稳定快速收敛的自适应格型IIR滤波

本文提出一种稳定快速收敛的格型自适应ＩＩＲ滤波（ＡＩＩＲＰ）新算法。新的自适应ＩＩＲ滤波结构不同于一般的格型ＡＩＩＲＦ实现结构，它由ＡＩＩＲＦ的分子、分母多项式分别以全零点格型结构综合构成，具有结构简单、计算量少等优点。ＡＩＩＲＦ的格型结构，解决了ＡＩＩＲＦ的系统稳定性问题。新的格型算法可快速收敛于ＡＩＩＲＦ的全局无偏最优解，解决了ＰＥＡ算法的有偏及ＯＥＡ算法的局部极小点问题。计算机模拟实验结果证明了新的ＡＩＩＲＦ格型算法的有效性。     

进化规划用于自适应IIR滤波器的优化设计

针对自适应ＩＲ滤波器（ＡＩＲＦ）潜在的不稳定性和性能指标函数容易陷入局部极小点而导致性能下降等问题，本文将进化规划用于直接、并联、级联和格型结构的ＡＩＩＲＦ的优化设计。基于进化规划的自适应滤波算法不依赖于梯度信息，能够有效地实现ＡＩＩＲＦ参数的全局寻优。大量的仿真实验结果表明不同结构的ＡＩＲＦ用进化规划进行参数寻优，不仅解决了自适应滤波器性能指标函数容易陷入局部极小点的问题，亦解决了ＡＩＲＦ的稳定性问题。     

收敛的级联形式自适应IIR滤波器

本文主要研究基于级联形式实现的自适应ＩＩＲ滤波器，分别讨论预测误差算法和利用预测误差及新的收敛梯度对ＡＩＩＲＦ参数估计算法的级联ＡＩＩＲＦ。与直接形式实现的ＡＩＩＲＦ相比，级联ＡＩＩＲＦ可直接对其极点估计检测，从而确保ＡＩＩＲＦ的稳定性，新算法的参估计可以收敛于全局最优解，解决了输出误差算法中的多局部极小点问题。     

自适应滤波的新方法--几何中心法

自适应滤波器的性能曲面具有某些有益的几何特性,根据这些特性,本文提出ＦＩＲ自适应几何中心法（ＦＩＲＡＧＣＭ）,然后通过转化ＩＩＲ滤波器的性能曲面,得到全局收敛的ＩＩＲＡＧＣＭ算法,与梯度法不同的是：ＦＩＲＡＧＣＭ算法无需步长选择,需要的存储量极少,并且收敛迅速,ＵＲＡＧＣＭ继承了ＦＩＲＡＧＣＭ的全部优点,并且大幅度提高了收敛精度,仿真结果表明,两种算法性能相当优越。     

自适应Volterra滤波器的递归结构和类递归结构及其算法和应用

本文参考自适应ＩＩＲ滤波器理论，提出了自适应Ｖｏｌｔｅｒｒａ滤波器（ＡＶＦ）的递归结构和类递归结构，讨论了其特点和应用范围，递归结构的引入可显著减少ＡＶＦ的参数和计算量，本文还给出了类递归结构ＡＶＦ的在线辨识算法和非线性系统辨识的应用，给出了递归结构ＡＶＦ的滤波算法和非线性相关噪声低消中的应用，在仿真实验中，将上述算法与多层感知器和非递归结构ＡＶＦ做了对比，结果表明，本文算法在性能和计算量上均有明     

自适应IIR滤波器梯度算法的收敛性分析

自适应ＩＩＲ滤波器梯度算法的收敛性分析戴宪华，徐秉铮（华南理工大学无线电所，广州５１０６４１）ＴｈｅＣｏｎｖｅｒｇｅｎｃｅＡｎａｌｙｓｉｓｏｆＡｄａｐｔｉｖｅＩＩＲＦｉｌｔｅｒｉｎｇＧｒａｄｉｅｎｔＡｌｇｏｒｉｔｈｍ￥ＤａｉＸｉａｎｈｕａｍ；ＸｕＢｉ...     

管道自适应有源消声系统的LMS算法

本文研究管道有源消声系统的自适应控制算法。比较了ＡＡＮＣ系统中自适应滤波器采用ＬＭＳ，ＲＬＳ和ＬＳＬ等算法的特性，导出ＬＭＳ算法的递推公式。针对管道中有源消声自适应控制系统给出了用ＦＩＲ和ＩＩＲ滤波器实现有源消声的ＬＭＳ算法。通过计算机仿真比较了不同输入信号时自适应管道有源消声系统的消声量并讨论了ＬＭＳ算法中步长因子，滤波器阶数，声反馈和声延迟对算法收敛速度和消声量的影响，最后简述了对ＬＭＳ算法的     

一种人工神经网络自适应IIR滤波器

本文提出了一种人工神经网络自适应ＩＩＲ滤波器，这种自适应ＩＩＲ滤波器采用并联型结构，用人工神经网络实现，并保证系统在自适应过程中的稳定性，从而得到了一种稳定的、高度并行的自适应ＩＩＲ滤波器，从根本上改变了以往的串行数值迭代系统，使滤波器自适应过程仅需要几个微秒就可以完成。从而有可能用神经自适应系统完成对快速变化信号的实时处理。本文给出了计算机模拟的结果，理论和模拟结果均表明该结构是稳定的，收敛速度也有明显增加。     

格型结构滤波器设计

近几年来,随着科技的发展,电子通信事业也蓬勃向前,但随之带来的信号干扰、稳定性差、难以控制等问题也亟待解决。格型数字滤波器凭借其独特的结构及优良的特性,被认为是解决此类问题的理想滤波器。本文首先对格型滤波器的结构特点做了简要介绍,对格型滤波器的结构优点进行说明。进而引出自适应格型滤波器算法和LMS算法及相关算法的基本原理。利用此原理设计一个二阶格型滤波器,并在MATLAB环境下完成了仿真工作。     

在自适应滤波器中干扰信号检测算法的研究

本文针对自适应滤波器性能易受外部干扰的影响,提出了一种基于自适滤波器进行干扰信号检测（ＡＦＤＩ）的算法。它可以提高自适应算法的抗噪声性能。最后对该算法在强干扰环境的性能进行了模拟,结果表明其各方面的性能指标均优于传统结构的自适应滤波器。     

The genetic search approach. A new learning algorithm for adaptiveIIR filtering

An “evolutionary” approach called the genetic algorithm (GA) was introduced for multimodal optimization in adaptive IIR filtering. However, the disadvantages of using such an algorithm are slow convergence and high computational complexity. Initiated by the merits and shortcomings of the gradient-based algorithms and the evolutionary algorithms, we developed a new hybrid search methodology in which the genetic-type search is embedded into gradient-descent algorithms (such as the LMS algorithm). The new algorithm has the characteristics of faster convergence, global search capability, less sensitivity to the choice of parameters, and simple implementation. The basic idea of the new algorithm is that the filter coefficients are evolved in a random manner once the filter is found to be stuck at a local minimum or to have a slow convergence rate. Only the fittest coefficient set survives and is adapted according to the gradient-descent algorithm until the next evolution. As the random perturbation will be subject to the stability constraint, the filter can always minimum in a stable manner and achieve a smaller error performance with a fast rate. The article reviews adaptive IIR filtering and discusses common learning algorithms for adaptive filtering. It then presents a new learning algorithm based on the genetic search approach and shows how it can help overcome the problems associated with gradient-based and GA algorithms     

Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization

In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks     

免疫粒子群算法及其在矿井提升机故障诊断中的应用

基于人工免疫系统的故障诊断方法是人工智能领域发展起来的一个十分活跃的分支.为了提高免疫算法在矿井提升机故障诊断系统中的执行效率,通过对诊断问题进行更精确的建模和分析,提出了将免疫模型和离散粒子群进化算法相结合的提升机系统的故障诊断方法.该方法在免疫形态空间中采用核主元形式的相似性度量,解决了传统距离判别函数法在故障诊断中存在误差较大等问题.仿真结果表明,该方法能够适应诊断过程中出现的不确定性,并实现多故障诊断.     

基于混合PSO小波网络的网络异常检测

提出一种用新型的进化学习算法训练的小波神经网络(WNN).这种新型的进化学习算法是基于粒子群算法(PSO)和共轭下降法(CG)提出的.以往,将粒子群算法用于神经网络的训练一般是可行的.因为粒子群算法相比于其他的优化算法,具有相对简单的结构和快速的收敛速度,然而,由于粒子的搜索坍塌速度过快而导致粒子停滞这种潜在的危险.粒子的持续停滞使搜索结果很难达到全局最优,甚至会陷入局部最优.为了克服粒子群算法缺点提出了改进的混合算法.通过对KDD 99数据集的实验表明,利用新型混合算法训练的小波神经网络对于异常检测具有很高的异常检测率并且又较低的误判率.可见,该方法对于网络异常检测是有效的.     

一种基于U-D分解卡尔曼滤波多层感知器学习算法

在研究多层感知器结构后，提出一种利用U－D分解卡尔曼滤波训练多层网的新算法．仿真结果表明：与BP算法比较，此算法有着学习速度快、数值稳定性好、对学习参数不敏感、能避免局部极小点等特点。     

Bio-Interactive Healthcare Service System Using Lifelog Based Context Computing

Intelligent bio-sensor information processing was developed using lifelog based context aware technology to provide a flexible and dynamic range of diagnostic capabilities to satisfy healthcare requirements in ubiquitous and mobile computing environments. To accomplish this, various noise signals were grouped into six categories by context estimation and effectively reconfigured noise reduction filters by neural network and genetic algorithm. The neural network-based control module effectively selected an optimal filter block by noise context-based clustering in running mode, and filtering performance was improved by genetic algorithm in evolution mode. Due to its adaptive criteria, genetic algorithm was used to explore the action configuration for each identified bio-context to implement our concept. Our proposed Bio-interactive healthcare service system adopts the concepts of biological context-awareness with evolutionary computations in working environments modeled and identified as bio-sensors based environmental contexts. We used an unsupervised learning algorithm for lifelog based context modeling and a supervised learning algorithm for context identification.     

Evolutionary fuzzy neural networks for hybrid financial prediction

In this paper, an evolutionary fuzzy neural network using fuzzy logic, neural networks (NNs), and genetic algorithms (GAs) is proposed for financial prediction with hybrid input data sets from different financial domains. A new hybrid iterative evolutionary learning algorithm initializes all parameters and weights in the five-layer fuzzy NN, then uses GA to optimize these parameters, and finally applies the gradient descent learning algorithm to continue the optimization of the parameters. Importantly, GA and the gradient descent learning algorithm are used alternatively in an iterative manner to adjust the parameters until the error is less than the required value. Unlike traditional methods, we not only consider the data of the prediction factor, but also consider the hybrid factors related to the prediction factor. Bank prime loan rate, federal funds rate and discount rate are used as hybrid factors to predict future financial values. The simulation results indicate that hybrid iterative evolutionary learning combining both GA and the gradient descent learning algorithm is more powerful than the previous separate sequential training algorithm described in.     

Inherent matrix identities on ARMA lattice filter realizationalgorithm and their application

Presents the matrix identities that are inherent in the solution of the normal equations for an ARMA lattice filter. This derivation also makes clear the relationship between the recursive least squares (RLS) method and the ARMA lattice filter realization algorithm. Further, as an application of the matrix identities, a new method for model identification with frequency weighting (MIFW) is presented     

Feedforward adaptive noise control with multivariable gradientlattice filters

This work presents a novel feedforward adaptive noise control (ANC) algorithm based on multivariable gradient lattice filters to control acoustic noise or vibration globally. In addition, a gradient-based lattice for AR and FIR models and its transpose lattice for the multivariable ANC algorithm are derived. The filter has different forward and backward reflection coefficient matrices to provide a faster convergence than the gradient lattice algorithm when using the same reflection coefficient matrices. Experimental results demonstrate the effectiveness of the proposed algorithm in controlling broadband acoustic noise in an enclosure