BP算法改进及在控制系统中应用

研究控制问题,为了解决系统的稳定性和系统的精度,采用模糊控制方法,对模糊隶属度函数输入域上致密分布的要求,采用隶属函数约束寻优对基于BP算法的模糊神经网络进行了改进。算法首先采用-S函数对输入变量进行非线性映射,函数在把输入变量映射人确定区域的同时最大程度上保留了原样本的信息,然后根据经验知识给出隶属函数参数的优化范围,保证了模糊变量在输入域上的致密分布。经过仿真实验,仿真结果取得了与理论分析一致的实验结果,保证系统的稳定性。     

一种新型区间二型模糊神经网络隶属函数的设计

对于区间二型模糊神经网络（IT2FNN）,论文给出了一种新型的模糊隶属函数（FMF）设计方法.通过所设计的模糊隶属函数,可以衍生出三种区间二型模糊隶属函数（IT2FMF）.每种区间二型模糊隶属函数都具有不同的不确定域.论文将三种衍生模糊隶属函数应用于简化区间二型模糊神经网络辨识两个非线性系统.通过仿真,将衍生区间二型模糊隶属函数的辨识性能与高斯和椭圆型模糊隶属函数进行了对比.仿真结果表明,通过调节简化区间二型模糊神经网络的参数,本文所设计的区间二型模糊隶属函数比高斯和椭圆型模糊隶属函数具有更好的辨识性能.     

正态云隶属度函数确定的FSM方法

隶属度函数及其确定方法的研究具有重要意义。采用正态云模型表示的隶属度函数综合了模糊性和随机性,具有普适性;提出了确定隶属度函数的模糊减法均值聚类(FSM)方法,得到了最优聚类中心和数据的隶属度。实例仿真表明,采用该方法确定了石油钻井中总体积的正态云隶属度函数,解决了隶属度函数难以客观描述和难以确定的问题。     

FRBF神经网络分类器设计新方法

提出了一种结合模糊径向基函数网络和稀疏V-SVM的二分类器构建方法。FRBF初始网络中的RBF隶属度函数中心由随机抽取的样本确定,而RBF隶属度函数的宽度由样本各个属性的分布方差确定。根据FRBF网络输出为模糊基函数线性组合的特点,在后件参数学习中引入具有结构风险最小化和属性选择功能的稀疏V-SVM方法,在对输出层的参数进行学习的同时进行模糊基函数的约简。若干UCI标准数据集分类测试结果验证了该分类器的有效性。     

学习矢量量化的软竞争算法

尽管FALVQ算法的亏损因子为模糊隶属度函数,但由于它的尺度函数并不是模糊隶属度函数,使得算法的性能不稳定.为了克服这个问题,通过推广FALVQ中获胜亏损因子的定义,导出了广义LVQ的一类软竞争算法(SCALVQ),并且给出了它的3种具体形式.在SCALVQ中,亏损因子和对应的尺度函数是同一个模糊隶属度函数,它汲取了FALVQ和软竞争格式的优点,有效地克服了FALVQ存在的问题.     

一种保证隶属度函数完备性和模糊集合语义一致性的学习方法

文中首先对模糊系统的两类主要的学习算法：梯度下降和遗传算法方法进行了深入分析,并指出了存在的问题．然后,在此基础上提出了一种针对半梯形和三角形隶属度函数的保证隶属度函数ε完备性和模糊集语义一致性的参数调整方法．并基于上述方法实现了一种新的基于遗传算法并利用梯度下降的快速模糊系统学习算法．最后通过实例进行了模拟,验证了该方法的高效性,以及保证隶属度函数完备性和模糊集合语义一致性的优点     

基于Mean-shift算法与模糊熵的图像平滑

为解决均值漂移算法的滤波核带宽的选择问题,通过分析模糊理论的模糊隶属度函数和Mean-shift算法的核函数的定义,指出可将模糊隶属度函数作为Mean-shift的核函数。由此定义新隶属度函数作为表示灰度信息的核函数,应用Mean-shift算法对一幅混有噪声的细胞图像进行平滑处理。通过实验表明该方法能到达较好的平滑效果且不需要选择核带宽hr。     

模糊多核支持向量机研究进展

模糊多核支持向量机将模糊支持向量机与多核学习方法结合,通过构造隶属度函数和利用多个核函数的组合形式有效缓解了传统支持向量机模型对噪声数据敏感和多源异构数据学习困难等问题,广泛应用于模式识别和人工智能领域.综述了模糊多核支持向量机的理论基础及其研究现状,详细介绍模糊多核支持向量机中的关键问题,即模糊隶属度函数设计与多核学习方法,最后对模糊多核支持向量机算法未来的研究进行展望.     

自适应神经网络模糊推理系统最优参数的研究

模糊规则的提取和隶属度函数的学习是模糊系统设计中重要而困难的问题。自适应神经网络模糊推理系统（ANFIS）能基于数据建模,无须专家经验,自动产生模糊规则和调整隶属度函数。在建立一个初始系统进行训练时,其隶属度函数的类型、隶属度函数的数日以及训练次数都是待定的,这三个参数的选择直接影响系统训练后的效果,它们的确定方法有待研究。该文应用自适应神经网络模糊推理系统的方法对一个典型系统进行建模仿真,并阐述这三个参数的寻优方法。     

基于混合人工鱼群的图像模糊增强算法

模糊隶属度函数的形式直接影响灰度图像增强的质量,为进一步改善图像模糊增强的效果,对目前的模糊隶属度函数进行研究,并提出一种改进的参数化s型模糊隶属度函数用于图像增强;所提算法利用图像对比度的质量评价模型,结合人工鱼群算法和Powell算法搜索s型函数中的未知参数值,进而确定该模糊隶属度函数;通过实验结果表明:该算法能够较好地改善灰度图像质量,并且控制参数可通过优化算法自适应获得,具有较好的通用性,是一种有效的图像模糊增强算法。     

高阶CMAC神经网络的研究

提出了一种高阶ＣＭＡＣ（ＨＣＭＡＣ）神经网络，它是采用高阶的径向基函数作为接收域函数，为了进一步增强对输入模式的表达，还可以用接收域函数输入模式向量构成张量积，这时产生的是高维的增强表达，同时ＨＣＭＡＣ沿用ＣＭＡＣ的地址映射方法，由于高阶接收域函数的引入，使其可以获得较ＣＭＡＣ连续性强且有解析微分的复杂函数近似，ＨＣＭＡＣ在不改变ＣＭＡＣ简单结构的基础上较ＲＢＦ网络有计算量少，学习效率高等优点，中     

Fuzzy system and CMAC network with B-spline membership/basis functions are smooth approximators

Due to their universal approximation, fuzzy system with B-spline membership functions and CMAC neural network with B-spline basis functions have been extensively used in control. In many practical applications, they are desired to approximate not only the assigned smooth function as well as its derivatives. In this paper, by designing a fuzzy system and CMAC neural network with B-spline basis functions, we prove that such a fuzzy system and CMAC can universally approximate a smooth function and its derivatives, i.e, for a given accuracy, we can approximate an arbitrary smooth function by such a fuzzy system and CMAC that not only the function is approximate within this accuracy, but its derivatives are approximated as well. The conclusions here provide solid theoretical foundation for their extensive applications. The authors would like to thank the referees for their invaluable suggestions.     

Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems

This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.     

用多个改进低维CMAC神经网络学习多维函数的研究

提出了一种多维非线性函数的多神经网络学习方法。即用变量代换的方法把一个多维非线性函数分解为若干低维函数。用多个改进的低维小脑模型神经网络分别映射这些低维函数。提高了收敛性。减少了存储空间。大大提高了学习精度。且易于实现。给出了大量学习非线性函数的仿真实验，其结果表明。采用这种方法的学习精度比用一个CMAC的学习精度提高l0倍以上。     

Discrete-time CMAC NN control of feedback linearizable nonlinearsystems under a persistence of excitation

The local structure of CMAC neural networks (NN) result in better and faster controllers for nonlinear dynamical systems. A CMAC neural network-based discrete-time controller which linearizes the unknown multiinput and multioutput nonlinear system through feedback is presented. Control action is defined in order to achieve tracking performance for this unknown nonlinear system. An efficient and localized weight addressing scheme for the CMAC NNs is described using an appropriate choice of the B-spline receptive field functions that form a basis. A uniform ultimate boundedness of the closed-loop system is given in the sense of Lyapunov using the persistency of excitation condition. Simulation results are shown to demonstrate the theoretical conclusions.     

模糊神经元及其在FCMAC中的应用

本文通过对模糊集合与神经网络的特点及相互关系的阐述，引出将二者优点结合于一身的模糊神经网络理论。从模糊神经元开始，着重介绍其拓扑结构、分类、隶属函数的特点及相应激励函数的确定方法。最后以模糊小脑神经网络(FCMAC)为算例，阐述了模糊神经元的应用问题。仿真结果表明：它具有比常规CMAC学习速度快，结果精确等优势。     

Zoning methods for handwritten character recognition: A survey

This paper presents a survey on zoning methods for handwritten character recognition. Through the analysis of the relevant literature in the field, the most valuable zoning methods are presented in terms of both topologies and membership functions. Throughout the paper, diverse zoning topologies are presented based on both static and adaptive approaches. Concerning static approaches, uniform and non-uniform zoning strategies are discussed. When adaptive zonings are considered, manual and automatic strategies for optimal zoning design are illustrated as well as the most appropriate zoning representation techniques. In addition, the role of membership functions for zoning-based classification is highlighted and the diverse approaches to membership function selection are presented. Concerning global membership functions, the paper introduces order-based approaches as well as fuzzy approaches using border-based and ranked-based fuzzy membership values. Concerning local membership functions, the recent parameter-based approaches are described, in which the optimal membership-function is selected for each zone of the zoning method. Finally, a comparative analysis on the performance of zoning methods is presented and the most interesting approaches are focused on in terms of topology design and membership function selection. A list of selected references is provided as a useful tool for interested researchers working in the field.     

模糊小脑模型神经网络

提出输入层具有一定隶属度的模糊小脑模型神经网络(Fuzzy CMAC),它比小脑 模型CMAC(Cerebellar Model Articulation Controller)能更真实地描述客观世界.给出n维 Fuzzy CMAC算法,仿真结果表明Fuzzy CMAC比小脑模型CMAC具有如下优点:学习收敛 速度快得多,可以学习模糊规则.Fuzzy CMAC比CMAC优越,使CMAC成为Fuzzy CMAC 的特例.     

Fuzzy model based recognition of handwritten numerals

This paper presents the recognition of handwritten Hindi and English numerals by representing them in the form of exponential membership functions which serve as a fuzzy model. The recognition is carried out by modifying the exponential membership functions fitted to the fuzzy sets. These fuzzy sets are derived from features consisting of normalized distances obtained using the Box approach. The membership function is modified by two structural parameters that are estimated by optimizing the entropy subject to the attainment of membership function to unity. The overall recognition rate is found to be 95% for Hindi numerals and 98.4% for English numerals.     

Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions

The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness.