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     

变频正弦混沌神经网络及其应用

针对暂态混沌神经网络全局寻优能力受限的问题,提出了一种基于脑电波生物机制的新型混沌神经网络模型——变频正弦混沌神经网络.该模型将变频正弦函数和Sigmoid函数组合作为非单调激励函数,本文给出了该混沌神经元的倒分岔图及Lyapunov指数的时间演化图,分析了其动力学特性.进一步将该模型应用到非线性函数优化和组合优化问题上,并分析了参数的变化规律.仿真实验证明变频正弦混沌神经网络比暂态混沌神经网络及其他相关模型具有更好的全局寻优能力.     

混沌神经网络的控制

提出了一种针对混沌神经网络的钉扎控制方法.利用此方法对混沌神经网络进行控制,使网络的输出稳定在网络的一个存储的模式上.实现了混沌神经网络的信息搜索,并对影响混沌神经网络控制效果的几个因素进行了讨论. 关键词： 混沌控制 混沌神经网络 钉扎控制方法     

基于径向基神经网络预测的混沌时间序列嵌入维数估计方法

根据Takens定理,研究了混沌时间序列相空间重构嵌入维数的选取问题.提出了基于径向基函数神经网络预测模型性能的嵌入维数估计方法,即根据嵌入维数与混沌时间序列预测模型性能的变化关系来确定嵌入维数.通过对几种典型混沌动力学系统的数值验证,结果表明该方法能够确定出合适的相空间重构嵌入维数. 关键词： 混沌 相空间重构 嵌入维数 预测     

束晕-混沌的神经网络自适应控制

 理论分析了强流离子束在周期磁场聚焦通道中传输时产生的束晕 混沌动力学行为,给出了近似反映实际聚焦磁场的余弦函数形式。然后利用神经网络方法对非线性复杂系统控制的优越性,提出前馈反传神经网络方法对强流离子束中束晕 混沌进行自适应控制。通过适当选择的神经网络控制结构和线性反馈系数以及自适应调整神经网络的权系数,可将强流离子束的包络半径达到束匹配半径的控制目标,且束包络的抖动大小明显减少,束晕 混沌现象得到了明显的抑制。     

具有时滞的一阶细胞神经网络动态行为研究

研究了具有时滞的一阶细胞神经网络的复杂动力学行为-证明了Hopf分叉的存在性，指出了 若选择适当的参数，则该网络中可以产生混沌-计算机仿真实验表明所得结论的正确性-由于 该网络的构成较简单，因此实际中可以作为混沌发生器加以应用- 关键词： 细胞神经网络 时滞 Hopf分叉 混沌     

忆阻突触耦合环形Hopfield神经网络动力学分析及其电路实现

提出一种新型忆阻器模型, 利用标准非线性理论分析三个忆阻特性, 并设计模拟电路。基于忆阻突触, 构建一个忆阻突触耦合环形Hopfield神经网络模型。采用分岔图、李雅普诺夫指数谱、时序图等方法, 揭示与忆阻突触密切相关的特殊动力学行为。数值仿真表明: 在忆阻突触权重的影响下, 它能够产生多种对称簇发放电模式和复杂的混沌行为。实现了该忆阻环形神经网络的模拟等效电路, 并由PSIM电路仿真验证MATLAB数值仿真的正确性。     

韦达定理在动力学逆问题中的应用

提出一种分析确定频率约束条件下的动力学逆问题的求解方法,并应用此法给出了一确定问题的解,方法简单,易于编程计算,可在同类问题中推广应用     

DC-DC变换器的符号时间序列描述及模块熵分析

本文提出了一种采用符号时间序列和熵理论分析DC-DC变换器非线性行为的方法.该方法首先用离散时间序列描述非线性连续系统,然后将其转换为由简单字符构成的符号序列,再用信息学方法计算出该符号序列的模块熵,从而得到一种新的可量化的非线性动力学行为统计指标.文中以一阶电压反馈DCM和二阶电流反馈CCM Boost变换器为例进行研究.研究结果表明,模块熵这种粗粒化的统计分析方法,能够量化DC-DC变换器的倍周期分岔和混沌行为,且能够准确地确定混沌行为的发生,是一种尚未在DC-DC变换器中提出的简单、实用的分析方法. 关键词： 符号时间序列 符号动力学 模块熵 Lyapunov指数     

由确定论到混沌的计算机数值模拟

一、引言 混沌是非线性系统在远离平衡状态下,由于运动轨迹高度不稳定经多次分岔达到的一种运动状态,是一种有统计规律的序．哈肯曾指出：“混沌性来源于决定论性方程的无规则运动．”那么确定论系统又是如何产生混沌的呢？由自然界中广泛存在的混沌现象,我们可以知道系统产生混沌归咎于非线性条件．单摆是物理学中的一个简单模型,是确定论的典型例子,但在有阻尼的情况下,受周期性驱动力作用之后,其运动规律就将发生变化,出现周期的分岔,在一定大小的驱动力条件下出现混沌行为．为了将这一由确定论走向混沌的全过程完整地演示出来…     

基于BP神经网络的排爆机械臂逆运动学分析

机械臂逆运动学是已知末端执行器的位姿求解机械臂各关节变量,主要用于机械臂末端执行器的精确定位和轨迹规划,如何高效的求解机械臂运动学逆解是机械臂轨迹控制的难点。针对传统的机械臂逆运动学求解方法复杂且存在多解等问题,提出一种基于BP神经网络的机械臂逆运动学求解方法。以四自由度机械臂为研究对象,对其运动学原理进行分析,建立BP神经网络模型并对神经网络算法进行改进,最后使用MATLAB进行仿真验证。仿真结果表明：使用BP神经网络模型求解机械臂逆运动学问题设计过程简单,求解精度较高,一定程度上避免了传统方法的不足,是一种可行的机械臂逆运动学求解方法。     

A New Approach for Controlling Chaos Based on Direct Optimizing Predictive Control

We introduce the predictive control theory into the study of chaos control and propose a direct optimizing predictive control algorithm based on a neural network model. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. Compared with the existing similar algorithms, the proposed control system has faster response, so it requires much shorter time for the stabilization of the chaotic systems.The proposed approach can control hyperchaos and the algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulations demonstrate the effectiveness of the algorithm.     

Collision avoidance for a mobile robot based on radial basis function hybrid force control technique

Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function (RBF) neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.     

Discontinuity and Complex Dynamics of Neural Networks

We focus on the discontinuity of a neural network model with diluted and clipped synaptic connections (±l only). The exact evolution rule of the average firing rate becomes a discontinuous piece-wise nonlinear map when very simple functions of dynamical threshold are introduced into the network. Complex dynamics is observed.     

混沌光学系统之前向神经网络混沌加速的系统辨识研究

研究了利用前向神经网络对混沌光学系统进行混沌加速系统辨识的可能性，计算机数值仿真发现，利用三层前向神经网络混沌光学系统辨识器。在基于混沌动力学角度的修正ＢＰ算法（混沌加速ＢＰ算法）支持下可克服由常规ＢＰ算法导致的辨识时间长的缺点，在较少的训练次数内即可对布拉格声双稳混沌系统进行良好的系统辨识，此研究结果表明，在混沌加速ＢＰ算法支持下，三层前向神经网络可用来快速处理混沌光学时间序列以进行相应的动力学     

Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models

Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent dynamics. On the other hand, emerging evidence suggests that dynamic functional connectivities (DFC) may be responsible for neural activity patterns underlying cognition or behavior. We are interested in studying how DFC are associated with the low-dimensional structure of neural activities. Most existing LVMs are based on a point process and fail to model evolving relationships. In this work, we introduce a dynamic graph as the latent variable and develop a Variational Dynamic Graph Latent Variable Model (VDGLVM), a representation learning model based on the variational information bottleneck framework. VDGLVM utilizes a graph generative model and a graph neural network to capture dynamic communication between nodes that one has no access to from the observed data. The proposed computational model provides guaranteed behavior-decoding performance and improves LVMs by associating the inferred latent dynamics with probable DFC.     

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.     

种荧光光谱法和PSO-BP神经网络相结合的山梨酸钾浓度测定的新方法

研究了山梨酸钾在水溶液和橙汁中的荧光特性,结果表明在两种溶液中山梨酸钾的荧光特性虽然有很大的区别,但是它们的荧光特征峰都存在于λ_ex/λ_em=375/490 nm。从二维荧光光谱可以看出,橙汁中山梨酸钾的浓度和相对荧光强度关系错综复杂,两者不再满足线性关系。为了准确测定橙汁中山梨酸钾的浓度,提出了一种微粒群(PSO)算法优化的误差逆向传播(BP)神经网络的新方法。两组预测浓度的相对误差分别为1.83%和1.53%,预测结果表明该方法具有可行性。在浓度范围为0.1~2.0 g·L-1内,PSO-BP神经网络能够完成橙汁中梨酸钾浓度的准确测定。     

Control of Unknown Chaotic Systems Based on Neural Predictive Control

We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.     

Dynamics of Transiently Chaotic Neural Network and Its Application to Optimization

Through adding a nonlinear self-feedback term in the evolution equations of nerual network,we introduced a transiently chaotic neural network model.In order to utilize the transiently chaotic dynamics mechanism in optimization problem efficiently,we have analyzed the dynamical pocedure of the transiently chaotic neural network model and studied the function of the crucial bifurcation parameter which governs the chaotic behavior of the system.Based on the dynamical analysis of the transiently chaotic neural network model,Chaotic annealing algorithm is also examined and improved.As an example,we applied chaotic annealing method to the traveling salesman problem and obtained good results.