基于IRBF的草原环境污染物预测模型

综合考虑传统RBF模型在污染物预测中容易出现参数学习不足、泛化能力低下等问题,构建基于IRBF网络的草原环境污染物预测模型.利用均值思想,通过最近邻聚类之后每个类的平均值作为新的聚类中心,对中心参数进行改进,利用共轭梯度下降法优化RBF网络的权值参数.实验结果表明,IRBF网络模型相比传统的RBF和BP网络模型,预测误差得到大幅度降低,在检测性能上有明显提高,对神经网络应用于草原污染物预测进行了有效探索.     

一种优化的RBF神经网络在调制识别中的应用

提出了一种基于径向基函数 (RBF) 神经网络的通信信号调制识别方法, 该方法采用模糊 C-均值 (FCM) 聚类算法对数据进行聚类, 并获取基函数的参数, 采用梯度下降法训练网络权值. 利用最优停止法对网络进行了优化, 避免了过学习现象, 提高了 RBF 网络的训练速度和泛化能力, 以实际信号数据对该网络进行性能检验, 实验结果表明了该 RBF 网络具有较高的识别精度.     

采用中心聚类与PSO的RBF网络设计方法

基于中心聚类法与微粒群（PSO）优化方法,提出一种径向基函数（RBF）网络的设计算法。算法采用中心聚类方法对输入样本数据进行聚类处理,自适应地确定RBF网络隐含层的初始参数;利用修正全局最优解计算方法的经典PSO算法优化RBF网络隐含层参数,进一步修正网络结构参数;输出层权值采用带遗忘因子的递推最小二乘算法在线更新。采用该方法建立炼铁过程中烧结矿成分与转鼓强度关系的预测模型,并用现场数据加以验证;实验结果表明该方法收敛速度快,所建立的模型具有较高的预测精度,可用于复杂非线性系统建模。     

自构造RBF神经网络及其参数优化

径向基函数神经网络的构造需要确定每个RBF的中心、宽度和数目。该文利用改进的聚类算法自动构造RBFN,考虑样本的类别属性,根据样本分布自动计算RBF的中心和宽度,并确定RBF的数目。所有的网络参数采用非线性优化算法来优化。通过IRIS分类问题和混沌时间序列预测评价自构建RBFN的性能,验证参数优化效果。结果表明,自构造RBFN不但能够自动确定网络结构,而且具有良好的模式分类和函数逼近能力。通过对网络参数的非线性优化,该算法明显改善了网络性能。     

粗糙集意义下的一种RBF 神经网络设计方法

提出一种集成粗糙集理论的RBF网络设计方法.由布尔逻辑推理方法进行属性离散化,得到初始决策模式集,通过差异度对初始决策模式的相似度进行衡量并实现聚类,以聚类决策模式构造RBF网络.为加快训练速度,分别对隐层参数和输出权值采用BP算法和线性最小二乘滤波法进行训练.实验结果表明,该方法设计的RBF网络结构简洁,泛化性能良好,混合学习算法的收敛速度优于单纯的BP算法.     

基于FPGA及RBF神经网络的电磁无损检测技术

电磁无损检测技术是无损检测领域的一个研究重点,针对电磁无损检测技术中的超声波处理,提出了一种基于FPFA的参数优化的RBF神经网络;首先,通过FPGA编程实现对电磁超声波信号的采集,设计了放大电路将原始的电磁超声波进行放大处理已满足RBF神经网络的需求;提出一种采用K-means聚类算法来计算RBF中径向基函数的中心和宽度的参数优化RBF算法,K-means聚类算法的初始聚类中心难以确定会导致RBF算法的参数无法优化,提出KL散度,采用数据密度分析法来计算K-means算法的聚类中心;试验表明,改进后的K-means算法的聚类误差的数量级为10~(-12),传统K-means算法的聚类误差为10~(-13),改进后的K-means算法的聚类结果更准;参数优化后的RBF神经神级网络对具有1.02 mm缺陷长度的发动机涡轮叶片的缺陷长度预测结果为0.9~1.1 mm,传统的RBF神经网络的预测结果为0.7~1.2 mm,参数优化后的RBF神经网络的预测结果更准确。     

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。     

基于动态聚类RBF网络的小企业信贷预测研究

利用动态聚类算法确定RBF网络的隐含层节点,不仅聚类速度快,而且隐含层节点数的优化提高了网络的利用效率;定义了广义重要度欧氏距离用于算法中的距离计算;根据穆迪、安德尔违约概率曲线定义了信用评级风险系数等指标。最后,以南京某商业银行数据为依据,利用Matlab为工具平台,建立基于动态聚类的RBF神经网络模型。实证分析表明:本信贷预测模型对违约小企业的判别准确率较高,可为银行有效地甄别高风险企业。     

基于RBF网络的机械手运动学逆解

通过对RBF网络学习算法的分析,并在此基础上对最近邻聚类算法进行改进,以提高训练速度;并针对最近邻聚类算法整体拟舍效果不十分理想.对网络的输出进行修正,提高了求解精度.采用所提出的改进算法训练RBF网络,建立机械手逆运动学模型.仿真结果表明了该算法的有效性.     

车辆行驶安全运行决策研究

以高速公路为背景,通过综合考虑车辆的行驶状态、驾驶员疲劳程度以及天气状况等因素,对车辆主动安全运行决策进行了研究.目的是根据车辆当前状态,结合运行参数提出操作指导,供驾驶员参考.算法部分引入了RBF径向基网络用于计算各个特征指标对运行模式的影响系数,并利用模糊积分方法,在基于特征层次融合多种性能指标信息的同时,结合主观...     

Reformulated radial basis neural networks trained by gradientdescent

This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBFs. The form of the RBFs is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ones, which lead to Gaussian RBFs. This paper also proposes a supervised learning algorithm based on gradient descent for training reformulated RBF neural networks constructed using the proposed approach. A sensitivity analysis of the proposed algorithm relates the properties of RBFs with the convergence of gradient descent learning. Experiments involving a variety of reformulated RBF networks generated by linear and exponential generator functions indicate that gradient descent learning is simple, easily implementable, and produces RBF networks that perform considerably better than conventional RBF models trained by existing algorithms     

Evolutional RBFNs prediction systems generation in the applications of financial time series data

An artificial neural prediction system is automatically developed with the combinations of step wise regression analysis (SRA), dynamic learning and recursive-based particle swarm optimization (RPSO) learning algorithms. In the first stage, the SRA can be considered like a data filtering machine to choose two primary factors from 20 channel technical indexes as input variables of the RBFNs system. Then, an efficient dynamic learning algorithm is applied to sequentially generate RBFs functions from training data set, where it can efficiently determine the proper number of RBFs’ centers and their associated positions. It can be exploited to forecast appropriate behaviors of the wanted identified financial time series data. While characteristics of training data set are automatically mined and generated by the proposed dynamic learning algorithm, architecture of the RBFNs prediction system is initially represented with collected information. Moreover, the RPSO learning scheme with the hybrid particle swarm optimization (PSO) and recursive least-squares (RLS) learning methods are applied to extract those appropriate parameters of the RBFNs prediction system.The RBFNs prediction systems are implemented in data analysis, module generation and price trend of the financial time series data. It not only automatically determines proper RBFs number but also fast approach the desired target in actual trading of Taiwan stock index (TAIEX). Computer simulations in training and testing phases of historic TAIEX are compared with other learning methods, which illustrate our great performance not only increases the accuracy of the stock price prediction but also improves the win rate in the trend of TAIEX.     

Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of their radial basis functions (RBFs). The experiments verify that quantum neural networks (QNNs) and cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is not shared by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks (FFNNs). Finally, this study leads to a simple classification strategy that can be used to improve the classification accuracy of QNNs and cosine RBFNNs by rejecting ambiguous feature vectors based on their responses.     

Median radial basis function neural network

Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approach uses classical estimation in the learning stage and it is based on the learning vector quantization algorithm and its second-order statistics extension. After the presentation of this approach, we introduce the median radial basis function (MRBF) algorithm based on robust estimation of the hidden unit parameters. The proposed algorithm employs the marginal median for kernel location estimation and the median of the absolute deviations for the scale parameter estimation. A histogram-based fast implementation is provided for the MRBF algorithm. The theoretical performance of the two training algorithms is comparatively evaluated when estimating the network weights. The network is applied in pattern classification problems and in optical flow segmentation.     

基于FCM的神经网络建模及其在智能驾驶中应用研究

首先对模糊c-均值聚类算法进行了分析,然后把改进的FCM算法和RBF神经网络结 合起来建模,得到一种映射能力较强的自组织RBF神经网络.最后把它应用到智能驾驶中对驾 驶员的熟练程度和疲劳程度进行识别,得到了满意的结果.     

基于结构性质保持和相关性学习的多标记分类算法

现有的多标记学习技术大多只考虑了相关性学习问题而忽略了数据因变换而引起的结构性质不一致问题,导致原始特征数据的结构性质因映射变换发生改变,从而影响了模型的分类性能。为了解决这一问题,提出了基于结构性质保持和相关性学习的多标记分类算法。首先,构造了线性映射函数以实现特征空间与标记空间的映射;然后借鉴图正则化思想,引入基于特征数据的结构性质保持策略以降低特征数据因线性变换引起的结构性质差异;最后,针对标记数据引入基于标记对的相关性学习策略进一步优化算法参数,以提高模型的分类性能。在不同规模的标准数据集上进行测试,结果表明所提算法与一些流行的多标记分类算法相比具有更优的分类性能,验证了所提算法的有效性。     

基于Cubic映射的灰狼优化算法及应用

针对标准的灰狼优化算法GWO对于复杂优化问题的求解易陷入局部最优的缺点,从混沌初始化和非线性控制策略2个角度,提出一种基于Cubic映射和反向学习的灰狼优化算法COGWO。首先,利用Cubic映射和反向学习策略对种群进行初始化,并通过非线性参数控制策略来调节寻优过程中的参数;然后,对6种基准测试函数进行寻优实验,实验结果表明,COGWO算法具有更好的收敛精度、收敛速度和稳定性;最后,将COGWO算法应用到了实际的工程优化问题中。     

Variable activation function extreme learning machine based on residual prediction compensation

For solving the problem that extreme learning machine (ELM) algorithm uses fixed activation function and cannot be residual compensation, a new learning algorithm called variable activation function extreme learning machine based on residual prediction compensation is proposed. In the learning process, the proposed method adjusts the steep degree, position and mapping scope simultaneously. To enhance the nonlinear mapping capability of ELM, particle swarm optimization algorithm is used to optimize variable parameters according to root-mean square error for the prediction accuracy of the mode. For further improving the predictive accuracy, the auto-regressive moving average model is used to model the residual errors between actual value and predicting value of variable activation function extreme learning machine (V-ELM). The prediction of residual errors is used to rectify the prediction value of V-ELM. Simulation results verified the effectiveness and feasibility of this method by using Pole, Auto-Mpg, Housing, Diabetes, Triazines and Stock benchmark datasets. Also, it was implemented to develop a soft sensor model for the gasoline dry point in delayed coking and some satisfied results were obtained.     

Intuitionistic fuzzy C-regression by using least squares support vector regression

This paper proposes a novel intuitionistic fuzzy c-least squares support vector regression (IFC-LSSVR) with a Sammon mapping clustering algorithm. Sammon mapping effectively reduces the complexity of raw data, while intuitionistic fuzzy sets (IFSs) can effectively tune the membership of data points, and LSSVR improves the conventional fuzzy c-regression model. The proposed clustering algorithm combines the advantages of IFSs, LSSVR and Sammon mapping for solving actual clustering problems. Moreover, IFC-LSSVR with Sammon mapping adopts particle swarm optimization to obtain optimal parameters. Experiments conducted on a web-based adaptive learning environment and a dataset of wheat varieties demonstrate that the proposed algorithm is more efficient than conventional algorithms, such as the k-means (KM) and fuzzy c-means (FCM) clustering algorithms, in standard measurement indexes. This study thus demonstrates that the proposed model is a credible fuzzy clustering algorithm. The novel method contributes not only to the theoretical aspects of fuzzy clustering, but is also widely applicable in data mining, image systems, rule-based expert systems and prediction problems.     

Learning task applied to identification of a marine vehicle

This paper describes a learning algorithm to be applied in process control techniques, covering several topics of control applications such as system identification, or process supervision in a simple and useful way which make the method reliable to be applied on industrial process control. Knowledge acquired by means of a proposed learning algorithm is stored into a DAM or FAM (deterministic or fuzzy associative memory) for finally be applied on model parameter mapping. With such mappings, it is possible to know if the process is being disturbed and the intensity of such excitation inputs when no variation exist in system parameters or it is also possible to know the variation of system parameters when all input forces (control force and disturbances) are known.