基于蚁群算法优化回声状态网络的研究

针对输出权值采用最小二乘法的回声状态网络(ESN),在随机选取输入权值和隐层神经元阈值时,存在收敛速度慢、预测精度不稳定等问题,提出了基于蚁群算法优化回声状态网络(ACO-ESN)的算法。该算法将优化回声状态网络的初始输入权值、隐层神经元阈值问题转化为蚁群算法中蚂蚁寻找最佳路径的问题,输出权值采用最小二乘法计算,通过蚁群算法的更新、变异、遗传等操作训练回声状态网络,选择出使回声状态网络预测误差最小的输入权值和阈值,从而提高其预测性能。将ACO-ESN与ELM、I-ELM、OS-ELM、B-ELM等神经网络的仿真结果进行对比,结果验证经过蚁群算法优化的回声状态网络加快了其收敛速度,改善了其预测性能,并增强了隐层神经元的敏感度。     

基于模拟退火算法的改进极限学习机

传统的极限学习机作为一种有监督的学习模型,任意对隐藏层神经元的输入权值和偏置进行赋值,通过计算隐藏层神经元的输出权值完成学习过程.针对传统的极限学习机在数据分析预测研究中存在预测精度不足的问题,提出一种基于模拟退火算法改进的极限学习机.首先,利用传统的极限学习机对训练集进行学习,得到隐藏层神经元的输出权值,选取预测结果评价标准.然后利用模拟退火算法,将传统的极限学习机隐藏层输入权值和偏置视为初始解,预测结果评价标准视为目标函数,通过模拟退火的降温过程,找到最优解即学习过程中预测误差最小的极限学习机的隐藏层神经元输入权值和偏置,最后通过传统的极限学习机计算得到隐藏层输出权值.实验选取鸢尾花分类数据和波士顿房价预测数据进行分析.实验发现与传统的极限学习机相比,基于模拟退火改进的极限学习机在分类和回归性能上都更优.     

基于压缩动量项的增量型ELM虚拟机能耗预测

在基于基础设施即服务（Infrastructure as a service,IaaS）的云服务模式下,精准的虚拟机能耗预测,对于在众多物理服务器之间进行虚拟机调度策略的制定具有十分重要的意义.针对基于传统的增量型极限学习机（Incremental extreme learning machine,I-ELM）的预测模型存在许多降低虚拟机能耗预测准确性和效率的冗余节点,在现有I-ELM模型中加入压缩动量项将网络训练误差反馈到隐含层的输出中使预测结果更逼近输出样本,能够减少I-ELM的冗余隐含层节点,从而加快I-ELM的网络收敛速度,提高I-ELM的泛化性能.     

基于半监督学习的动态神经网络结构设计

针对神经网络初始结构的设定依赖于工作者的经验、自适应能力较差等问题,提出一种基于半监督学习(SSL)算法的动态神经网络结构设计方法。该方法采用半监督学习方法利用已标记样例和无标记样例对神经网络进行训练,得到一个性能较为完善的初始网络结构,之后采用全局敏感度分析法(GSA)对网络隐层神经元输出权值进行分析,判断隐层神经元对网络输出的影响程度,即其敏感度值大小,适时地删减敏感度值很小的神经元或增加敏感度值较大的神经元,实现动态神经网络结构的优化设计,并给出了网络结构变化过程中收敛性的证明。理论分析和Matlab仿真实验表明,基于SSL算法的神经网络隐层神经元会随训练时间而改变,实现了网络结构动态设计。在液压厚度自动控制(AGC)系统应用中,大约在160 s时系统输出达到稳定,输出误差大约为0.03 mm,与监督学习(SL)方法和无监督学习(USL)方法相比,输出误差分别减小了0.03 mm和0.02 mm,这表明基于SSL算法的动态网络在实际应用中能有效提高系统输出的准确性。     

一种量子衍生神经网络模型

为提高神经网络的逼近能力,通过在普通BP网络中引入量子旋转门,提出了一种新颖的量子衍生神经网络模型. 该模型隐层由量子神经元组成,每个量子神经元携带一组量子旋转门,用于更新隐层的量子权值,输入层和输出层均为普通神经元. 基于误差反传播算法设计了该模型的学习算法. 模式识别和函数逼近的实验结果验证了提出模型及算法的有效性.     

基于权值与结构确定法的单极Sigmoid神经网络分类器

构造了以单极Sigmoid函数作为隐层神经元激励函数的神经网络分类器,网络中输入层到隐层的权值和隐层神经元的阈值均为随机生成。同时,结合利用伪逆思想一步计算出隐层和输出层神经元之间连接权值的权值直接确定(WDD)法,进一步提出了具有边增边删和二次删除策略的网络结构自确定法,用来确定神经网络最优权值和结构。数值实验结果表明,该算法能够快速有效地确定单极Sigmoid激励函数神经网络分类器的最优网络结构; 分类器的分类性能良好。     

一类反馈过程神经元网络模型及其学校算法

提出了一种基于权函数基展开的反馈过程神经元网络模型．该模型为三层结构，由输入层、过程神经元隐层和过程神经元输出层组成．输入层完成系统时变过程信号的输入及隐层过程神经元输出信号向系统的反馈；过程神经元隐层用于完成输入信号的空间加权聚合和激励运算，同时将输出信号传输到输出层并加权反馈到输入层；输出层完成隐层输出信号的空间加权聚集和对时间的聚合运算以及系统输出．文中给出了学习算法，并以旋转机械故障自动诊断问题为例验证了模型和算法的有效性．     

基于ELM-PNN算法的第24周太阳黑子预测预报

为了提高太阳黑子预测预报的精度,提出固定型极限学习过程神经网络(FELM-PNN)和增量型极限学习过程神经网络(IELM-PNN)两种学习算法.FELM-PNN的隐层节点数目固定,使用SVD求解隐层输出矩阵的Moore-Penrose广义逆,通过最小二乘法计算隐层输出权值;IELM-PNN逐次增加隐层节点,根据隐层输出矩阵和网络误差计算增加节点的输出权值.通过Henon时间序列预测验证了两种方法的有效性,并实际应用于第24周太阳黑子平滑月均值的中长期预测预报中.实验结果表明,两种方法的预测精度均有一定程度的提高,IELM-PNN的训练收敛性优于FELM-PNN.     

BP神经网络在胸癌诊断中的应用研究

为了提高胸癌识别的识别精度,提出了应用反向传播网络(Back Propagation,BP)建立胸癌诊断.BP网络是一种典型的多层前馈型神经网络,采用有监督学习模式,利用均方误差和梯度下降来实现时网络连接权值的修正.应用BP网络建立的诊断模型是三层网络结构,输入层的9个神经元分别对应描述细胞的特征值;隐层设置4个神经元;输出层的一个单元是对该细胞诊断的类别.数据实验结果显示诊断模型具有较高的识别精度,表明BP网络是一种有效的诊断方法.     

基于模型输出敏感度分析法的动态RBF神经网络设计

针对一般RBF神经网络在学习过程中网络结构不能改变的问题,提出一种动态RBF神经网络结构设计方法．算法的实质是利用敏感度分析法（SA）对神经网络模型的输出进行分析,通过判断隐含层神经元的输出对整个网络输出的影响,删除RBF隐含层中冗余的神经元,实现对神经网络的动态修剪．非线性函数逼近结果及动态系统建模结果表明,该动态RBF神经网络具有较好的性能;与最小RBF（MRBF）神经网络相比,采用所提算法能得到更小的检测误差和更短的训练时间,最终网络结构紧凑．     

Length-Changeable Incremental Extreme Learning Machine

Extreme learning machine (ELM) is a learning algorithm for generalized single-hidden-layer feed-forward networks (SLFNs). In order to obtain a suitable network architecture, Incremental Extreme Learning Machine (I-ELM) is a sort of ELM constructing SLFNs by adding hidden nodes one by one. Although kinds of I-ELM-class algorithms were proposed to improve the convergence rate or to obtain minimal training error, they do not change the construction way of I-ELM or face the over-fitting risk. Making the testing error converge quickly and stably therefore becomes an important issue. In this paper, we proposed a new incremental ELM which is referred to as Length-Changeable Incremental Extreme Learning Machine (LCI-ELM). It allows more than one hidden node to be added to the network and the existing network will be regarded as a whole in output weights tuning. The output weights of newly added hidden nodes are determined using a partial error-minimizing method. We prove that an SLFN constructed using LCI-ELM has approximation capability on a universal compact input set as well as on a finite training set. Experimental results demonstrate that LCI-ELM achieves higher convergence rate as well as lower over-fitting risk than some competitive I-ELM-class algorithms.     

改进式混合增量极限学习机算法

针对增量型极限学习机(I-ELM) 中存在大量降低学习效率及准确性的冗余节点的问题, 提出一种基于Delta 检验(DT) 和混沌优化算法(COA) 的改进式增量型核极限学习算法. 利用COA的全局搜索能力对I-ELM 中的隐含层节点参数进行寻优, 结合DT 算法检验模型输出误差, 确定有效的隐含层节点数量, 从而降低网络复杂程度, 提高算法的学习效率; 加入核函数可增强网络的在线预测能力. 仿真结果表明, 所提出的DCI-ELMK 算法具有较好的预测精度和泛化能力, 网络结构更为紧凑.

     

Convex incremental extreme learning machine

Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17(4) (2006) 879–892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis function (RBF) hidden nodes (according to any continuous sampling distribution) can work as universal approximators and the resulting incremental extreme learning machine (I-ELM) outperforms many popular learning algorithms. I-ELM randomly generates the hidden nodes and analytically calculates the output weights of SLFNs, however, I-ELM does not recalculate the output weights of all the existing nodes when a new node is added. This paper shows that while retaining the same simplicity, the convergence rate of I-ELM can be further improved by recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. Furthermore, we show that given a type of piecewise continuous computational hidden nodes (possibly not neural alike nodes), if SLFNs can work as universal approximators with adjustable hidden node parameters, from a function approximation point of view the hidden node parameters of such “generalized” SLFNs (including sigmoid networks, RBF networks, trigonometric networks, threshold networks, fuzzy inference systems, fully complex neural networks, high-order networks, ridge polynomial networks, wavelet networks, etc.) can actually be randomly generated according to any continuous sampling distribution. In theory, the parameters of these SLFNs can be analytically determined by ELM instead of being tuned.     

基于多级神经元的神经网络及其在分类中的应用

为了提高前向神经网络的分类能力,该文将多级神经元扩展使用到多层感知器的输出层和隐含层中,并提出了量子神经网络的学习算法。通过一个实际的分类问题实验验证了该方法的有效性。实验表明,无论输出层或隐含采用多级神经元,都可以带来分类能力的提高。而当输出层采用多级神经元时,还可以导致连接的减少和训练速度的加快。     

Performance enhancement of extreme learning machine for multi-category sparse data classification problems

This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for multi-category sparse data classification problems. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for sparse data classification problem depends critically on three free parameters. They are, the number of hidden neurons, the input weights and the bias values which need to be optimally chosen. Selection of these parameters for the best performance of ELM involves a complex optimization problem.In this paper, we present a new, real-coded genetic algorithm approach called ‘RCGA-ELM’ to select the optimal number of hidden neurons, input weights and bias values which results in better performance. Two new genetic operators called ‘network based operator’ and ‘weight based operator’ are proposed to find a compact network with higher generalization performance. We also present an alternate and less computationally intensive approach called ‘sparse-ELM’. Sparse-ELM searches for the best parameters of ELM using K-fold validation. A multi-class human cancer classification problem using micro-array gene expression data (which is sparse), is used for evaluating the performance of the two schemes. Results indicate that the proposed RCGA-ELM and sparse-ELM significantly improve ELM performance for sparse multi-category classification problems.     

Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems

In this paper, we present a fast learning fully complex-valued extreme learning machine classifier, referred to as ‘Circular Complex-valued Extreme Learning Machine (CC-ELM)’ for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational/rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like (‘sech’) activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. This paper also presents an analytical proof to show that the decision boundaries of a single complex-valued neuron at the hidden and output layers of CC-ELM consist of two hyper-surfaces that intersect orthogonally. These orthogonal boundaries and the input circular transformation help CC-ELM to perform real-valued classification tasks efficiently.Performance of CC-ELM is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository. Finally, the performance of CC-ELM is compared with existing methods on two practical problems, viz., the acoustic emission signal classification problem and a mammogram classification problem. These study results show that CC-ELM performs better than other existing (both) real-valued and complex-valued classifiers, especially when the data sets are highly unbalanced.     

Dynamic programming approach to optimal weight selection inmultilayer neural networks

A novel algorithm for weight adjustments in a multilayer neural network is derived using the principles of dynamic programming. The algorithm computes the optimal values for weights on a layer-by-layer basis starting from the output layer of the network. The advantage of this algorithm is that it provides an error function for every hidden layer expressed entirely in terms of the weights and outputs of the hidden layer, and minimization of this error function yields the optimum weights for the hidden layer.     

An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances

This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for classifying power system disturbances using particle swarm optimization (PSO). Learning time is an important factor while designing any computational intelligent algorithms for classifications. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are chosen randomly and the output weights are calculated analytically. However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. One of the advantages of ELM over other methods is that the parameter that the user must properly adjust is the number of hidden nodes only. But the optimal selection of its parameter can improve its performance. In this paper, a hybrid optimization mechanism is proposed which combines the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the number of hidden nodes to enhance the performance of ELM. The experimental results showed the proposed algorithm is faster and more accurate in discriminating power system disturbances.     

A simple method to derive bounds on the size and to trainmultilayer neural networks

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to #1>/= p-1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving #1+1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.