A parallelized artificial immune network for fuzzy clustering

An artificial immune network (AIN), AINFCM, has been successfully used for fuzzy clustering to overcome the shortage of FCM algorithm that is sensitive to the selection of initial centres. However, as a stochastic searching algorithm, the runtime of AINFCM goes up especially when dealing with large quantities of data or generating much more antibodies for clone selection. In this paper, the PAINFCM is proposed for parallel affinity calculation of antibodies according to time complexity of AINFCM algorithm. Subsequently, a coarse-grained version of PAINFCM algorithm was proposed to parallelize clone expansion. Experiments indicated that the PAINFCM improves efficiency of AIN. Furthermore, it provides a good balance between global searching ability and run time of the AIN.     

Adaptive fuzzy output-feedback fault-tolerant tracking control of a class of uncertain nonlinear switched systems

ABSTRACT

This paper considers the output-feedback fault-tolerant tracking control problem for a class of uncertain nonlinear switched systems with nonlinear faults and strict-feedback form, where the faults which are nonaffine occur on the actuator. As a kind of specialised function approximating tool, fuzzy logic systems (FLSs), are employed to approximate the unknown smooth nonlinear functions. A switched fuzzy observer is designed to address the problem of unmeasurable states, filtered signals are used to address algebraic loop problem and the average dwell time (ADT) method is further utilised to prove the stability of the resulting closed-loop systems under a type of slowly switching signals. Based on the backstepping recursive design technique and Lyapunov function method, an adaptive fuzzy output-feedback control scheme is developed. The developed control method can ensure all the signals are semi-globally uniformly ultimately bounded (SGUUB) and the system output tracks the reference signal tightly even if unknown fault occurs. A simulation carried on an example demonstrates the validity of the obtained control scheme.     

基于EM算法的混合模型医学图像分割

医学图像分割中的期望最大化(EM)算法在求解混合模型参数时存在局限性。为此,提出一种模糊约束的混合模型图像分割算法。该算法以像素的独立性假设为前提,在采用EM算法对模型参数进行求解的过程中,通过模糊集合论方法,引入像素空间信息。实验结果表明,该算法没有引入新的模型参数,能够保持独立混合模型的简单性,且具有自动模型选择能力,可以获得较理想的分割结果。     

神经模糊PID控制算法在恒压供水系统中的应用

针对恒压供水系统的大迟滞、非线性特点,提出了神经模糊PID控制算法,利用被控量偏差及偏差的变化,在线调整PID参数.通过仿真分析得出,神经模糊PID控制算法的效果较之传统PID控制和模糊PID控制具有更低的超调和更快的反应速度.     

模糊综合评价法在供水管网安全评价中的应用

针对供水管网的安全隐患及特点,将模糊理论与综合评价方法相结合,通过MATLAB7.7的GUI界面,建立了可视化、直观性的安全模糊综合评价系统.进而以北方某市区域供水管网的安全评价为例,结合实际数据,建立了行之有效的3个层次、7个评价指标的评价体系,计算了该区域供水管网的安全等级.     

复合量化多目标方法在维修力量派遣决策中的应用

针对部队装备维修决策特点,从多目标决策需求出发,结合三角模糊数结构特点,考虑非平衡指派和任务紧急程度,建立装备维修力量模糊多目标指派模型,用水平效应及不确定度对决策意识的模糊性进行描述并加以量化,应用改进遗传算法求多目标模糊派遣模型的最优决策解。实例仿真表明,其计算结果符合实际情况,其最优结果可为决策者提供决策依据,帮助提高维修保障调配的适时性和任务执行的有效性,充分发挥装备维修力量的作用。     

基于正态信息扩散原理的路基沉降模糊可靠度分析

基于正态信息理论,给出了路基沉降概率分布的拟合方法,并利用精度较高K-S检验法验证了该方法的正确性。将Beta分布引入路基沉降模糊失效事件的隶属函数中,解决了常用隶属函数存在的不足。同时,利用模糊可靠度理论,给出了路基沉降模糊可靠度的计算公式。最后通过工程实例验证了本文方法的有效性。     

基于模糊语言变量的综合评判模型及其工程应用

提出一种模糊综合评判模型．该模型在权重赋值、评价集和单因素评判三个方面利用模糊语言变量表达决策者的观点,并用一种带可信度的梯形模糊数来量化模糊语言变量．举例说明该模型的工程应用方法．     

模糊RBF神经网络在麻醉深度控制系统中的应用

麻醉深度通常用病人的平均动脉压（MAP）值来直接反应和度量。针对平均动脉压的时变、非线性特点,提出了基于模糊RBF神经网络的麻醉深度PID控制系统。通过采用模糊RBF神经网络对检测到的平均动脉压值进行模糊化处理及神经网络辨识,从而在线整定PID控制器各个参数,以获得更好的控制效果。MATLAB仿真结果表明模糊RBF神经网络用于麻醉深度控制具有良好的动态响应性能。