混沌神经网络的动态阈值控制

提出了混沌神经网络的动态阈值控制方法, 将大脑脑电波的主要成分, 正弦信号作为控制变量实现对混沌神经网络内部状态的阈值动态改变, 从而达到了控制混沌神经网络混沌的目的. 利用该方法可以将混沌神经网络的输出稳定在一个与网络初始模式相关的存储模式和其反相模式上, 从而使混沌神经网络在模式识别、信息搜索等信息处理功能得以实现. 该控制方法不需要事先指定阈值, 是一种自适应方法, 符合实际人脑的思维运动. 关键词： 混沌控制 混沌神经网络 动态阈值控制 信息处理     

混沌神经网络的延时反馈控制研究

针对混沌神经网络，提出了一种改进的延时反馈控制方法. 利用该方法，当延时参数τ为奇数时，被控神经网络收敛于记忆模式以及它的反相模式的2周期上. 若选取不同的延时参数，被控网络则收敛于不同的周期态上. 关键词： 控制混沌 延时反馈控制 混沌神经网络     

非线性系统混沌运动的神经网络控制

设计前馈反传神经网络控制非线性系统混沌运动的新方法.根据扰动参数模型输入输出数据,按照非线性学习算法训练网络产生系统稳定所需的小扰动控制信号,去镇定混沌运动,使嵌入在混沌吸引子中的不稳定周期轨道回到稳定不动点上.Hnon映射数值仿真结果表明,这种方法控制非线性混沌系统响应速度快、控制精度高 关键词： 混沌控制 神经网络 吸引子 非线性     

混沌神经网络逆控制的同步及其在保密通信系统中的应用

利用神经网络的学习、逼近能力构造混沌神经网络，提出逆控制混沌同步方法来同步两个混沌神经网络，并基于逆控制和混沌神经网络的同步给出一种新的混沌保密通信系统.理论分析和数值实验结果表明，新系统能够有效地克服信道噪声对信息传输的不良影响，具有较强通用性和柔韧性，且有同步速度快，信号恢复精度高和密钥量大的优点. 关键词： 混沌同步 自适应逆控制 混沌神经网络 保密通信     

混沌系统的遗传神经网络控制

提出遗传神经网络控制混沌新方法.将小扰动技术和周期控制技术结合起来，用遗传算法训练神经网络，使之成为混沌控制器.对Henon映射和Logistic映射的仿真结果说明控制器能产生小扰动控制序列信号，将混沌振荡转变成规则运动状态.该方法无需了解动态系统数学模型，具有一定抗噪声干扰能力，可将它推广应用到其他混沌系统的控制中. 关键词： 遗传算法 神经网络 混沌 周期控制     

不确定混沌系统的径向基函数神经网络反馈补偿控制

对不确定混沌系统控制问题, 研究了一种基于径向基函数神经网络(radial basis function neural network, RBFNN)的反馈补偿控制方法. 该方法首先用RBFNN对混沌系统的动力学特性进行学习, 然后用训练好的RBFNN模型对混沌系统进行反馈补偿控制. 该方法的特点是不需要被控混沌系统的数学模型,可以快速跟踪任意给定的参考信号. 数值仿真试验表明了该控制方法不仅具有响应速度快、控制精度高, 而且具有较强的抑制混沌系统参数摄动能力和抗干扰能力.     

混沌的模糊神经网络逆系统控制

提出用Sugeno型的模糊推理神经网络建立混沌系统的逆系统模型,并采用逆系统方法进行混沌的控制.这种方法的特点是可以不必建立混沌系统的解析模型,通过模糊神经网络学习混沌系统的运动规律,通过学习获得的规律对混沌进行有效的控制,并且该控制方法可以控制混沌系统以一定精度跟踪连续变化的给定信号.理论分析及针对虫口模型和Henon模型仿真研究证明了该方法的有效性 关键词： 混沌 模糊神经网络 逆系统控制     

不确定混沌系统的直接自适应模糊神经网络控制

提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法.采用Takagi-Sug eno模糊逻辑系统估计混沌对象中未知函数，然后再对模糊神经网络控制律参数进行在线调 整，在系统所有信号一致有界情形下，解决混沌状态跟踪给定参考轨道控制问题.仿真结果 表明所得结论是正确的. 关键词： 模糊神经网络 自适应控制 混沌 参数不确定性     

基于改进的径向基函数神经网络的铁磁谐振系统混沌控制

面向中性点直接接地电力系统发生的铁磁谐振过电压所显现的混沌特性，在径向基函数神经网络的基础上，提出引进一种极大熵学习算法对该混沌系统进行控制.该方法通过最优化一个目标函数导出中心向量的学习规则，充分利用网络隐层的聚类功能，极大改善网络的回归和学习能力.对具体的铁磁谐振系统的数值实验证实了该方法在针对铁磁谐振过电压混沌控制中的有效性和可行性. 关键词： 中性点直接接地系统 混沌控制 径向基函数 极大熵原理     

受扰统一混沌系统基于RBF网络的主动滑模控制

针对受外扰影响的统一混沌系统,提出一种基于径向基函数(RBF)神经网络的主动滑模自适应控制方法.将被控系统分解为受控子系统和自由子系统,利用主动控制思想,建立受控子系统在目标点处的状态误差的可控标准型,设计出一个结构简单的基于滑模趋近率在线参数整定的RBF函数神经网络控制器,并且基于Lyapunov稳定性理论分析了系统的稳定性.仿真结果表明该控制器对系统参数突变和外部干扰具有鲁棒性,同时抑制了抖振. 关键词： 统一混沌系统 主动控制 滑模控制 RBF网络     

卷积神经网络用于近红外光谱预测土壤含水率

近红外光谱分析技术在土壤含水率预测方面具有独特的优势,是一种便捷且有效的方法。卷积神经网络作为高性能的深度学习模型,能够从复杂光谱数据中自主提取有效特征结构进行学习,与传统的浅层学习模型相比具有更强的模型表达能力。将卷积神经网络用于近红外光谱预测土壤含水率,并提出了有效的卷积神经网络光谱回归建模方法,简化了光谱数据的预处理要求,且具有更高的光谱预测精度。首先对不同含水率下土壤样品的光谱反射率数据进行简单的预处理,通过主成分分析减少光谱数据量,并将处理后的光谱数据变换为二维光谱信息矩阵,以适应卷积神经网络特殊的学习结构。然后基于卷积神经网络算法,设置双层卷积和池化结构逐层提取光谱数据的内部特征信息,并采用局部连接和权值共享减少网络参数、提高泛化性能。通过试验优化网络结构和各项参数,最终获得针对土壤光谱数据的卷积神经网络土壤含水率预测模型,并与传统的BP,PLSR和LSSVM模型进行对比实验。结果表明在训练样本达到一定数量时,卷积神经网络的预测精度和回归拟合度均高于三种传统模型。在少量训练样本参与建模的情况下,模型预测表现高于BP神经网络,但略低于PLSR和LSSVM模型。随着参与训练样本量的增加,卷积神经网络的预测精度和回归拟合度也随之稳定提升,达到并显著优于传统模型水平。因此,卷积神经网络能够利用近红外光谱数据对土壤含水率做出有效预测,且在较多样本参与建模时取得更好效果。     

Image noise cancellation using linear matrix inequality and cellular neural network

In this paper, the technique of image noise cancellation is presented by employing cellular neural networks (CNN) and linear matrix inequality (LMI). The main objective is to obtain the templates of CNN by using a corrupted image and a corresponding desired image. A criterion for the uniqueness and global asymptotic stability of the equilibrium point of CNN is presented based on the Lyapunov stability theorem (i.e., the feedback template “A” of CNN is solved at this step), and the input template “B” of CNN is designed to achieve desirable output by using the property of saturation nonlinearity of CNN. It is shown that the problem of image noise cancellation can be characterized in terms of LMIs. The simulation results indicate that the proposed method is useful for practical application.     

Stochastic optimization of spin-glasses on cellular neural/nonlinear network based processors

Nowadays, Cellular Neural/Nonlinear Networks (CNN) are practically implemented in parallel, analog computers, showing a fast developing trend. It is important also for physicists to be aware that such computers are appropriate for implementing in an elegant manner practically important algorithms, which are extremely slow on the classical digital architecture. Here, CNN is used for optimization of spin-glass systems. We prove, that a CNN in which the parameters of all cells can be separately controlled, is the analog correspondent of a two-dimensional Ising type spin-glass system. Using the properties of CNN we show that one single operation on the CNN chip would yield a local minimum of the spin-glass energy function. By using this property a fast optimization method, similar to simulated annealing, can be built. After estimating the simulation time needed for this algorithm on CNN based computers, and comparing it with the time needed on normal digital computers using the classical simulated annealing algorithm, the results look promising: a speed-up of the order 10¹² is expected already at 50×50 lattice sizes. Hardwares realized nowadays are of 128×128 size. Also, there seem to be no technical difficulties adapting CNN chips for such problems and the needed local control of the parameters could be fully developed in the near future.     

Recognizing pedestrian’s unsafe behaviors in far-infrared imagery at night

Pedestrian behavior recognition is important work for early accident prevention in advanced driver assistance system (ADAS). In particular, because most pedestrian-vehicle crashes are occurred from late of night to early of dawn, our study focus on recognizing unsafe behavior of pedestrians using thermal image captured from moving vehicle at night. For recognizing unsafe behavior, this study uses convolutional neural network (CNN) which shows high quality of recognition performance. However, because traditional CNN requires the very expensive training time and memory, we design the light CNN consisted of two convolutional layers and two subsampling layers for real-time processing of vehicle applications. In addition, we combine light CNN with boosted random forest (Boosted RF) classifier so that the output of CNN is not fully connected with the classifier but randomly connected with Boosted random forest. We named this CNN as randomly connected CNN (RC-CNN). The proposed method was successfully applied to the pedestrian unsafe behavior (PUB) dataset captured from far-infrared camera at night and its behavior recognition accuracy is confirmed to be higher than that of some algorithms related to CNNs, with a shorter processing time.     

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Multi-focus-image-fusion is a crucial embranchment of image processing. Many methods have been developed from different perspectives to solve this problem. Among them, the sparse representation (SR)-based and convolutional neural network (CNN)-based fusion methods have been widely used. Fusing the source image patches, the SR-based model is essentially a local method with a nonlinear fusion rule. On the other hand, the direct mapping between the source images follows the decision map which is learned via CNN. The fusion is a global one with a linear fusion rule. Combining the advantages of the above two methods, a novel fusion method that applies CNN to assist SR is proposed for the purpose of gaining a fused image with more precise and abundant information. In the proposed method, source image patches were fused based on SR and the new weight obtained by CNN. Experimental results demonstrate that the proposed method clearly outperforms existing state-of-the-art methods in addition to SR and CNN in terms of both visual perception and objective evaluation metrics, and the computational complexity is greatly reduced. Experimental results demonstrate that the proposed method not only clearly outperforms the SR and CNN methods in terms of visual perception and objective evaluation indicators, but is also significantly better than other state-of-the-art methods since our computational complexity is greatly reduced.     

A Signal processing method of OFDM communication receiver based on CNN

Orthogonal frequency division multiplexing (OFDM) the signal processing is a key issue in wireless communication research. The multipath effect and Doppler shift of wireless communication channels can lead to distortion of the transmitted signal, which poses a considerable challenge to the information recovery of communication receivers. This paper presents the signal processing method of OFDM communication based on convolutional neural network (CNN). The method replaces all signal processing modules of the OFDM communication receiver with CNN, and the information is recovered by the CNN. In order to adapt to the processing of communication signals, we designed a one-dimensional convolutional neural network (1D-CONV-CNN) model as the neural network structures by this method. Simulation results indicate that the signal processing method effectively reduces the bit error rate (BER) and improves its performance compared with the conventional reception method under different channel conditions.     

A Security-Enhanced Image Communication Scheme Using Cellular Neural Network

In the current network and big data environment, the secure transmission of digital images is facing huge challenges. The use of some methodologies in artificial intelligence to enhance its security is extremely cutting-edge and also a development trend. To this end, this paper proposes a security-enhanced image communication scheme based on cellular neural network (CNN) under cryptanalysis. First, the complex characteristics of CNN are used to create pseudorandom sequences for image encryption. Then, a plain image is sequentially confused, permuted and diffused to get the cipher image by these CNN-based sequences. Based on cryptanalysis theory, a security-enhanced algorithm structure and relevant steps are detailed. Theoretical analysis and experimental results both demonstrate its safety performance. Moreover, the structure of image cipher can effectively resist various common attacks in cryptography. Therefore, the image communication scheme based on CNN proposed in this paper is a competitive security technology method.     

Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks

This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network(CNN).To promote the detection performance and efficiency,we proposed a scheme based on wavelet packet(WP)decomposition and CNN.The WP decomposition is a time-frequency method and can enhance the discriminant features between gravitational wave signal and noise before detection.The CNN conducts the gravitational wave detection by learning a function mapping relation from the data under being processed to the space of detection results.This function-mapping-relation style detection scheme can detection efficiency significantly.In this work,instrument effects are con-sidered,and the noise are computed from a power spectral density(PSD)equivalent to the Advanced LIGO design sensitivity.The quantitative evaluations and comparisons with the state-of-art method matched filtering show the excellent performances for BNS gravitational wave detection.On efficiency,the current experiments show that this WP-CNN-based scheme is more than 960 times faster than the matched filtering.     

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.