一类平衡前馈序列的分析

该文指出《一类平衡前馈序列的构造与性质》一文构造的密码系统模型事实上是一个前馈函数为广半Bent函数的滤波-组合模型,并对该模型进行了简单分析。     

一种全数字前馈式时间交织模数转换器时间误差后台校准算法

该文设计实现了一种全数字前馈式时间交织模数转换器(TIADC)时间误差校准算法,其中采样时间误差提取采用改进的时间误差函数求导模块的前馈式提取方法,可以提高在输入信号频率较高时误差提取的准确度;同时,为了降低误差提取单元的复杂性,采用了以减法实现的时间误差函数;最后,采用基于1阶泰勒补偿完成时间误差的实时校正。仿真验证表明,应用于4通道14位TIADC系统,当输入信号为多频信号时,系统动态性能无杂散动态范围(SFDR)从48.6 dB提高到80.7 dB。与传统基于前馈校准结构对比,可以将有效校准输入信号带宽从0.19提高到0.39,提高了校准算法的应用范围。

     

一种动态筛选样本的前向神经网络快速学习算法

从理论上讨论了一类隐含层激励函数满足Mercer条件的前向神经网络学习问题,分析了提高网络学习速度的途径,提出了一种动态筛选样本的前向神经网络快速学习算法。它大大提高了网络学习速度,克服了传统的基于梯度下降的网络学习方法存在的诸多弊端。算法还具有动态确定隐含层神经元数的自构性优点。文中通过具体数值试验验证了上述算法的可行性和优越性。     

Integral combinations of Heavisides

A sufficiently smooth function of d variables that decays fast enough at infinity can be represented pointwise by an integral combination of Heaviside plane waves (i.e., characteristic functions of closed half‐spaces). The weight function in such a representation depends on the derivatives of the represented function. The represen‐tation is proved here by elementary techniques with separate arguments for even and odd d, and unifies and extends various results in the literature (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Partially connected feedforward neural networks on Apollonian networks

This paper presents a novel and data-independent method to construct a type of partially connected feedforward neural network (FNN). The proposed networks, called Apollonian network-based partially connected FNNs (APFNNs), are constructed in terms of the structures of two-dimensional deterministic Apollonian networks. The APFNNs are then applied in various experiments to solve function approximation, forecasting and classification problems. Their results are compared with those generated by partially connected FNNs with random connectivity (RPFNNs), different learning algorithm-based traditional FNNs and other benchmark methods. The results demonstrate that the proposed APFNNs have a good capacity to fit complicated input and output relations, and provide better generalization performance than traditional FNNs and RPFNNs. The APFNNs also demonstrate faster training speed in each epoch than traditional FNNs.     

基于受限玻尔兹曼机的语音带宽扩展

语音带宽扩展是为了提高语音质量,利用语音低频和高频之间的相关性重构语音高频的一种技术。高斯混合模型法是语音带宽技术中被广泛应用的一种方法,但是,由于该方法假设语音高频、低频服从高斯分布,且只表征了语音低频、高频之间的线性关系,从而导致合成的高频语音出现失真。因此,该文提出一种基于受限玻尔兹曼机的方法,该方法利用两个高斯伯努利受限玻尔兹曼机提取语音低频和高频中蕴含的高阶统计特性;并利用前馈神经网络将语音低频高阶统计特性参数映射为高频高阶统计特性参数。这样,通过提取语音低频和高频中蕴含的高阶统计特性,该方法可以深层挖掘语音高频和语音低频之间的实际关系,从而更加准确地模拟频谱包络分布,合成质量更高的语音。客观测试、主观测试结果表明,该方法性能优于传统的高斯混合模型方法。     

基于前馈电流控制斜坡补偿的Boost功率因数校正零交越失真研究

该文提出了一种前馈电流控制的斜坡补偿方法,将该方法引入到临界导通Boost 功率因数校正(PFC)转换器的设计中,以减小零交越失真问题,改善谐波电流和频率对系统的限制。基于临界导通Boost PFC转换器的拓扑结构,理论分析了前馈电流控制斜坡补偿技术对脉冲宽度调制(PWM)信号占空比的调制作用,推导出补偿斜率与输入线电压的关系式,迫使线电压零交越点附近的电流跟随电压变化。仿真和测试结果表明,该方法可有效抑制零交越失真现象,提高系统的动态性能,尤其在高频及轻负载情况下。测得Boost PFC转换器的总谐波失真(THD)仅为3.8%,功率因数0.988,负载调整率3%,线性调整率小于1%,效率达到97.3%。有效芯片面积为1.611.52 mm2。     

基于回溯法的逆推攻击

该文针对基于乘法电路的前馈序列密码模型的分析问题,在Golic逆推攻击算法的基础上,利用回溯法及前馈函数的输入输出相关性,提出了基于回溯法的逆推攻击算法。在解决了回溯法平均计算复杂性的基础上,给出了基于回溯法的逆推攻击算法的平均计算复杂性。新的逆推攻击算法在存储复杂性和平均计算复杂性方面均优于Golic算法。     

Approximation design of optimal controllers for nonlinear systems with sinusoidal disturbances

This paper considers an infinite-time optimal damping control problem for a class of nonlinear systems with sinusoidal disturbances. A successive approximation approach (SAA) is applied to design feedforward and feedback optimal controllers. By using the SAA, the original optimal control problem is transformed into a sequence of nonhomogeneous linear two-point boundary value (TPBV) problems. The existence and uniqueness of the optimal control law are proved. The optimal control law is derived from a Riccati equation, matrix equations and an adjoint vector sequence, which consists of accurate linear feedforward and feedback terms and a nonlinear compensation term. And the nonlinear compensation term is the limit of the adjoint vector sequence. By using a finite term of the adjoint vector sequence, we can get an approximate optimal control law. A numerical example shows that the algorithm is effective and robust with respect to sinusoidal disturbances.     

对“存储采样前馈序列”的评述

评述了文献[1]所构造的所谓“存储采样前馈序列”。