基于改进GA-Elman的无线智能传播损耗预测方法

在5G移动通信系统商用落地的背景下,设计准确、高效的信道估计方法对无线网络性能优化具有重要意义。基于改进GA-Elman算法,提出一种新的无线智能传播损耗预测方法。对Elman神经网络中的连接权值、阈值和隐藏神经元进行实数编码,在隐藏神经元编码中加入二进制控制基因,同时利用自适应遗传算法对权值、阈值和隐藏神经元数量进行优化,解决网络易陷入局部极小值和神经元数目难以确定的问题,从而提高预测性能。仿真结果表明,与仅优化连接权值及阈值的GA-Elman神经网络和标准Elman神经网络相比,该方法具有较高的预测精度。     

Variable Hidden Layer Sizing in Elman Recurrent Neuro-Evolution

The relationship between the size of the hidden layer in a neural network and performance in a particular domain is currently an open research issue. Often, the number of neurons in the hidden layer is chosen empirically and subsequently fixed for the training of the network. Fixing the size of the hidden layer limits an inherent strength of neural networks—the ability to generalize experiences from one situation to another, to adapt to new situations, and to overcome the brittleness often associated with traditional artificial intelligence techniques. This paper proposes an evolutionary algorithm to search for network sizes along with weights and connections between neurons.This research builds upon the neuro-evolution tool SANE, developed by David Moriarty. SANE evolves neurons and networks simultaneously, and is modified in this work in several ways, including varying the hidden layer size, and evolving Elman recurrent neural networks for non-Markovian tasks. These modifications allow the evolution of better performing and more consistent networks, and do so more efficiently and faster.SANE, modified with variable network sizing, learns to play modified casino blackjack and develops a successful card counting strategy. The contributions of this research are up to 8.3% performance increases over fixed hidden layer size models while reducing hidden layer processing time by almost 10%, and a faster, more autonomous approach to the scaling of neuro-evolutionary techniques to solving larger and more difficult problems.     

基于Chaos-MEA-Elman的电离层临界频率f0F2预测

针对飞机高频通信中通信频率选择不当导致信号衰落等问题,提出一种基于混沌理论和思维进化算法优化的Elman神经网络相结合的临界频率f0F2预测方法。分析和验证f0F2时间序列的混沌特性;采用混沌理论重构技术重建f0F2时间序列的相空间,并根据相空间结构确定Elman神经网络各层节点个数;利用思维进化算法优化Elman神经网络的初始权值和阈值。数值和实验分析表明,Chaos-MEA-Elman算法比Elman神经网络对电离层临界频率f0F2的预测精确度更高,为预测飞机最佳通信频率提供新的方法。     

遗传算法与BP神经网络相结合的说话人识别系统

基于BP神经网络的说话人识别系统是目前说话人识别中的一种主要模型,但BP神经网络通常难以确定隐含层单元的数目,且收敛速度慢。针对此缺点,提出了一种基于遗传算法（GA）的说话人识别BP神经网络优化方案,该方案利用混合编码的GA对神经网络的连接权和结构进行了优化,可以有效地剔除整个网络冗余节点和冗余连接权,方案利用了BP神经网络的并行性和GA的全局搜索能力,显著地改善了网络的处理能力。实验表明：基于混合编码GA的BP神经网络具有快速学习网络权重的能力,识别率高,是说话人识别的一种有效可行的新方案。     

基于优化RBF-DDA神经网络的摄像机标定

设计了一种改进的基于动态衰减的RBF神经网络，它能够自适应地确定RBF隐层节点数、高斯函数中心值及径向基函数的宽度，克服了原算法中过多依赖先验知识设计参数的弊病，仿真实验验证了该算法的有效性。并且将此网络应用于摄像机定标中，该网络无需确定摄像机具体的内、外部参数，而且补偿了摄像机非线性畸变，使测量结果更加准确。实验结果表明，应用该神经网络进行摄像机标定能达到较高的精度，且在机器人平面跟踪实验中得到了令人满意的结果。     

基于快速密度聚类的RBF神经网络设计

针对径向基函数（radial basis function,RBF）神经网络隐含层结构难以确定的问题,提出一种基于快速密度聚类的网络结构设计算法。该算法将快速密度聚类算法良好的聚类特性用于RBF神经网络结构设计中,通过寻找密度最大的点并将其作为隐含层神经元,进而确定隐含层神经元个数和初始参数;同时,引入高斯函数的特性,保证了每个隐含层神经元的活性;最后,用一种改进的二阶算法对神经网络进行训练,提高了神经网络的收敛速度和泛化能力。利用典型非线性函数逼近和非线性动态系统辨识实验进行仿真验证,结果表明,基于快速密度聚类设计的RBF神经网络具有紧凑的网络结构、快速的学习能力和良好的泛化能力。     

季节周期性Elman网络的网络流量分析与应用

针对静态前馈网络和Elman网络在网络流量预测中的不足,建立了一个基于改进Elman神经网络的流量模型,并提出了一种基于季节周期性学习方法,根据实际网络中测量得到的网络流量数据,对网络流量进行预测。实验结果表明,该模型具有良好的预测效果,相对于传统线性模型、BP神经网络模型及标准Elman神经网络模型具有更高的预测精度和更好的自适应性,应用于网络流量预测是可行、有效的。     

基于改进遗传算法的RBF神经网络结构优化研究

针对RBF神经网络隐含层节点数过多导致网络结构复杂的问题,提出了一种基于改进遗传算法(IGA)的RBF神经网络优化算法。利用IGA优化基于正交最小二乘法的RBF神经网络结构,通过对隐含层输出矩阵的列向量进行全局寻优,从而设计出结构更优的基于IGA的RBF神经网络(IGA-RBF)。将IGA-RBF神经网络的学习算法应用于电子元器件贮存环境温湿度预测模型,与基于正交最小二乘法的RBF神经网络进行比较的结果表明:IGA-RBF神经网络设计出来的网络训练步数减少了44步,隐含层节点数减少了34个,且预测模型得到的温湿度误差较小,拟合精度大于0.95,具有更高的预测精度。     

Self-adaptive extreme learning machine

In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem.     

压电陶瓷传感器的灵敏度温漂误差补偿研究

针对微测系统中压电陶瓷传感器的灵敏度温漂会使其在变化的温度环境中工作时性能不稳定,进而影响检测精度问题,提出了一种基于改进Elman神经网络的压电陶瓷传感器灵敏度温漂误差补偿控制方法。分析了压电陶瓷传感器产生灵敏度温漂现象的原因。以压电陶瓷切削力测量传感器为对象,在不同温度下对传感器的灵敏度进行了标定试验研究。研究结果表明,压电陶瓷传感器在同一温度下工作时具有良好的线性度,在温度变化的环境中工作会伴有灵敏度温漂现象。为了有效补偿灵敏度温漂附加误差,提高检测精度,建立了基于改进Elman神经网络的灵敏度温漂补偿模型,并对模型涉及的学习算法、激励函数、输入输出层节点以及承接层和隐含层节点数等相关内容进行了研究。对比试验验证结果表明,所建立的灵敏度温漂补偿模型对压电陶瓷传感器的灵敏度温漂误差补偿控制效果明显,未经灵敏度温漂补偿,直接按照常温下灵敏度标定结果预测的压电陶瓷传感器加载力和实际加载力之间误差较大,最大误差达到29.16 N,利用本文建立的基于改进Elman神经网路灵敏度温漂补偿模型补偿后,补偿模型的预测力和压电陶瓷传感器的实际加载力最大误差仅0.72 N,有效保证了检测精度。     

改进的遗传算法在神经网络结构优化中的应用

为了解决人工神经网络隐层节点数目难以确定的问题,针对三层BP神经网络提出了一种最大上限隐层节点数模型,并用改进的遗传算法对其优化。最后,将优化的神经网络对语音特征信号进行分类。仿真结果表明优化后的神经网络具有很好的泛化能力,验证了该方法的有效性。     

基于AQPSO算法优化的Elman网络模型及其应用

Elman神经网络是一种典型的递归神经网络。提出了自适应量子粒子群优化（Adaptive Quantum-Behaved Particle Swarm Optimization,AQPSO）算法,用于训练Elman网络参数,改进了Elman网络的泛化能力。利用中集集团股票数据进行预测,实验结果表明,采用AQPSO算法获得的Elman网络模型不但具有很强的泛化能力,而且具有良好的稳定性,在股票数据预测中具有一定的实用价值。     

量子门Elman神经网络及其梯度扩展的量子反向传播学习算法

针对Elman神经网络的学习速度和泛化性能, 提出一种具有量子门结构的新型Elman神经网络模型及其梯度扩展反向传播(Back-propagation)学习算法, 新模型由量子比特神经元和经典神经元构成. 新网络结构采用量子映射层以确保来自上下文单元的局部反馈与隐藏层输入之间的模式一致; 通过量子比特神经元输出与相关量子门参数的修正互补关系以提高网络更新动力. 新学习算法采用搜索然后收敛的策略自适应地调整学习率参数以提高网络学习速度; 通过将上下文单元的权值扩展到隐藏层的权值矩阵, 使其在与隐藏层权值同步更新过程中获取时间序列的额外信息, 从而提高网络上下文单元输出与隐藏层输入之间的匹配程度. 以峰值检波为例的数值实验结果显示, 在量子反向传播学习过程中, 量子门Elman神经网络具有较快的学习速度和良好的泛化性能.     

Feedforward neural network construction using cross validation.

This article presents an algorithm that constructs feedforward neural networks with a single hidden layer for pattern classification. The algorithm starts with a small number of hidden units in the network and adds more hidden units as needed to improve the network's predictive accuracy. To determine when to stop adding new hidden units, the algorithm makes use of a subset of the available training samples for cross validation. New hidden units are added to the network only if they improve the classification accuracy of the network on the training samples and on the cross-validation samples. Extensive experimental results show that the algorithm is effective in obtaining networks with predictive accuracy rates that are better than those obtained by state-of-the-art decision tree methods.     

Uncertain data stream algorithm based on clustering RBF neural network

In this paper, the existing algorithms for modeling uncertain data streams based on radial basis function neural networks have problems of low accuracy, weak stability and slow speed. A new clustering method for uncertain data streams is proposed. Radial basis function neural network of the algorithm. The algorithm firstly models the uncertain data stream, then combines the fuzzy theory and the neural network principle to obtain the radial basis function neural network, and then obtains the radial basis function neural network through the clustering algorithm of the regular tetrahedral uncertain vector. The central weight and width weights ultimately result in hidden layer output and output layer output results. The experimental results show that the proposed algorithm is an effective algorithm for modeling uncertain data streams using clustering radial basis function neural networks. It has higher precision, stability and speed than similar algorithms.     

PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

Fault diagnosis of analog circuits is a key problem in the theory of circuit networks and has been investigated by many researchers in recent decades. In this paper, an active filter circuit is used as the circuit under test (CUT) and is simulated in both fault-free and faulty conditions. A modular neural network model is proposed in this paper for soft fault diagnosis of the CUT. To optimize the structure of neural network modules in the proposed scheme, particle swarm optimization (PSO) algorithm is used to determine the number of hidden layer nodes of neural network modules. In addition, the output weight optimization–hidden weight optimization (OWO-HWO) training algorithm is employed, instead of conventional output weight optimization–backpropagation (OWO-BP) algorithm, to improve convergence speed in training of the neural network modules in proposed modular model. The performance of the proposed method is compared to that of monolithic multilayer perceptrons (MLPs) trained by OWO-BP and OWO-HWO algorithms, K-nearest neighbor (KNN) classifier and a related system with the same CUT. Experimental results show that the PSO-optimized modular neural network model which is trained by the OWO-HWO algorithm offers higher correct fault location rate in analog circuit fault diagnosis application as compared to the classic and monolithic investigated neural models.     

改进的Elman模型与递归反传控制神经网络

在Elman网络的基础上提出了两种改进网络:输出-输入反馈Elman网络和输出-隐层反馈Elman网络模型,并以前者作为误差反传的通道,建立了递归反向传播控制神经网络模型.在Lyapunov稳定性意义下分别给出了改进网络的稳定性证明,得到了保证网络稳定收敛的最佳自适应学习速率.分别用Elman网络及其改进网络对超声马达进行了模拟.利用改进的Elman网络模型,除了可以较好地模拟马达速度以外,还得到了一些有意义的结果,据此可以根据现场数据采样的情况,选用不同的网络模型.模拟实验结果表明,递归反向传播控制神经网络对多种形式的超声马达参考速度都有很好的控制效果.     

一种改进的RBF神经网络参数优化方法

提出了一种改进的RBF神经网络参数优化算法。通过资源分配网络算法确定隐含层节点个数,引入剪枝策略删除对网络贡献不大的节点,用改进的粒子群算法对RBF网络的中心、宽度、权值进行优化,使RBF网络不仅可以得到合适的结构,同时也可以得到合适的控制参数。将此算法用于连续搅拌釜反应器模型的预测,结果表明,此算法优化后的RBF网络结构小,并且具有较高的泛化能力。     

多层前向人工神经网络图像分类算法

传统人工神经网络的输入均为向量形式,而图像由矩阵形式表示,因此,在用人工神经网络进行图像处理时,图像将以向量形式输入至神经网络,这破坏了图像的结构信息,从而影响了图像处理的效果。为了提高网络对图像的处理能力,文中借鉴了深度学习的思想与方法,引进了具有矩阵输入的多层前向神经网络。同时,采用传统的反向传播训练算法(BP)训练该网络,给出了训练过程与训练算法,并在USPS手写数字数据集上进行了数值实验。实验结果表明,相对于单隐层矩阵输入前向神经网络(2D-BP),所提多层网络具有较好的分类效果。此外,对于彩色图片分类问题,利用所提出的2D-BP网络,给出了一个有效的可行方法。     

混沌粒子群优化神经网络的井下人员无线定位方法研究

针对井下人员定位系统定位精度较低,不能满足智慧煤矿的需求,提出一种基于混沌粒子群算法优化Elman神经网络的井下人员无线定位方法。该定位方法首先在井下巷道无线网络环境中,利用无线终端采集一定数量的样本点指纹数据库。其次初始化Elman神经网络,利用混沌粒子群优化算法对神经网络权值和自连接反馈增益因子寻优。再次用指纹数据库对优化过的Elman神经网络进行训练和测试,建立神经网络定位算法模型。最后通过无线终端采集定位点的指纹数据,由神经网络定位算法模型进行实时定位。经试验表明,该井下人员无线定位方法平均定位误差为1.35 m;而混沌粒子群算法优化Elman神经网络定位算法,其算法全局搜索能力更强,更适合井下时变环境中应用。