpropagation based neuro fuzzy approach suffers from the lack of a higher number of input-output data training sets. All preliminary results obtained suggest the adequacy of the fuzzy based and neuro fuzzy based modeling techniques to tackle those types of problems in the material processing areas     

A high-speed multiplexer-based fine-grain pipelined architecture for digital fuzzy logic controllers

Design and implementation of a high-speed multiplexer-based fine-grain pipelined architecture for a general digital fuzzy logic controller has been presented. All the operators have been designed at gate level. For the multiplication, a multiplexer-based modified Wallace tree multiplier has been designed, and for the division and addition multiplexer-based non-restoring parallel divider and multiplexer-based Manchester adder have been used, respectively. To further increase the processing speed, fine-grain pipelining technique has been employed. By using this technique, the critical path of the circuit is broken into finer pieces. Based on the proposed architecture, and by using Quartus II 9.1, a sample two-input, one-output digital fuzzy logic controller with eight rules has been successfully synthesised and implemented on Stratix II field programmable gate array. Simulations were carried out using DSP Builder in the MATLAB/Simulink tool at a maximum clock rate of 301.84 MHz.