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1.
王晶  朱珂  汪斌强 《计算机应用》2013,33(10):2753-2756
分析用户社会属性和行为特征对微博粉丝网络演化的影响,提出一种基于用户社会属性及行为特征吸引度的微博粉丝网络演化模型SBPAF。模型引入社会属性吸引度及行为特征吸引度概念,依据吸引度优先连接的原则和第二跳连接原则增边,并引入边消亡过程,从而精确刻画现实微博粉丝网络演化过程。模型中的参数能够进行灵活调整,可以得到不同微博粉丝网络的仿真拓扑。仿真结果验证了SBPAF模型的合理性和有效性。  相似文献   

2.
何晶  李本先 《自动化学报》2019,45(11):2137-2147
恐怖组织网络是一种特殊的复杂网络,其时空演化规律反映出恐怖组织活动的特征.为更准确地理解恐怖组织网络的动态演化规律,提出一种基于多局域的恐怖组织网络择优增长演化模型,并对此模型进行了仿真与模拟.该模型能准确地描述在局部信息条件下,新节点的择优和网络的增长过程及其规律;并且利用网络信息中心度来衡量恐怖组织网络节点的信念水平,动态地刻画了恐怖组织网络的增长过程.实验结果表明:恐怖组织网络的局域度分布仍服从幂律分布,网络信息中心度具有集中与分散性的特征;最后,对多个恐怖组织网络按该模型进行仿真演化,验证了该模型的准确性与科学性.  相似文献   

3.
在分析在线社会网络的拓扑结构、特征及演化规律的基础上,借鉴了前人网络模型的思想,提出了在线社会网络演化模型,引入动态的加权方式,提出了一种在线社会网络演化模型。理论分析和仿真表明:在线社会网络演化模型具有无标度和小世界特性,点权、边权、度分布呈现幂律特性,具有较多的簇系数、较小的路径长度且可调。这种无标度和小世界特性与现实中的在线社会网络较为一致。  相似文献   

4.
近年来,P2P虚拟社区成为目前网络研究的热点之一。根据逻辑斯蒂克原理,从复杂系统理论的角度提出了P2P网络虚拟社区竞争与协作演化趋势进化过程的定量模型,讨论了P2P网络虚拟社区演化非平衡性、非线性和随机"涨落"等演化特性和作用机理,并给出P2P网络虚拟社区演化模型(EMOP2PVC)。理论分析和仿真实验均表明所提出的模型具有较好的合理性和可行性。  相似文献   

5.
陶少华  张向群 《计算机工程》2012,38(1):197-198,214
现实中有些复杂网络并不具备无尺度网络的偏好连接特性,但节点之间具有信息传递相似性。为此,研究基于自相似特征形成的复杂网络,提出一种具有自相似特征的网络演化模型。证明以节点自相似演化的网络具有自相似性,并以容量维数作为衡量尺度,揭示复杂网络的自相似性。理论分析及仿真结果表明,该模型能合理描述现实中复杂网络的演化及其特征。  相似文献   

6.
复杂网络结构演化研究中多探讨"如何形成",而忽视了 "为什么这样形成"的问题.基于合作演化的角度,利用空间囚徒困境理论,对社会网络中的个体进行了分类,并建立网络演化中个体选择的微观动力学机制,建立了社会网络的结构演化模型.使用多主体系统仿真工具Repast进行了仿真.利用度分布、聚集系数、平均最短路径及社会总收益作为演化判据,给出了网络演化的仿真结果.结果表明合作机制下的演化网络展现出明显的小世界特性,说明合作机制可以在一定程度上解释现实网络形成的原因.并且指出对于社会整体来说,即使在合作者较少的情况下,也能够通过社会关系的改善极大的提高社会的总体收益.  相似文献   

7.
本文讨论了一种特别的企业组织网络—无标度企业组织网络及其特征,并基于复杂网络理论提出了无标度企业组织网络的演化模型。该演化模型基于二种演化机制:第一种是考虑企业组织网络的初始结构—全连通结构和星形连接结构;第二种是基于局域信息的优势连接。计算机仿真结果显示:在全局择优连接下,无论企业组织网络的初始结构如何,企业组织网络都将演化无标度网络;局域择优连接仍然可能形成无标度企业组织网络。  相似文献   

8.
针对虚实互动网络环境下的双群体演化博弈问题,首先给出了一般博弈模型并进行了复制动态分析;然后,建立了双同质群体的多智能体仿真模型,并将仿真结果与复制动态分析和单同质群体进行了对比;最后,从策略更新时间、网络结构、学习机制三方面提出了双群体的异质演化机制。仿真结果表明,不同演化博弈机制下的演化稳定策略基本一致,但演化稳定策略的收敛速度及鞍点取值不同,应用时要根据实际问题的异质特征来构建恰当的博弈演化机制。  相似文献   

9.
给出了多三角形结构动态复杂网络演化模型的演化算法,利用平均场理论和MATLAB工具对模型的度分布、平均聚集系数等给出了精确的理论解与数值仿真解,验证了两种解完全吻合。利用MATLAB工具对演化模型的稳定性进行数值仿真,验证了该类演化模型与无标度网络BA演化模型在随机攻击策略下具有相似的稳定性。  相似文献   

10.
针对在线社交网络进行建模研究将有助于理解其网络特征结构和演化机制,为了提高网络模型描述在线社交网络的准确性,分析统计了新浪微博网络演化相关特征,并结合复杂网络中社团结构特征和优先连接特性提出了COMW(Community-Oriented Model for Weibo)网络演化模型。通过实验模拟验证了COMW模型的包括度分布、聚类系数、网络效率、社团结构演化等网络特征。实验表明,COMW模型具有明显的小世界特性和明显的社团结构,并在多项特征上均符合微博网络,能够较为合理地表征微博网络的演化。  相似文献   

11.
An architecture for on-line learning of time series prediction is presented which uses a series of echo state networks (ESNs). Each ESN learns to predict an error correction term for the previous ESN. This technique is demonstrated to improve prediction accuracy for on-line learning of the Mackey-Glass chaotic oscillator. The results are compared to other architectural configurations to show that the improved performance emerges from sequential ESN error correction. A new recurrent network structure is shown to be a useful simplification of the usual ESN reservoir.  相似文献   

12.
Artificial neural networks have been shown to perform well in automatic speech recognition (ASR) tasks, although their complexity and excessive computational costs have limited their use. Recently, a recurrent neural network with simplified training, the echo state network (ESN), was introduced by Jaeger and shown to outperform conventional methods in time series prediction experiments. We created the predictive ESN classifier by combining the ESN with a state machine framework. In small-vocabulary ASR experiments, we compared the noise-robust performance of the predictive ESN classifier with a hidden Markov model (HMM) as a function of model size and signal-to-noise ratio (SNR). The predictive ESN classifier outperformed an HMM by 8-dB SNR, and both models achieved maximum noise-robust accuracy for architectures with more states and fewer kernels per state. Using ten trials of random sets of training/validation/test speakers, accuracy for the predictive ESN classifier, averaged between 0 and 20 dB SNR, was 81plusmn3%, compared to 61plusmn2% for an HMM. The closed-form regression training for the ESN significantly reduced the computational cost of the network, and the reservoir of the ESN created a high-dimensional representation of the input with memory which led to increased noise-robust classification.  相似文献   

13.
Echo State Networks, ESNs, are standardly composed of additive units undergoing sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure called reservoir. Coming up with a good reservoir depends mainly on picking up the right parameters for the network initialization. Human expertise as well as repeatedly tests may sometimes provide acceptable parameters. Nevertheless, they are non-guaranteed. On the other hand, optimization techniques based on evolutionary learning have proven their strong effectiveness in unscrambling optimal solutions in complex spaces. Particle swarm optimization (PSO) is one of the most popular continuous evolutionary algorithms. Throughout this paper, a PSO algorithm is associated to ESN to pre-train some fixed weights values within the network. Once the network's initial parameters are set, some untrained weights are selected for optimization. The new weights, already optimized, are re-squirted to the network which launches its normal training process. The performances of the network are a subject of the error and the time processing evaluation metrics. The testing results after PSO pre-training are compared to those of ESN without optimization and other existent approaches. The conceived approach is tested for time series prediction purpose on a set of benchmarks and real-life datasets. Experimental results show obvious enhancement of ESN learning results.  相似文献   

14.
针对回声状态网络(Echo state network,ESN)的结构设计问题,提出基于灵敏度分析的模块化回声状态网络修剪算法(Pruning algorithm for modular echo state network,PMESN).该网络由相互独立的子储备池模块构成.首先利用矩阵的奇异值分解(Singular value decomposition,SVD)构造子储备池模块的权值矩阵,并利用分块对角阵原理生成储备池.然后利用子储备池模块输出和相应的输出层权值向量,定义学习残差对于子储备池模块的灵敏度以及网络规模适应度.利用灵敏度大小判断子储备池模块的贡献度,并根据网络规模适应度确定子储备池模块的个数,删除灵敏度低的子模块.在网络的修剪过程中,不需要缩放权值就可以保证网络的回声状态特性.实验结果说明,所提出的算法有效解决了ESN的网络结构设计问题,基本能够确定与样本数据相匹配的网络规模,具有较好的泛化能力和鲁棒性.  相似文献   

15.
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。  相似文献   

16.
Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.  相似文献   

17.
针对回声状态网络(ESN)结构设计复杂、参数选择难度大的问题,提出一种具有small world特性的ESN(SWESN).首先采用神经元空间增长算法在平面区域生成small world拓扑网络;然后根据网络节点与基准点的Euclidean距离将网络节点进行重新排序,并将平面上的物理节点及其连接映射为SWESN的内部神经元连接矩阵,从而使动态神经元池具有small world特性.实验表明,SWESN动力学特性比常规ESN更为丰富,在鲁棒性、抗干扰能力等方面均优于常规的ESN.  相似文献   

18.

This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic analysis and modeling principle of water quality soft-sensing. The common soft-sensing models mainly include a back-propagation neural network, radial basis function neural network, fuzzy neural network (FNN), echo state network (ESN), growing deep belief network and deep belief network with event-triggered learning (DBN-EL). They are compared in terms of accuracy, efficiency and computational complexity with partial-least-square-regression DBN (PLSR-DBN), growing ESN, sparse deep belief FNN, self-organizing DBN, wavelet-ANN and self-organizing cascade neural network (SCNN). In addition, this paper generally discusses and explains what factors affect the accuracy of the ANNs-based soft-sensing models. Finally, this paper points out several challenges in soft-sensing models of WWTP, which may be helpful for researchers and practitioner to explore the future solutions for their particular applications.

  相似文献   

19.
范思远  姚显双  曹生现  赵波 《自动化学报》2020,46(12):2701-2710
光伏电池温度变化影响光伏系统输出的稳定性, 精准地预测光伏电池板温度的变化趋势, 对光伏系统智能运行具有重要意义. 为了更好地预测温度的变化趋势, 本文考虑了光伏电池板温度的迟滞效应, 将先前的温度输出作为延迟项引入回声状态网中, 提出了一种基于延迟回声状态网的光伏电池板温度预测模型. 给出一个延迟回声状态网具有回声状态特性的判定条件, 使得预测模型能够稳定地预测光伏电池板温度. 同时, 建立了一套光伏多传感器监测系统, 利用该监测系统采集的数据, 训练和验证模型的准确性. 与回声状态网(Echo state network, ESN), Leaky ESN (Leaky-integrator ESN)和VML ESN (ESN with variable memory length)相比, 仿真结果表明, 本文所提出的延迟回声状态网具有更好的预测性能, 平均绝对百分比误差甚至达到3.45%.  相似文献   

20.
基于回声状态网络的多变量预测模型的研究   总被引:1,自引:0,他引:1  
考虑单变量在混沌时间序列预测中的不足,文章利用多变量模型进行混沌时间序列的预测。针对多变量预测过程中的维数过高问题,文章结合主元分析理论(PCA)和回声状态网络(ESN),构建了基于PCA和ESN的多变量混沌时间序列预测模型,将PCA降维后的时间序列数据输入ESN网络进行预测分析。论文对由Lorenz动态方程生成的三变量混沌时间序列进行了仿真实验,结果表明该模型有效地提高了预测的精度和预测的效率,是一种有效的混沌时间序列预测方法。  相似文献   

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