The Set of Hausdorff Continuous Functions— The Largest Linear Space of Interval Functions

Hausdorff continuous (H-continuous) functions are special interval-valued functions which are commonly used in practice, e.g. histograms are such functions. However, in order to avoid arithmetic operations with intervals, such functions are traditionally treated by means of corresponding semi-continuous functions, which are real-valued functions. One difficulty in using H-continuous functions is that, if we add two H-continuous functions that have interval values at same argument using point-wise interval arithmetic, then we may obtain as a result an interval function which is not H-continuous. In this work we define addition so that the set of H-continuous functions is closed under this operation. Moreover, the set of H-continuous functions is turned into a linear space. It has been also proved that this space is the largest linear space of interval functions. These results make H-continuous functions an attractive tool in real analysis and provides a bridge between real and interval analysis.     

Nearly bent functions

In this paper, we define a large class of almost balanced Boolean functions called nearly bent functions. These functions lie at large Hamming distance to all affine functions and preserve a high level of algebraic degree. Then a construction of nearly bent functions is described. It is shown that the constructed functions have good global avalanche characteristic properties. In the concluding remarks, we show that nearly bent functions have good stability.     

The novel generating algorithm and properties of hybrid-P-ary generalized bridge functions

1 Introduction Copy theory is a kind of signal generation technology, built on the research of Walsh function. The main content of the copy theory includes generating non-sine orthogonal functions, utilizing shift and symmetric copy methods and control in…     

Humanoid walking pattern generation based on model predictive control approximated with basis functions

This paper proposes a real-time walking pattern generator (WPG) based on model predictive control (MPC). Since reducing the calculation time is a crucial problem in real-time WPG, we consider introducing basis functions to reduce the number of control input. The control inputs in the MPC are described by a series of basis functions. Compared with the standard discrete-time MPC formulation, the approach with basis functions requires fewer optimization variables at the cost of decreasing precision. In order to find an appropriate trade-off, two basis functions named Laguerre functions and Haar functions, are tested in this paper. MPC with Laguerre functions decreases more computational load while MPC with Haar functions offers a more accurate solution. The approach is not restricted to Laguerre functions or Haar functions, users can select their own basis functions for different applications and preferences.     

雪崩布尔函数的构造方法及个数估计

研究了满足严格雪崩准则布尔函数的性质,证明了雪崩布尔函数的汉明重量只能为偶数,并且得到了雪崩布尔函数的汉明重量之集,给出了不同汉明重量的雪崩布尔函数的构造方法。改进了雪崩布尔函数个数的下界。     

Gbent函数的构造

基于研究布尔函数在子空间的限制,得到关于Gbent函数的一个充分必要条件。给出了两类简单的正则的Gbent函数。在此基础上,通过间接构造Bent函数的方法,利用已知的Gbent函数构造出了更多的Gbent函数。     

二进神经网络中SP函数的一般判别和构造方法

SP函数是一类具有明确逻辑意义的线性可分结构系,PSP函数是SP函数的特殊子集. 文中讨论了二进神经元对SP函数和PSP函数的表达问题,通过研究PSP函数分类超平面的某 些性质,建立了SP函数和PSP函数的一般判别和构造方法.     

互补对称布尔函数的非线性度

互补对称布尔函数是一类特殊的对称布尔函数。在所有代数免疫最优的对称布尔函数中,有相当的比例均属此类函数。特别是当变元数量为2m元时,有2/3比例的代数免疫最优对称布尔函数都是互补对称布尔函数。通过布尔函数非线性度、Walsh谱和Krawtchouk多项式间的关系,计算出互补对称布尔函数的非线性度。结果表明,任意n元互补对称布尔函数的非线性度为2n-1-1/2[nn/2]     

由符号函数与绝对值函数派生的非线性函数

人工智能控制中逻辑判断必不可少。因为可以采用简单的非线性函数进行逻辑判断,所以在实际应用中,可以用适当构造的非线性函数来实现部分的智能控制。因此,提出了一种采用符号函数和绝对值函数构造特殊非线性函数的方法,并讨论了利用这些特殊的非线性函数逼近任意的第一类非连续函数的问题。     

支持向量机的5阶光滑函数及其性能分析

光滑函数在支持向量机中起着重要作用,熊金志等人用插值函数的方法导出了一个递推公式,得到了一类新的光滑函数,解决了一个关于是否存在以及如何寻求性能更好的光滑函数的问题。然而其中五阶光滑的多项式函数还未进行性能分析。首先推导出了该光滑函数的表达式,然后分析了它的若干性能。结果表明,该光滑函数具有良好的逼近性能,其逼近精度高于以往的光滑函数。