基于几何参数的模糊拉格朗日插值推理

在稀疏规则库条件下，当给定的输入落入规则“间隙”时，采用传统的模糊推理方法是得不到任何结论的。模糊推理本质上就是插值器。Koczy和Hirota首先提出了KH线性插值推理方法，然而推理结果存在着无法保证凸性和正规性等问题。为了能有一个较好的插值推理结果，本文提出了一种基于几何参数的模糊拉格朗日插值推理方法，该方法不仅推理简单，推理结果较好，并且能很好地保证推理结果的凸性和正规性。这为智能系统中的模糊推理提供了一个非常有用的工具。     

稀疏规则条件下的相似插值推理研究

模糊推理本质上就是插值器。但在稀疏规则库的务件下，当输入的事实落入规则“空隙”时，采用传统的CRI方法是得不到任何推理结果的。而采用KH线性插值推理也存在着难以保证推理结果的凸性和正规性等问题。为了在稀疏规则条件下能有好的插值推理结果，提出了一种相似插值推理方法。谊方法能较好地保证推理结果隶属函数的凸性和正规性，这为智能系统中的模糊推理提供了一个十分有用的工具。     

基于几何相似的模糊插值推理

模糊推理中广泛采用的合成推理规则(CRI)要求规则库必须是稠密的。但在稀疏规则库的条件下，当输入的事实落入规则“空隙”时，采用传统的CRI方法是得不到任何推理结果的。为此Koczy和Hirota首先提出了KH线性插值推理方法，但推理结果存在着凸性和正规性等问题。许多学者叉提出了不同的插值推理方法，它们有不同的特点，但从实际应用角度看，都还比较复杂。Koczy等叉提出了改进的KH插值推理方法，这种方法虽然保证了凸性和正规性，但推理结果不够好。本文中我们提出了一种基于几何相似的插值推理方法。该方法能很好地保证推理结果的凸性和正规性，并且推理简单，推理结果较好。这为智能系统中的模糊推理提供了一个十分有用的工具。     

一种基于核集与相似性的模糊推理方法

在稀疏规则库条件下,当给定的输入落入规则"间隙"时,采用传统的模糊推理方法是得不到任何结论的.学者已经证明模糊推理本质上就是插值器.Koczy和Hirota首先提出了KH线性插值推理方法,然而推理结果存在着无法保证凸性和正规性等问题.为了能有一个较好的插值推理结果,本文提出了一种基于核集与相似性的模糊插值推理方法,并把此方法扩展到多维变量的情况,该方法不仅推理简单,推理结果较好,并且能很好地保证推理结果的凸性和正规性.这为智能系统中的模糊推理提供了一个非常有用的工具.     

多变量规则的线性插值推理方法

插值推理是稀疏规则条件下的一类重要的推理方法，单变量的情况已有较多研究，但针对多变量情况的研究还不多，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题。多变量规则的插值推理是插值推理研究的重要方面，为了在多变量稀疏规则条件下能得到好的插值推理结果，本文对多变量规则的插值推理方法进行了研究，提出了一个多变量规则的线性插值推理方法。该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具。     

一种新的多维稀疏规则模糊插值推理方法

经典的插值理论针对一维稀疏规则库的条件，提出了各种不同的插值方法，取得了很多很好的经验．但对多维稀疏规则条件的近似推理，研究很少，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题．为了在多维稀疏规则条件下能得到好的插值推理结果。提出了一种基于几何相似的插值推理方法．该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具．     

稀疏规则库条件下的模糊推理方法

当模糊规则库是稀疏型时,利用Kóczy线性插值推理方法不能保证推理结论的正规性和凸性,为了解决这一问题,石岩曾提出了插值推理方法的推理条件,当满足这些条件时利用Kóczy线性插值推理方法得到的推理结论也满足正规性和凸性;但是这些条件却限制了模糊推理系统的应用,而且如果多次推理中在同一输入点遇到稀疏情况,必须进行相同的计算才能得到正确的推理结果,这样增加了系统的计算量,降低了系统的速度和效率.因此提出了一种新的稀疏模糊推理方法,不仅能够简单的给出正确的推理结果,还能在相应的位置增加规则,提高规则库的紧密程度.     

基于优序图加权的多维稀疏模糊推理方法

在稀疏规则库条件下,多数的多维稀疏规则条件近似推理方法都难以保证推理结果的凸性和正规性,且没有考虑到多维变量对结论的影响权值。提出一种基于优序图加权的多维模糊推理方法,运用优序图确定权值,实验结果表明,该方法不仅减小推理结果的误差,而且能较好地保证推理结果的凸性和正规性。     

一种基于泰勒级数的简化的Kóczy线性插值推理方法

在传统的模糊推理方法中,如果出现模糊规则库稀疏的情况,模糊推理就得不出正确的推理结论.针对这个问题,Kóczy和Hirota提出了一种线性插值推理方法.线性插值推理方法解决了稀疏规则库情况下如何得出推理结论的问题,但是,用这种方法得出的结论有时是不正规的模糊集.本文提出的基于泰勒级数的Kóczy线性插值推理方法,能保证“当模糊规则A1= >B1 ,A2 =>B2 和推理前件A* 是正规的线性隶属函数(三角形或者梯形)时,插值推理结论B* 也是正规的线性隶属函数(三角形或者梯形)”.     

一种基于模糊神经网络加权的多维稀疏模糊推理方法

在稀疏规则库条件下,经典的插值理论针对一维稀疏规则库提出了各种不同的插值方法,取得了很多很好的经验;但对多维稀疏规则条件的近似推理研究很少,不仅存在着难以保证推理结果的凸性和正规性等问题,而且没有考虑到多维变量之间的联系即对结论的影响权值,造成推理结果的误差性更大.多变量规则的模糊插值推理是插值推理研究的重要方面,为了在多变量稀疏规则条件下得到好的插值推理效果,本文提出了一种基于模糊神经网络加权的多维模糊推理方法,为智能系统中的模糊推理提供了一个十分有用的工具.     

高速视频数据光纤传输系统的物理层实现

在实际的工程应用中要将不同性质的数字视频信号分时复用,通过单根光纤将各种信号从光电跟踪设备旋转的机上探测机构传输到机下数据处理单元,而数据的并发性和数据位宽的不同会造成数据传输不连续、带宽资源浪费等问题。针对上述问题,提出恢复视频信息中行场信号的方案,给出系统物理层协议的实现,结合物理层编解码芯片的特点,采用恢复系统中时钟信号以及8B／10B编解码方法,使系统传输速率提高到2．0Gb／s,误码率小于10^-12。实验结果证明,该方法可以有效解决传输总线不匹配问题,使所有数据并行传输。     

Fuzzy Interpolative Reasoning for Sparse Fuzzy-Rule-Based Systems Based on the Areas of Fuzzy Sets

Fuzzy interpolative reasoning is an inference technique for dealing with the sparse rules problem in sparse fuzzy-rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method for sparse fuzzy-rule-based systems based on the areas of fuzzy sets. The proposed method uses the weighted average method to infer the fuzzy interpolative reasoning results and has the following advantages: 1) it holds the normality and the convexity of the fuzzy interpolative reasoning result, 2) it can deal with fuzzy interpolative reasoning with complicated membership functions, 3) it can deal with fuzzy interpolative reasoning when the fuzzy sets of the antecedents and the consequents of the fuzzy rules have different kinds of membership functions, 4) it can handle fuzzy interpolative reasoning with multiple antecedent variables, 5) it can handle fuzzy interpolative reasoning with multiple fuzzy rules, and 6) it can handle fuzzy interpolative reasoning with logically consistent properties with respect to the ratios of fuzziness. We use some examples to compare the fuzzy interpolative reasoning results of the proposed method with those of the existing fuzzy interpolative reasoning methods. In terms of the six evaluation indices, the experimental results show that the proposed method performs more reasonably than the existing methods. The proposed method provides us a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy-rule-based systems.      

Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets

Fuzzy interpolative reasoning is an important research topic of sparse fuzzy rule-based systems. In recent years, some methods have been presented for dealing with fuzzy interpolative reasoning. However, the involving fuzzy sets appearing in the antecedents of fuzzy rules of the existing fuzzy interpolative reasoning methods must be normal and non-overlapping. Moreover, the reasoning conclusions of the existing fuzzy interpolative reasoning methods sometimes become abnormal fuzzy sets. In this paper, in order to overcome the drawbacks of the existing fuzzy interpolative reasoning methods, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets. The proposed fuzzy interpolative reasoning method can handle the situation of non-normal and overlapping fuzzy sets appearing in the antecedents of fuzzy rules. It can overcome the drawbacks of the existing fuzzy interpolative reasoning methods in sparse fuzzy rule-based systems.     

Fuzzy interpolative reasoning via scale and move transformations

Interpolative reasoning does not only help reduce the complexity of fuzzy models but also makes inference in sparse rule-based systems possible. This paper presents an interpolative reasoning method by means of scale and move transformations. It can be used to interpolate fuzzy rules involving complex polygon, Gaussian or other bell-shaped fuzzy membership functions. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using scale and move transformations to convert the intermediate inference results into the final derived conclusions. This method has three advantages thanks to the proposed transformations: 1) it can handle interpolation of multiple antecedent variables with simple computation; 2) it guarantees the uniqueness as well as normality and convexity of the resulting interpolated fuzzy sets; and 3) it suggests a variety of definitions for representative values, providing a degree of freedom to meet different requirements. Comparative experimental studies are provided to demonstrate the potential of this method.     

Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on ${bm alpha}$-Cuts and Transformations Techniques

In sparse fuzzy rule-based systems, the fuzzy rule bases are usually incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. The proposed method provides a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.