混沌多值序列用于光码分多址系统的性能分析

针对光码分多址(OCDMA)系统对地址码的要求,提出了一种利用3阶Chebyshev混沌多值序列编码的方案,给出了码字的生成方法;基于相移超结构光纤布拉格光栅(SSFBG)编/解码器,利用传输矩阵法仿真分析了系统相关特性与码字长度、量化级数的关系.结果表明,多值混沌序列用于OCDMA频谱相位编码是可行的.     

应用粒子群优化的非线性系统辨识

提出了一种应用粒子群优化的非线性系统辨识方法。首先将非线性系统的辨识问题转化为参数空间上的优化问题,然后利用粒子群优化算法对整个参数空间进行高效并行搜索以获得系统参数的最优估计。以Hammerstein模型的辨识为例说明了本方法的可行性。     

一种复值函数型连接神经网络

本文提出了一种复值函数型连接神经网络(CFLNN)结构,可以对复数域信号进行快速处理。函数型连接神经网络通过对输入模式预先进行非线性扩展,增强了输入信号的模式表达,从而可以大为简化网络结构,降低计算复杂度。本文将函数型连接神经网络推广到了复值情况并给出了基于梯度下降的学习方法。计算复杂度分析显示本方法具有结构简单,计算量低的优点。最后,将本方法运用到对复值非线性系统的辩识问题中,仿真实验表明本CFLNN性能与传统复值前馈神经网络相近或更优。     

Deterministic Sensing Matrices Based on Multidimensional Pseudo-Random Sequences

An approach is proposed for producing compressed sensing (CS) matrices via multidimensional pseudo-random sequences. The columns of these matrices are binary Gold code vectors where zeros are replaced by ?1. This technique is mainly applied to restore sub-Nyquist-sampled sparse signals, especially image reconstruction using block CS. First, for the specific requirements of message length and compression ratio, a set Λ which includes all preferred pairs of m-sequences is obtained by a searching algorithm. Then a sensing matrix A _M×N is produced by using structured hardware circuits. In order to better characterize the correlation between any two columns of A, the average coherence is defined and the restricted isometry property (RIP) condition is described accordingly. This RIP condition has strong adaptability to different sparse signals. The experimental results show that with constant values of N and M, the sparsity bound of A is higher than that of a random matrix. Also, the recovery probability may have a maximum increase of 20 % in a noisy environment.     

映射序列扩频通信方式及其特性

同时传送多路数据一直是通信的基本课题，本文提出用３个被调制的扩频序列处理成映射序列进行扩频通信，能同时传送３路信息数据，理论分析表明，在不改变常规扩频接收机的情况下，能可靠解出同时传送来的任何一路信息数据，它比８ＰＳＫ、８ＱＡＭ调制方式具有更好的通信特性。     

用确定性的有限自动机进行图像压缩编码

本文提出用确定性的有限自动机（ＤＦＡ）进行图像压缩编码的方法。对一幅输入的数字化灰度图像，检测其中的自相似性，该图像可被表示成一个确定的有限自动机（ＤＰＡ）。ＤＦＡ的尺寸小于原图像。解码算法可以非常高效地由ＤＦＡ复原图像。     

Parameter Identification Methods for an Additive Nonlinear System

This paper deals with parameter identification methods for an additive nonlinear system with a preload nonlinearity and a piece-wise nonlinearity. By using a switching function, we transfer the model of the additive nonlinear system into an identification model, and propose a recursive least squares algorithm and two modified stochastic gradient (SG) algorithms to estimate the parameters of the identification model. The simulation results indicate that the proposed methods converge faster than the SG algorithm.     

Deterministic Blind Channel Estimation for a Block Transmission System Using Fractional Sampling and Interpolation

This paper introduces a blind channel estimation method for a block transmission system using fractional sampling at a receiver. In most digital communication systems, low-pass filters are placed at a transmitter to confine the transmitting waveform to an allocated bandwidth and at a receiver to improve the signal-to-noise (SNR) power ratio. Consequently, if the received waveform is sampled with sufficiently fast rate, the resulting discrete-time signal is band-limited and exhibits a smooth waveform. The method exploits this property for estimating the channel impulse response, via an interpolation formula. The method does not require knowledge of input statistics or the autocorrelation of the received signal. It is free of channel order estimation and thus robust against its overestimation. Moreover, the algorithm is quite efficient because it does not need to compute eigenvalues of a matrix.     

基于Golay互补序列的压缩感知稀疏信道估计算法

该文针对现有基于压缩感知的信道估计方法峰均功率比高、计算量大等问题,使用确定性格雷(Golay)序列作为训练序列对稀疏信道进行信道估计,在接收端实现了对信道冲激响应的估计,给出了估计模型和具体的算法推演,推导了该方法的峰均功率比,并与基于随机高斯序列的压缩感知信道估计方法的性能、峰均功率比和计算量进行了比较。仿真实验表明：格雷序列以及随机高斯序列两种序列都可以重构出稀疏信道非零抽头系数,但是格雷序列对稀疏信道冲激响应的确定性观测估计值的均方误差(MSE)和匹配度性能(Match Rate, MR)均优于随机高斯序列,计算量降低了许多,且在OFDM系统中峰均功率比大大降低。     

使用接收分集的确定性OFDM盲信道估计算法

本文针对无循环前缀的OFDM系统研究了非数据辅助的信道估计方法.借助于2个接收天线实现的信道分集,提出了一个确定性信道盲估计算法,并给出了信道可辨识性条件.该算法使用1个OFDM块便可在一个标量模糊因子的意义上确定信道冲激响应.算法计算量小,而且对输入信号的星座和统计特性没有约束.仿真结果证实了算法的有效性.     

基于保护环的ATM多播树自愈机制

本文提出了一种采用预设计的方法生成可恢复ATM多播树的自愈机制,该机制考虑了多播树的任何单链路和单节点失效两种情况.多播树的所有链路都包含在不同的保护环中,点相邻的保护环通过不经过公共点的接通路径相连.节点检测到失效时,只需激活相应的保护环和接通路径即可.仿真结果验证了该机制的有效性和可靠性.     

Stability Analysis of Nonlinear System Identification via Delayed Neural Networks

In this brief, the identification problem for time-delay nonlinear system is discussed. We use a delayed dynamic neural network to do on-line identification. This neural network has dynamic series-parallel structure. The stability conditions of on-line identification are derived by Lyapunov-Krasovskii approach, which are described by linear matrix inequality. The conditions for passivity, asymptotic stability and uniform stability are established in some senses. We conclude that the gradient algorithm for updating the weights of the delayed neural networks is stable to any bounded uncertainties     

Adaptive Nonlinear System Identification in the Short-Time Fourier Transform Domain

In this paper, we introduce an adaptive algorithm for nonlinear system identification in the short-time Fourier transform (STFT) domain. The adaptive scheme consists of a parallel combination of a linear component, represented by crossband filters between subbands, and a quadratic component, which is modeled by multiplicative cross-terms. We adaptively update the model parameters using the least-mean-square (LMS) algorithm, and derive explicit expressions for the transient and steady-state mean-square error (MSE) in frequency bins for white Gaussian inputs. We show that estimation of the nonlinear component improves the MSE performance only when the power ratio of nonlinear to linear components is relatively high. Furthermore, as the number of crossband filters increases, a lower steady-state MSE may be obtained at the expense of slower convergence. Experimental results support the theoretical derivations.     

基于Kernel-SOM的非线性系统辨识及模型运行收敛性分析

该文提出基于核SOM(Kernel-SOM)的非监督非线性系统辨识方法。在辨识误差和系统初始输入误差同时存在的条件下,对Kernel-SOM辨识模型独立运行的收敛性进行了理论分析,并给出了辨识模型运行收敛的定理。数字仿真表明了所述方法的有效性及收敛定理的正确性。     

用基函数神经网络实现多阈值图象分割

本文介绍了一种用基函数神经网络实现多阈值图象分割的新方法。它从函数逼近的角度研究基于灰度直方图的多阈值分割问题，提出了一种模糊反向传播学习算法，采用该算法的高斯基函数网络能够准确检测直方图中包含的子区域和它们的分布函数，而且速度很快。实验表明本文的方法在实际图象分割中是有效的。     

Performance of Multilevel Baseband Digital Systems in a Nonlinear Environment

A good deal of effort has been spent, in the past few years, to devise numerical algorithms for evaluating the performance of digital communication systems over noisy linear channels, i.e., in the presence of intersymbol interference and noise. In this concise paper we present a method for computing the error probability of multilevel baseband digital modulation systems when the channel is nonlinear with memory. The algorithm is based on a Volterra series expansion of the nonlinearity; with this model, we show that the moments of the disturbance can be computed recursively, and the same techniques in use for linear channels can be applied for evaluating the error probability. This approach can be generalized to consider noise entering the nonlinear channel. The computing algorithms are described in detail, and a complete example is worked out.     

Current-Sweep Operation on Nonlinear Selectorless RRAM for Multilevel Cell Applications

Bilayer selectorless resistive random-access memories (RRAM) have been demonstrated by utilizing the intrinsic nonlinear resistive switching (RS) characteristics, without additional transistor or a selector integration. The bilayer structures, i.e. high-k layer/low-k layer stacks, are highly scalable while suppressing the sneak path currents (SPC) and reading error in the crossbar RRAM array. The nonlinearity (NL) modulation is also investigated by different operating schemes, and a multilevel cell application is demonstrated with the current-sweep method. The results provide additional insights into the development and optimization of bilayer selectorless RRAMs with high nonlinearity, good memory window, and low switching energy (∼ 40 pJ/bit), which enable the high-density storage and low-power crossbar array memory applications.     

MIMO非线性系统辨识：Volterra级数法

本文讨论了一般ＭＩＭＯ非线性系统的Ｖｏｌｔｅｒｒａ级数表示,给出并证明了Ｖｏｌｔｅｒｒａ核矩阵存在的条件。提出了两种辨识Ｖｏｌｔｅｒｒａ核矩阵的方法,即随机响应法与脉冲响应法。给出了利用系统的输入输出信号计算Ｖｏｌｔｅｒｒａ核矩阵的公式。最后,利用仿真实例验证上述方法的有效性。     

改进的PSO算法在非线性模型参数辨识中的应用

参数辨识是过程建模的基础,对于参数辨识问题提出了许多不同的方法.针对传统模型参数辩识方法和遗传算法用于模型参数辨识时的缺点,提出一种基于微粒群优化(PSO)算法的模型参数辨识方法,利用PSO算法的强大优化能力,通过对算法的改进,将过程模型的每个参数作为微粒群体中的一个微粒,利用微粒群体在参数空间进行高效并行的搜索,以获得过程模型的最佳参数值,并将其用于对非线性系统模型的参数辨识,可有效提高参数辨识的精度和效率.该方法应用到实际例子中,获得了满意的辨识精度和效率,得到较为精确的过程模型,模型输出与实际输出基本一致,仿真结果令人满意.实例仿真结果表明,微粒群算法为非线性系统模型参数辨识提供了一种有效的途径.