Non-dominance and attitudinal prioritisation methods for intuitionistic and interval-valued intuitionistic fuzzy preference relations

A novel intuitionistic fuzzy set (IFS) score function and an intuitionistic fuzzy preference relation (IFPR) quantifier guided non-dominance based prioritisation method are introduced. Based on Yager’s continuous OWA (COWA) operator, the interval-valued intuitionistic fuzzy COWA (IVIF-COWA) operator is defined, and a new attitudinal expected score function for interval-valued intuitionistic fuzzy numbers (IVIFNs) is introduced. The novelty of this attitudinal expected score function is that it allows the comparison of IVIFNs by taking into account of the decision makers’ attitudinal character. Moreover, we show that the new attitudinal expected score function extends: (i) the IFS score function introduced in this paper, which is mathematically equivalent to Chen and Tan’s score function (Chen and Tan, 1994); and (ii) Xu and Chen’s score function for IVIFNs (Xu and Chen, 2007). Using the proposed score functions, a method is developed to construct FPRs from a given IFPR and IVIFPR, respectively. When the hesitancy degree function is null, we prove that the score FPRs coincide with their respective IFPR and IVIFPR. Finally, a ranking sensitivity analysis of the attitudinal expected score function with respect to the attitudinal parameter is provided.     

Function synthesis algorithm based on RTD-based three-variable universal logic gates

Compared with complementary metal–oxide semiconductor (CMOS), the resonant tunneling device (RTD) has better performances; it is the most promising candidate for next-generation integrated circuit devices. The universal logic gate is an important unit circuit because of its powerful logic function, but there are few function synthesis algorithms that can implement an n-variable logical function by RTD-based universal logic gates. In this paper, we propose a new concept, i.e., the truth value matrix. With it a novel disjunctive decomposition algorithm can be used to decompose an arbitrary n-variable logical function into three-variable subset functions. On this basis, a novel function synthesis algorithm is proposed, which can implement arbitrary n-variable logical functions by RTD-based universal threshold logic gates (UTLGs), RTD-based three-variable XOR gates (XOR3s), and RTD-based three-variable universal logic gate (ULG3s). When this proposed function synthesis algorithm is used to implement an n-variable logical function, if the function is a directly disjunctive decomposition one, the circuit structure will be very simple, and if the function is a non-directly disjunctive decomposition one, the circuit structure will be simpler than when using only UTLGs or ULG3s. The proposed function synthesis algorithm is straightforward to program, and with this algorithm it is convenient to implement an arbitrary n-variable logical function by RTD-based universal logic gates.     

Improving the effectiveness of RBF classifier based on a hybrid cost function

This paper presents a new minimum classification error (MCE)–mean square error (MSE) hybrid cost function to enhance the classification ability and speed up the learning process of radial basis function (RBF)-based classifier. Contributed by the MCE function, the proposed cost function enables the RBF-based classifier to achieve an excellent classification performance compared with the conventional MSE function. In addition, certain learning difficulties experienced by the MCE algorithm can be solved in an efficient and simple way. The presented results show that the proposed method exhibits a substantially higher convergence rate compared with the MCE function.     

有限自动机定义函数的线性本原圈积分解

本文讨论有限自动机显表出定义函数ｆ的线性本原圈积分解问题。在该圈积分解之下,函数ｆ被表成线性外函数因子ｆＬ与线性本原内涵数因子ｆＮ的圈积,这里函数ｆＬ定义了一个线性弱可逆有限自动机ＭｆＬ,而函数ｆＮ定义了一个非线性有限自动机ＭｆＮ且其不能再分解成一非平凡的线性外函数与一非线性内函数的圈积。本文证明了一函数ｆ的任两线性本原内因子互为对方的线性本原内因子,从而证明了函数ｆ的线性本原圈积分解的唯一性。本     

A new semismooth Newton method for NCPs based on the penalized KK function

In this paper, based on the Kanzow-Kleinmichel (KK) function, we introduce a new nonlinear complementarity problem (NCP) function: penalized KK function. We show that the function possesses desirable properties similar to those of the KK function. Based on this new NCP function, we reformulate the NCP to a system of semismooth equations. We also propose a new semismooth Levenberg–Marquardt method to solve the system of semismooth equations that employs both trust region techniques and line searches. The global and quadratic convergence properties can be established under very mild assumptions. Numerical results show the effectiveness of the proposed algorithm and also indicate that superior behaviour of the proposed new NCP function.     

数字函数微分算法误差分析

在数字函数发生方法中,微分算法和齐田法形式不同,但就它们的基本特点及发生函数时 的误差而言,两者并没有重要的区别.微分法发生的曲线与给定函数之间的误差包括基本误 差和走步误差.基本误差系微分法发生曲线时实际逼近的函数(称为逼近函数)与给定函数 间的误差.逼近函数可以从给定的函数导出.走步误差为微分法发生曲线与逼近函数间因步 法不同而产生的误差.本文证明了用微分法发生二阶、三阶函数时的基本误差相当大,走步误 差比基本误差要小得多.     

基于Rijindael的安全Hash函数

该文提出了基于分组密码算法Rijindael的安全Hash函数。此Hash函数基于分组长度和密钥长度均为256比特的分组密码算法Rijindael-(256,256),其输出长度为256比特。并且该文证明了此Hash算法抵抗碰撞及作为单向函数的安全性。     

A comparison of the von Mises and Gaussian basis functions for approximating spherical acoustic scatter

This paper compares the approximation accuracy of two basis functions that share a common radial basis function (RBF) neural network architecture used for approximating a known function on the unit sphere. The basis function types considered are that of a new spherical basis function, the von Mises function, and the now well-known Gaussian basis function. Gradient descent learning rules were applied to optimize (learn) the solution for both approximating basis functions. A benchmark approximation problem was used to compare the performance of the two types of basis functions, in this case the mathematical expression for the scattering of an acoustic wave striking a rigid sphere.     

四维飞行轨迹预测方法的研究

通过对空中交通运输管理中目前常用的轨迹预测算法的研究比较和分析,提出了利用遗传算法的从历史数据中进行函数挖掘的思想.针对四维轨迹数据特征的分析和传统的单一函数挖掘的局限性,提出了基于基因表达式编程的频繁函数集挖掘的建模方法.该模型方法通过对历史飞行数据进行遗传算法的操作挖掘出数据集中对应的函数关系集合,用较好的函数模型预测未来航迹.以某一航班雷达数据为训练集做实验,结果表明了应用该方法的准确性和可用性.     

加权稳健支撑向量回归方法

给出一类基于奇异值软剔除的加权稳健支撑向量回归方法(WRSVR)．该方法的基本思想是首先由支撑向量回归方法(SVR)得到一个近似支撑向量回归函数,基于这个近似模型给出了加权SVR目标函数并利用高效的SVR求解技巧得到一个新的近似模型,然后再利用这个新的近似模型重新给出一个加权SVR目标函数并求解得到一个更为精确的近似模型,重复这一过程直至收敛．加权的目的是为了对奇异值进行软剔除．该方法具有思路简捷、稳健性强、容易实现等优点．实验表明,新算法WRSVR比标准SVR方法、稳健支撑向量网(RSVR)方法和加权最小二乘支撑向量机方法(WLS—SVM)更加稳健,算法的逼近精度受奇异值的影响远小于SVM、RSVR和WLS—SVM算法．     

A novel objective function for improved phoneme recognition usingtime-delay neural networks

Single-speaker and multispeaker recognition results are presented for the voice-stop consonants /b,d,g/ using time-delay neural networks (TDNNs) with a number of enhancements, including a new objective function for training these networks. The new objective function, called the classification figure of merit (CFM), differs markedly from the traditional mean-squared-error (MSE) objective function and the related cross entropy (CE) objective function. Where the MSE and CE objective functions seek to minimize the difference between each output node and its ideal activation, the CFM function seeks to maximize the difference between the output activation of the node representing incorrect classifications. A simple arbitration mechanism is used with all three objective functions to achieve a median 30% reduction in the number of misclassifications when compared to TDNNs trained with the traditional MSE back-propagation objective function alone.     

Extremum seeking loops with quadratic functions: Estimation and control

Extremum-seeking (also peak-seeking ) controllers are designed to operate at an a priori unknown set-point that extremizes the value of a performance function. Traditional approaches to the problem assume a time-scale separation between the gradient computation and function minimization and the system dynamics. The work here, in contrast, assumes that the performance function can be approximated by a quadratic function with a finite number of parameters. These parameters are estimated on-line and the extremum seeking controller operates based on these estimated values. A significant advantage of a quadratic function is that it allows the peak-seeking control loop to be reduced to a linear system. For such a loop, the wealth of linear system analysis and synthesis tools can be employed. First, the control loop is analysed assuming that the parameters in the function are known (full information case) and then when the parameters are estimated on line (the partial information case).     

Evaluation of a set of new ORF kernel functions of SVM for speech recognition

The kernel function is the core of the Support Vector Machine (SVM), and its selection directly affects the performance of SVM. There has been no theoretical basis on choosing a kernel function for speech recognition. In order to improve the learning ability and generalization ability of SVM for speech recognition, this paper presents the Optimal Relaxation Factor (ORF) kernel function, which is a set of new SVM kernel functions for speech recognition, and proves that the ORF function is a Mercer kernel function. The experiments show the ORF kernel function's effectiveness on mapping trend, bi-spiral, and speech recognition problems. The paper draws the conclusion that the ORF kernel function performs better than the Radial Basis Function (RBF), the Exponential Radial Basis Function (ERBF) and the Kernel with Moderate Decreasing (KMOD). Furthermore, the results of speech recognition with the ORF kernel function illustrate higher recognition accuracy.     

最小二乘Littlewood-Paley小波支持向量机

基于小波分解理论和支持向量机核函数的条件，提出了一种多维允许支持向量核函数——Littlewood-Paley小波核函数．该核函数不仅具有平移正交性，而且可以以其正交性逼近二次可积空间上的任意曲线，从而提升了支持向量机的泛化性能．在Littlewood-Paley小波函数作为支持向量核函数的基础上，提出了最小二乘Littlewood-Paley小波支持向量机(LS-LPWSVM)．实验结果表明，LS-LPWSVM在同等条件下比最小二乘支持向量机的学习精度要高，因而更适用于复杂函数的学习问题．     

磁共振成像重建的新算法

提出了一种高效的磁共振成像重建算法,将磁共振成像重建看作标准的线性求逆问题,基于变量分离技术重新建立线性求逆问题的目标函数,用交替最小化方法处理新目标函数的优化问题.新目标函数的优化包含两个交替的过程:一个凸的光滑函数的优化和一个凸的非光滑函数的优化.为处理非光滑函数的优化问题,引入投影算法对其求解.实验结果表明了该磁共振成像重建算法的有效性,与同类算法性比,重建的磁共振图像的均方误差(MSE)最小,重建的速度最快.     

基于变换函数与填充函数的模糊粒子群优化算法

本文提出了一种基于变换函数与填充函数的模糊粒子群优化算法（Fuzzy partical swarm optimization based on filled function and transformation function,FPSO-TF）.以基于不同隶属度函数的多回路模糊控制系统为基础,进一步结合变换函数与填充函数,使该算法减少了陷入局部最优的可能,又可以跳出局部极小值点至更小的点,快速高效地搜索到全局最优解.最后采用基准函数对此算法进行测试,并与几种不同类型的改进算法进行对比分析,验证了此算法的有效性与优越性.     

基于Hash函数的报文鉴别方法

基于当前网络通信中对报文鉴别码(MAC)的需求，介绍了Hash函数在密码学上的安全性质，分析了Hash函数在报文鉴别中的应用和针对Hash函数的主要攻击。在此基础之上，提出一种基于Hash函数的报文鉴别码——伪报文鉴别码(PMAC)。利用当前现有的Hash函数来构造MAC，而不改变原有的Hash函数的内部结构。在没有利用任何现有加密算法的基础上，仅应用一个密钥不仅对报文提供了鉴别，而且也提供了机密性。对该伪报文鉴别算法的安全性进行了初步分析。     

Adaptive function allocation stabilization and a comparison of trigger types and adaptation strategies

Two experiments were conducted to (1) test and quantify the effect of an adaptive function allocation system on human performance under different combinations of trigger type (heart rate vs. performance-based) and function allocation adaptation strategy (complete reallocation, partial reallocation, partial transformation) and (2) to determine if the adaptive function allocation system continues to actively change the level of automation over relatively long periods of time (30 min). It was found that the adaptive function allocation system improves primary task performance by, on average, 6% and does not improve secondary task performance. In addition, the level of automation did not stabilize over the 30 min, suggesting the adaptive function a system continues to be relevant even over longer periods of time. Lastly, the study found that the use of heart rate as a trigger mechanism resulted in many more reallocations of function than a performance-based measure.     

Strong Lyapunov functions for systems satisfying the conditions of La salle

We present a construction of a (strong) Lyapunov function whose derivative is negative definite along the solutions of the system using another (weak) Lyapunov function whose derivative along the solutions of the system is negative semidefinite. The construction can be carried out if a Lie algebraic condition that involves the (weak) Lyapunov function and the system vector field is satisfied. Our main result extends to general nonlinear systems the strong Lyapunov function construction presented in a previous paper that was valid only for homogeneous systems.