卡尔曼体系下的滤波算法计算框架

卡尔曼体系下的滤波算法是指滤波算法中含有基于状态方程的状态预测过程和基于观测方程的状态更新过程.为了便于理解卡尔曼体系下的滤波算法计算过程,从滤波算法计算框架角度对它们分别进行了描述.提出了一个统一的卡尔曼体系下的滤波算法计算框架,该统一计算框架既可用于理解滤波算法计算过程又可用于构造新滤波算法.在统一计算框架中存在两个反馈回路,构造新滤波算法的难点在于确定两个反馈回路中的操作函数.本文以自适应卡尔曼滤波算法(Adaptive Kalman filters,AKF)为例就操作函数选择问题进行了初步探讨,证明了几种探作函数是次优的,这为最终构造一种性能优良的AKF算法奠定了良好的理论基础.     

一种新的全局优化BP网络*

将L-M算法与填充函数法相结合,提出一种训练前向网络的混合型全局优化GOBP(Global Optimization BP)算法。L-M算法的收敛速度快,利用它先得到一个局部极小点,然后利用填充函数算法跳出局部最小,得到一个更低的局部极小点,重复计算即可得到全局最优点。经实验验证,该算法收敛速度很快,避免了局部收敛,而且性能稳定。     

并行的贝叶斯网络参数学习算法

针对大样本条件下EM算法学习贝叶斯网络参数的计算问题,提出一种并行EM算法(Parallel EM,PL-EM)提高大样本条件下复杂贝叶斯网络参数学习的速度.PL-EM算法在E步并行计算隐变量的后验概率和期望充分统计因子;在M步,利用贝叶斯网络的条件独立性和完整数据集下的似然函数可分解性,并行计算各个局部似然函数.实验结果表明PL-EM为解决大样本条件下贝叶斯网络参数学习提供了一种有效的方法.     

基于贪婪-改进果蝇算法的无线传感器网络路由协议

针对无线传感器网络(WSNs)中簇首选择和传输问题,基于贪婪和改进果蝇算法提出一种新型网络路由协议CRP-FOAGA.该协议结合节点位置和剩余能量建立适值函数,通过改进果蝇算法实现适值函数的最优求解,利用贪婪算法实现簇头节点的多跳传输.仿真结果表明:该算法合理规划了簇头节点分布,降低了网络能耗,提升了网络的寿命,具有更好的性能.     

一种新型的RBF网络多用户检测器

本文提出了一种用于设计径向基函数(RBF)网络的递阶免疫算法,并将采用这种递阶免疫算法设计的RBF网络用于DS-CDMA系统的多用户检测.该方法利用递阶免疫算法确定RBF网络隐层(非线性层)的结构和参数,采用最小二乘算法计算RBF网络的输出层权值.递阶免疫算法针对RBF网络的特点引入免疫算子,能够有效提高群体的适应度,加快算法的收敛速度.仿真结果表明,基于这种RBF网络的多用户检测器具有较强的抑制多址干扰和克服远近效应的能力.     

一种新型递归函数的求值算法

上下文无关语言上递归函数(recursive functions on context-free languages,简称CFRF)是为描述计算机上用的非数值算法而提出的一种新型递归函数.该函数的一个重要研究方面是函数的求值算法研究.对此问题的一些研究结果进行了总结.在讨论计算和语法分析的结合方式之后,对主要算法按照算法适用范围从小到大的顺序(同时也是算法研究和提出的顺序)做了较为全面的介绍,着重介绍一种通用的新的高效求值算法,即面向树的求值算法.同时对把CFRF扩充为多种类递归函数后的求值方法进行了说明.CFRF的几个求值算法均已在机器上实现,得到了实践的检验.     

面向多峰值函数优化的人工免疫网络算法特性分析

对用于多峰值函数的人工免疫网络算法进行了改进并分析了其特性.首先在给出基于人工免疫网络的多峰值函数优化算法及其流程的基础上,提出了一种克服早熟现象的改进方案;然后通过与克隆选择算法的数值对比实验,对改进后算法的计算结果加以分析,验证了该算法用于求解多峰值函数优化的有效性;最后重点讨论了算法主要参数对其求解性能的影响,得到了若干参考性结论,可为人工免疫网络计算提供指导.     

一种高效的计算带宽请求微时隙的算法

首先提出了一种基于HFC网络和DOCSIS规范精确计算带宽请求微时隙的算法,该算法根据用户数据长度、MAC管理报文的大小计算出不同RF（射频）条件下发送上行数据所需要的最少微时隙数,有效提高了网络带宽利用率.其次,在算法实现上提出了一种优化的快速实现方法,引入Hash算法,构造了一个Hash函数,大大降低了算法的运算时间,可满足实时系统的要求.该算法已成功应用于由自主开发的物理层和MAC层芯片组成的HDTV（高清晰度电视）双向系统平台.实际网络环境下的测试结果表明该算法及其实现完全满足HDTV双向点播、VoIP、Internet网络通信等应用需求,在实际应用中表现出了良好的性能和可靠性.     

接入网MAC层QoS系统多维报文分类算法的研究与应用

以HFC网络核心设备双向CM(Cable Modem)为研究背景,首先对报文分类经典算法和最新算法研究进展进行总结和分析,然后依据HFC网络QoS系统需求提出了一种基于B树结构和无冲突Hash函数的BH报文分类算法,并给出了该算法的详细设计和实现过程.通过理论分析得出该算法具有时间复杂度较低和占用内存小的特点,适合于CM等嵌入式应用环境.     

一种基于神经网络复杂度的修剪算法

针对神经网络结构设计问题,提出一种基于神经网络复杂度的修剪算法.其实质是在训练过程中,利用网络连接权矩阵的协方差矩阵计算网络的信息熵,获得网络的复杂度;在保证网络信息处理能力的前提下,删除对网络复杂度影响最小的隐节点.该算法不要求训练网络到代价函数的极小点,适合在线修剪网络结构,并且避免了结构调整前的网络权值预处理.通过对典型函数逼近的实验结果表明,该算法在保证网络逼近精度的同时,可有效地简化网络结构.     

Application of temporal difference learning rules in short‐term traffic flow prediction

Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short‐term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi‐layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back‐propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi‐resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

Extraction of voltage harmonics using multi-layer perceptron neural network

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning to effectively track and extract the 3rd and 5th voltage harmonics. For this purpose, two different MLP neural network structures are constructed and their performances compared. The effects of hidden layer, supervisors and learning rate are also presented. The proposed MLP Neural Network algorithm is trained and tested in MATLAB program environment. The results show that MLP neural network enable to extract each harmonic effectively.     

Identification and control using MLP,Elman, NARXSP and radial basis function networks: a comparative analysis

This paper describes four neural networks multilayer perceptron (MLP) network, Elman network, NARXSP network and radial basis function (RBF) network. Neural networks are applied for identification and control of DC servo motor and benchmark nonlinear system. Number of epochs required and time taken to train the controller are shown in the form of bar plots for four neural networks. Levenberg-Marquardt algorithm is used for training the controller using neural network toolbox in MATLAB. Each neural network controller is run ten times. Their performances are compared for each run in terms of number of epochs required and time taken to train each controller for tracking a reference trajectory.     

基于MLP神经网络的分组密码算法能量分析研究

随着嵌入式密码设备的广泛应用,侧信道分析(side channel analysis,SCA)成为其安全威胁之一。通过对密码算法物理实现过程中的泄露信息进行分析实现密钥恢复,进而对密码算法实现的安全性进行评估。为了精简用于能量分析的多层感知器(multi-layer perceptron,MLP)网络结构,减少模型的训练参数和训练时间,针对基于汉明重量(HW)和基于比特的MLP神经网络的模型进行了研究,输出类别由256分类分别减少为9分类和2分类;通过采集AES密码算法运行过程中的能量曲线对所提出的MLP神经网络进行训练和测试。实验结果表明,该模型在确保预测精度的前提下能减少MLP神经网络84%的训练参数和28%的训练时间,并减少了密钥恢复阶段需要的能量曲线数量,最少只需要一条能量曲线即可完成AES算法完整密钥的恢复。实验验证了模型的有效性,使用该模型可以对分组密码算法实现的安全性进行分析和评估。     

Combined projection and kernel basis functions for classification in evolutionary neural networks

This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three different combined basis function models are proposed with all the different pairs that can be obtained with SU, PU and RBF nodes: product–sigmoidal unit (PSU) neural networks, product–radial basis function (PRBF) neural networks, and sigmoidal–radial basis function (SRBF) neural networks; and these are compared to the corresponding pure models: product unit neural network (PUNN), multilayer perceptron (MLP) and the RBF neural network. The proposals are tested using ten benchmark classification problems from well known machine learning problems. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of classification in several datasets.     

On-line neural training algorithm with sliding mode control and adaptive learning rate

This paper presents a new algorithm for on-line artificial neural networks (ANN) training. The network topology is a standard multilayer perceptron (MLP) and the training algorithm is based on the theory of variable structure systems (VSS) and sliding mode control (SMC). The main feature of this novel procedure is the adaptability of the gain (learning rate), which is obtained from sliding mode surface so that system stability is guaranteed.     

Optimization of the weights and asymmetric activation function family of neural network for time series forecasting

The use of neural network models for time series forecasting has been motivated by experimental results that indicate high capacity for function approximation with good accuracy. Generally, these models use activation functions with fixed parameters. However, it is known that the choice of activation function strongly influences the complexity and neural network performance and that a limited number of activation functions has been used in general. We describe the use of an asymmetric activation functions family with free parameter for neural networks. We prove that the activation functions family defined, satisfies the requirements of the universal approximation theorem We present a methodology for global optimization of the activation functions family with free parameter and the connections between the processing units of the neural network. The main idea is to optimize, simultaneously, the weights and activation function used in a Multilayer Perceptron (MLP), through an approach that combines the advantages of simulated annealing, tabu search and a local learning algorithm. We have chosen two local learning algorithms: the backpropagation with momentum (BPM) and Levenberg–Marquardt (LM). The overall purpose is to improve performance in time series forecasting.     

Breast cancer classification applying artificial metaplasticity algorithm

A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phase, the artificial metaplasticity multilayer perceptron (AMMLP) algorithm assigns higher values for updating the weights in the less frequent activations than in the more frequent ones. AMMLP achieves a more efficient training and improves MLP performance. The well-known and readily available Wisconsin Breast Cancer Database (WBCD) has been used to test the algorithm. Performance of the AMMLP was tested through classification accuracy, sensitivity and specificity analysis, and confusion matrix analysis. The results obtained by AMMLP are compared with the backpropagation algorithm (BPA) and other recent classification techniques applied to the same database. The best result obtained so far with the AMMLP algorithm is 99.63%.     

Bio-inspired and gradient-based algorithms to train MLPs: The influence of diversity

This paper has three main goals: (i) to employ two classes of algorithms: bio-inspired and gradient-based to train multi-layer perceptron (MLP) neural networks for pattern classification; (ii) to combine the trained neural networks into ensembles of classifiers; and (iii) to investigate the influence of diversity in the classification performance of individual and ensembles of classifiers. The optimization version of an artificial immune network, named opt-aiNet, particle swarm optimization (PSO) and an evolutionary algorithm (EA) are used as bio-inspired methods to train MLP networks. Besides, the standard backpropagation with momentum (BPM), a quasi-Newton method called DFP and a modified scaled-conjugate gradient (SCGM) are the gradient-based algorithms used to train MLP networks in this work. Comparisons among all the training methods are presented in terms of classification accuracy and diversity of the solutions found. The results obtained suggest that most bio-inspired algorithms deteriorate the diversity of solutions during the search, while immune-based methods, like opt-aiNet, and multiple initializations of standard gradient-based algorithms provide diverse solutions that result in good classification accuracy for the ensembles.