Inverse stochastic resonance in modular neural network with synaptic plasticity

This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.

MNN-XSS:Modular Neural Network Based Approach for XSS Attack Detection

The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic.In recent years,researchers have used different machine learning methods to detect network attacks without relying on those signatures.The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product.In this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system.Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample.The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks.The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Synchronization transition of a modular neural network containing subnetworks of different scales

Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.

基于注意力模块化神经网络的城市固废焚烧过程氮氧化物排放预测

氮氧化物(nitrogen oxides,NO_(x))浓度的实时精准检测是实现脱硝过程闭环控制的前提,对提高城市固废焚烧(municipal solid waste incineration,MSWI)过程脱硝效率具有重要意义。为此,提出了一种基于注意力模块化神经网络(attention modular neural network,AMNN)的MSWI过程NO_(x)排放预测方法。首先,模拟脑网络“分而治之”处理复杂任务的特性,利用模糊C均值(fuzzy C-means,FCM)聚类算法将待预测任务划分为多个子任务,从而降低预测任务复杂度;其次,针对各子任务,设计一种自组织模糊神经网络(self-organizing fuzzy neural network,SOFNN)构建子模型,通过神经元增删机制和二阶学习算法提高子模型的学习效率和学习精度;然后,提出了一种基于注意力机制的子模型整合策略,进一步提高预测模型的泛化性能;最后,通过基准实验Mackey-Glass时间序列预测和北京某MSWI厂实际数据验证了AMNN的可行性和有效性。

基于模糊Modular神经网络的官厅水库及邻区的地震危险性估计

首先介绍了模糊Modular神经网络的原理、建模方法与仿真实验,然后利用该方法把一些常用的地震学指标作为神经网络的输入,未来50年最大震级则作为网络的期望输出,对官厅水库及邻区的地震活动进行学习与最大震级序列建模,进行危险性预测。通过分析,认为该方法在一定程度上具有学习、建模与外推预测泛化能力,具有很好的中长期地震危险性预测效果,可以作为中长期地震危险性分析的工具。

改进的模糊Modular神经网络在既有建筑可靠性鉴定中的应用

在Takagi-Sugeno模糊逻辑系统的基础上,提出了改进的模糊Modular神经网络模型(IF-MNN),并将该模型应用于既有建筑的可靠性鉴定。改进的模型是将传统的模糊Modular神经网络模型中的单输出改进为多输出。这种改进的多输入多输出的模糊Modular神经网络模型具有预测性能好、训练学习速度快的优点,它的系统门网络采用模糊C均值聚类算法代替K-means算法,专家网络的训练中引进了先进的Levenberg-Marquardt算法。在应用改进的模糊Modular神经网络模型对既有建筑进行可靠性鉴定的过程中,综合考虑了各主要因素对既有建筑可靠性鉴定等级的影响,并将经量化处理的影响因素作为网络的外部输入,将网络计算得到的4个输出值分别作为样本对应于不同可靠性等级的隶属度,建筑可靠性鉴定的最终评判等级为最大隶属度所对应的等级。训练和预测样本的计算结果证明了改进的模糊Modular神经网络模型在既有建筑可靠性鉴定中应用的可行性和有效性。

一种模糊Modular神经网络模型及其应用

将神经网络模糊系统与模糊C均值聚类法相结合 ,对模糊Modular神经网络进行研究 ,提出了该模糊神经网络模型的多输出结构及其学习算法 ,据此开发了模糊神经网络诊断系统 ,并将其用于某电源分系统的诊断分析 ,运行的结果表明 。

模糊化的Modular模糊神经网络降水预报模型研究

以广西西南部前汛期5、6月25个气象站平均逐日降水量作为预报对象,采用自然正交分解方法和模糊化方法对输入因子预处理后,结合Modular模糊神经网络建立了一种新的降水预报模型,并进行了逐日业务预报应用试验。结果表明,该降水预报模型比常规Modular模糊神经网络方法及逐步回归方法有更高的预报精度,具有较好的业务应用前景。

模糊Modular神经网络在黄金矿区可持续发展能力评价模型中的应用

以黄金矿区可持续发展能力的评价为研究对象 ,以矿区REES系统经济、社会、环境、资源子系统的综合发展水平、可持续发展度以及相互之间的状态协调度作为评价指标 ,建立了模糊Modular神经网络评价模型。通过实例分析 ,证明该模型具有较好的泛化、学习和映射能力 ,对类似于可持续发展能力的非线性系统评价有一定的参考价值。

Modular-tree:一个自构筑的神经网络结构(英文)

提出了一种新颖的具有自构筑能力的神经网络结构,称之为Modular-tree和两个相应的自构筑算法。在此结构中,任何现存的前馈神经网络均可以作为子网。对于一个给定的学习任务,利用提出的生成算法通过对输入空间递归地划分,自动生成一树状的模块神经网络,从而避免了网络结构预置问题。由于使用了“分治”原理,Modular-tree具有良好的性能及快速训练的能力。此结构已用于多个监督学习问题(包括:标准测试及现实世界问题)并取得令人满意的实验结果。

基于机器学习的模块化多电平换流器的模型预测控制研究

模型预测控制器(model predictive controller,MPC)因可实现多目标优化控制被广泛应用在模块化多电平换流器(modular multilevel converter,MMC)领域,但随着子模块数量增加,MPC的在线计算量成几何级数增长。因此,提出了基于机器学习的MMC的模型预测控制方法,首先利用MPC-MMC仿真平台收集数据并对数据进行预处理,再进行神经网络训练得到神经网络-MPC(neural network-MPC,NN-MPC)。为了提高神经网络训练效率,采用随机森林来优化神经网络的初始权值和阈值,得到随机森林-神经网络-MPC(random forest-neural network-MPC,RF-NN-MPC),将其用来模拟MPC。仿真结果表明,RF-NN-MPC在学习效率和学习精度方面都优于NN-MPC,在保持了良好的控制效果的同时,使MPC-MMC不受子模块数量约束,在线计算量始终为1次。

基于RT-thread的小麦病虫害检测系统的研究与应用

【目的】为解决小麦病虫害问题,设计出一款基于RT-thread物联网操作系统的小麦病虫害检测系统。【方法】该检测系统以STM32H750XBH6单片机为核心主控板,使用神经网络对小麦病虫害进行识别。同时,该检测系统采用OneNET物联网云平台来实现数据的上行,并使用OneNET view组件实现对数据的二维显示。【结果】系统稳定运行后,能在多种恶劣环境中识别出不同的小麦病虫害问题,并通过调用数据库来给出该健康状况下的解决方案。【结论】该系统不仅能降低因小麦病虫害带来的损失,还能减少人力物力的投入。

面向心血管疾病的自适应模块化神经网络预测模型

随着计算机科学技术的发展,将计算机技术应用在医疗方面进行疾病预测已经成为当前研究的热点.针对单一全互连前馈神经网络难以应对心血管疾病预测时的多维数据,并且参数和结构设计需要人工实验确定,泛化收敛的能力差等问题,本文提出一种自适应模块化神经网络结构模型(AMNN).首先,对训练数据集采用概率密度峰值聚类算法确定训练数据集的聚类中心,以此确定每个模块的训练样本集,然后每个模块采用训练BP神经网络算法,该算法可以利用分配来的训练样本数据自适应构建模块结构.实验结果表明,该模型相对标准的随机森林算法和传统单一全互连前馈神经预测心血管疾病准确率高,收敛速率快.

基于模块化神经网络的动态联盟伙伴优化选择

动态联盟的关键问题是伙伴选择问题。本文构造了动态联盟伙伴选择问题的数学模型,提出了一种基于模块化神经网络的动态联盟伙伴选择的优化方法,实现了动态联盟伙伴的优化选择。

层次模块化神经网络新方法研究

多个神经网络组成的系统有两种工作方式:其一是通过训练多个神经网络,以协同或竞争的方式来构建学习系统解决同一问题;其二则是将复杂问题分解成若干简单问题而后进行处理。应用分而治之的思想提出了一种层次模块化神经网络新方法———三层结构的模块化神经网络HMNN(HierarchicalModularNeuralNetwork)模型,在提高算法性能方面有一定优势,对比实验研究也表明该算法有效地提高了系统的泛化能力和算法稳定性。

控制图模式及其智能识别方法

控制图是现场质量控制的重要工具 ,使用控制图最为困难的问题是识别控制图中是否存在异常现象以及异常现象存在时的具体模式 .针对这一问题展开研究 ,提出了基于多 ANN神经网络模型与模糊化预处理的控制图模式智能识别方法 .经计算机模拟和应用实践表明该方法是切实可行的 .

层次模块化模糊神经网络在铝电解故障诊断中的应用

目的针对铝电解故障发生机理及特点,提出采用层次模块化模糊神经网络实现对铝电解故障快速、有效检测的新方法.方法利用数据处理子模块,诊断子模块,决策子模块,从而对铝电解生产过程中的故障进行诊断.结果数据处理子网络实现了对数据的特征提取,优化了数据结构,将大结构的神经网络分解成多个小网络,并用决策神经网络对故障进行模式分类.结论该方法优化了大结构神经网络的学习性能,提高了故障检测的准确率,具有良好的实用价值.