基于细胞神经网络的数学形态滤波方法与应用

数学形态学以集合运算为基础,在图像处理领域得到了广泛地运用。数学形态学以集合运算为基础,用具有一定形态的结构元素去度量图像中的形态以解决理解问题。证明利用细胞神经网络（Cellular Neural Network）,运用数学形态滤波可并行完成数学形态运算。该文给出了细胞神经网络（CNN）在腐蚀、膨胀、结构开、结构闭中的实现及应用。将其结果运用在指纹图像的预处理当中,取得了较理想的结果。     

Neural networks and volterra series for time‐domain power amplifier behavioral models

This paper presents a black‐box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers (PAs), including strong nonlinearities and memory effects. Feedforward time‐delay Neural Networks (TDNN) are used to extract the model from a large‐signal input‐output time‐domain characterization in a given bandwidth; furthermore, explicit formulas to derive Volterra kernels from the TDNN parameters are also presented. The TDNN and related Volterra models can predict the amplifier response to different frequency excitations in the same bandwidth and power sweep. As a case study, a PA, characterized with a two‐tone power swept excitation, is modeled and simulations are found in good agreement with training measurements; moreover, a model validation with two tones of different frequencies and spacing is also performed. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise

The main limits on adaptive Volterra filters are their computational complexity in practical implementation and significant performance degradation under the impulsive noise environment. In this paper, a low-complexity pipelined robust M-estimate second-order Volterra (PRMSOV) filter is proposed to reduce the computational burdens of the Volterra filter and enhance the robustness against impulsive noise. The PRMSOV filter consists of a number of extended second-order Volterra (SOV) modules without feedback input cascaded in a chained form. To apply to the modular architecture, the modified normalized least mean M-estimate (NLMM) algorithms are derived to suppress the effect of impulsive noise on the nonlinear and linear combiner subsections, respectively. Since the SOV-NLMM modules in the PRMSOV can operate simultaneously in a pipelined parallelism fashion, they can give a significant improvement of computational efficiency and robustness against impulsive noise. The stability and convergence on nonlinear and linear combiner subsections are also analyzed with the contaminated Gaussian (CG) noise model. Simulations on nonlinear system identification and speech prediction show the proposed PRMSOV filter has better performance than the conventional SOV filter and joint process pipelined SOV (JPPSOV) filter under impulsive noise environment. The initial convergence, steady-state error, robustness and computational complexity are also better than the SOV and JPPSOV filters.     

模拟退火粒子群算法的Volterra主动噪声消除器

当Volterra滤波器的阶数较大时,滤波器的系数呈几何级数增长,实现困难。本文利用模拟退火粒子群算法优化二阶Volterra非线性滤波器系数,并将其用于管道噪声消除。该算法结构简单、运行速度快,有较强的全局搜索能力。数值仿真结果表明,该方法达到良好的非线性消噪效果。     

分形滤波的网络流量预测

提出了一种结合分形滤波与线性神经网络进行网络流量预测的新方法。通过分形滤波增强网络流量中的长相关结构,使序列更加平滑,根据相空间重构理论利用线性神经网络进行预测操作,并用实际网络流量验证该模型的有效性。     

Multilayer perceptron for simulation models reduction: Application to a sawmill workshop

Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken into account in order to describe how the bottlenecks are fed. It is modeled through a continuous approach thanks to a neural network. In particular, we use a multilayer perceptron. The structure of the network is determined by using a pruning procedure. For validation, this approach is applied to the modelisation of a sawmill workshop.     

基于神经网络的单相有源滤波器

有源滤渡器的控制是一个典型的非线性控制过程．非常适合用神经网络来实现．本文提出了一种应用于有源滤波器系统的神经网络控制器,神经网络控制器的输入是负载电流和补偿电流。输出是开关控制信号甩于控制有源滤波器产生补偿电流来抵消非线性负载的畸变电流。基于MATLAB/SIMULINK平台．建立了单相有源滤波器仿真模型．仿真结果表明所提出的神经网络控制器的有效性。     

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

The pipelined adaptive Volterra filters (PAVFs) with a two-layer structure constitute a class of good low-complexity filters. They can efficiently reduce the computational complexity of the conventional adaptive Volterra filter. Their major drawbacks are low convergence rate and high steady-state error caused by the coupling effect between the two layers. In order to remove the coupling effect and improve the performance of PAVFs, we present a novel hierarchical pipelined adaptive Volterra filter (HPAVF)-based alternative update mechanism. The HPAVFs with hierarchical decoupled normalized least mean square (HDNLMS) algorithms are derived to adaptively update weights of its nonlinear and linear subsections. The computational complexity of HPAVF is also analyzed. Simulations of nonlinear system adaptive identification, nonlinear channel equalization, and speech prediction show that the proposed HPAVF with different independent weight vectors in nonlinear subsection has superior performance to conventional Volterra filters, diagonally truncated Volterra filters, and PAVFs in terms of initial convergence, steady-state error, and computational complexity.     

HSOP: A neural network paradigm and its applications

This paper presents a neural network paradigm, case studies on its applications and performance. We called this paradigm the Hybrid Sum-of-Products (HSOP), and it is an improvement on both sum-of-products and backpropagation paradigms. In the HSOP architecture, the lowest layer (input layer) is connected to the layer above in the standard backpropagation manner. Subsequent layers are connected in the sum-of-products manner. The learning rates required for the HSOP are similar to those required for sum-of-products, with slight differences. Since input units do not have defined error, the fact that they are connected differently has no consequences on the calculations of the error and values for the rest of the units. The proposed paradigm was applied to two classification problems: computer user identification and characterisation of ultrasonic transducers. In both cases, the HSOP showed faster learning than backpropagation and sum-of-products without a significant computational penalty, since the number of its weights is comparable to backpropagation. The classification accuracy of HSOP when applied to the two applications is better than the traditional sum-of-products, and is comparable to that of the backpropagation.     

An adaptive conscientious competitive learning algorithm and its applications

An adaptive conscientious competitive learning (ACCL) algorithm is proposed in this paper. The ACCL algorithm can adjust the conscience parameter itself according to the feedback information about the practical winning situation of all neurons during the learning process. The a priori information about the distribution range of the input patterns which is required for the conventional conscientious competitive learning (CCL) algorithm, is no longer required in the ACCL algorithm. The “neurons get stuck” problem of the competitive learning (CL) algorithm and conscientious competitive learning (CCL) algorithm with small conscience parameter is overcome. At the same time, neurons will not be tangled together as in the case of the CCL algorithm with large conscience parameter. The ACCL algorithm is applied to vector quantization (VQ) and probability density function estimation (PDFE). It can generate better results than the conventional CL and CCL algorithms. Experimental results are also included to demonstrate its effectiveness.     

一种优化多层前向网络的IA-BP混合算法

该文针对免疫算法(IA)在优化较大规模的多层前向神经网络时收敛速度慢的缺点,给出了一种综合免疫算法和BP算法优点的IA-BP混合算法,它首先采用免疫算法进行全局搜索,然后调用BP算法进行局部搜索,从而加快收敛速度。实验结果表明该算法在训练较大规模的前向神经网络时性能要优于免疫算法和BP算法。     

基于BP神经网络的预测建模系统的研究与实现

神经网络具有良好的记忆、归纳和学习能力,对难以用数学方法建立精确模型的信息、工艺等能够进行有效地预测建模。该文通过对BP神经网络的分析和研究,针对传统BP算法的不足,采用Levenberg—Marquardt(LM)优化算法的建立一个基于BP神经网络预测建模系统。在介绍了系统的主要功能之后,给出了用MATLAB软件实现该系统主要模块的具体程序。最后采用该系统对一个制造过程中刀具磨损量的进行了预测建模,实验仿真结果表明：系统具有良好的预测效果,刀具实际磨损量与预测磨损量的误差基本上在10％以下。     

Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7–31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.     

一种多层前馈网参数可分离学习算法

目前大部分神经网络学习算法都是对网络所有的参数同时进行学习.当网络规模较大时,这种做法常常很耗时.由于许多网络,例如感知器、径向基函数网络、概率广义回归网络以及模糊神经网络,都是一种多层前馈型网络,它们的输入输出映射都可以表示为一组可变基的线性组合.网络的参数也表现为二类:可变基中的参数是非线性的,组合系数是线性的.为此,提出了一个将这二类参数进行分离学习的算法.仿真结果表明,这个学习算法加快了学习过程,提高了网络的逼近性能.     

一种迭代离差差分滤波算法及其应用研究

针对扩展卡尔曼滤波（EKF）和迭代EKF量测更新过程采用线性化误差传递,导致状态估计精度偏低的问题,将迭代方法、统计线性化误差传递和离差差分滤波器相结合,建立了一种新型迭代离差差分滤波方法．将该方法应用于再入弹道目标状态估计,仿真实验结果显示,此方法降低了测量方程的非线性对滤波的影响,有效提高了目标的状态估计精度．     

高速船运动在线联机预报及其控制仿真

船舶在航行时由于航行工况和环境变化多端,受力十分复杂,使得船舶推进系统具有非线性、时变性和干扰的复杂性等特点。该文基于MATLAB／SIMULINK对某一高速船推进系统进行建模。在此模型中,利用神经网络对航行海况进行在线联机预报,预报结果传给局域网中另一计算机的模糊控制器,对船桨子系统进行在线控制,从而改变船舶航速。同时船舶的实时航速被反馈到神经网络预报器,进行下一轮预报及其控制,如此反复,最终实现智能控制。对翼滑艇的仿真研究结果表明．应用此种联机控制,在各种海况下,均能达到良好的控制效果。有理由相信神经网络结合模糊控制在船舶工程中有很好的应用前景。     

对多层前向神经网络研究的几点看法

从不同的领域对多层前向网络的作用本质作了分析,对泛化能力、模型选择、有限样本量等主要问题做了定性讨论;对当前前向网络研究中的一些问题提出了看法.     

形态滤波和空间滤波在两相流速度测量中的应用

为了快速、准确地对气固两相流速度进行测量,介绍一种利用形态滤波和空间滤波处理气固两相流信号的基本方法,首先研究电容传感器的空间滤波效应,并找出固体速度和电容传感器带宽之间的关系.然后通过对一维形态滤波算法理论进行分析,推导出可用于实时运算的形态滤波方法,此方法具有处理速度快,滤波效果好,适用性广的特点,可应用于多种信号的实时处理中.然后利用形态滤波确定传感器的带宽,进而求出固体速度.最后给出仿真实验结果,仿真实验结果表明:该方法可以满足气固两相流速度的测量要求.     

神经传递通路的神经网络建模

建立与生物神经系统相像的人工神经网络是设计具有智能的人工制品的有潜力的研究方法.建立神经网络传递通路简化模型,并应用神经网络建模.模型由4个回路构成动态延迟网络,包括丘脑回路、大脑皮质回路、基底核回路和小脑回路.仿真测试表明:网络易于数学实现,并具有良好的函数逼近能力和泛化能力.网络可用于自治系统和自治产品.＃