耦合条件下大脑皮层神经振子群的能量函数

探讨了局部脑皮层网络活动中,耦合条件下的大规模神经振子群的能量消耗与神经信号编码之间的内禀关系,得到了神经元集群在阈下和阈上互相耦合时神经元膜电位变化的函数. 这个能量函数能够精确地再现神经电生理学实验中的EPSP,IPSP,动作电位以及动作电流. 最近功能性核磁共振实验证明了神经信号的编码是与能量的消耗紧密地耦合在一起的,因此研究结果表明利用能量原理研究大脑在神经网络层次上是如何进行编码的这一重大科学问题的讨论是十分有益的. 可以预计得到的能量函数将是生物学神经网络动力学稳定性计算的基础.      

生物神经网络系统动力学与功能研究

生物神经系统是由数量极其巨大的神经元相互联结的信息网络系统,在生物体的感觉、认知和运动控制中发挥关键性的作用.首先介绍神经元、大脑和一些生物神经网络的生理结构和理论模型,然后分别介绍其放电活动和网络动态特性的一些重要问题,包括神经元的复杂放电模式、耦合神经元网络系统的同步活动和时空动力学、大脑联合皮层神经微回路的网络结构特征,以及工作记忆和抉择过程的动力学机制等. 最后对今后研究给出一些展望.      

大脑血液动力学现象中的能量编码

神经信息的编码与解码是神经科学中的核心研究内容,同时又极具挑战性.传统的编码理论都具有各自的局限性,很难从脑的全局运行方式上给出有效的理论.而由于能量是一个标量具有可叠加性,因此能量编码理论可以从神经元活动的能量特征出发来研究脑功能的全局神经编码问题,取得了一系列的研究成果.本研究以王-张神经元能量计算模型为基础,构建了一个多层次结构的神经网络,通过计算机数值模拟得到了神经网络的能量消耗和血液中葡萄糖供能的变化情况.计算结果显示,和网络的神经活动达到峰值的时间相比,血液中葡萄糖的供能达到峰值的时间延迟了约5.6s.从定量的角度再现了功能性核磁共振(fMRI)中的血液动力学现象:大脑某个脑区的神经元集群被激活以后经过5~7 s的延迟,脑血流的变化才会大幅增加.模拟结果表明先前发表的由王-张神经元模型所揭示的负能量机制在控制大脑的血液动力学现象中起着核心的作用,预测了刺激条件下大脑的能量代谢与血流之间变化的本质是由神经元在发放动作电位过程中正、负能量之间的非平衡、不匹配性质所决定的.本文的研究结果为今后进一步探究血液动力学现象的生理学机制提供了新的研究方向,在神经网络的建模与计算方面给出了一个新的视角和研究方法.      

Chimera states and synchronization behavior in multilayer memristive neural networks

To study the effect of electromagnetic induction on the spatiotemporal dynamic behavior of neural networks, in this paper, we have mainly studied both the synchronization behavior and the evolution of chimera states (CS) in coupled neural networks. To do this, a multilayer memristive neural network was constructed by selecting the Hindmarsh–Rose neurons as the network nodes, and the effect of electromagnetic induction is introduced by using the cubic flux-controlled memristive model as synapse. For simplicity, the following coupling scheme is adopted: only the coupling connections for the neurons between different layers are considered with memristive synapses, while those neurons in each layer are still bidirectional coupled with the classical electrical synapses. It is found that, by adjusting the coupled strength of electrical synapses and the parameters of memristive synapses, the coexistence behavior of coherent and incoherent states, i.e., the CS, could appear in each layer. It is interesting that the CS are also found in inter-layer memristive synapse network. Furthermore, we have discussed the synchronization behavior in this multilayer memristive neural network, one can find when the whole multilayer network is in a synchronization state, not only the spatiotemporal consistency of the CS in each layer neural networks is observed, but also the memductance of all memristive synapses is completely synchronized. Our results suggest that the electromagnetic induction may play an important role in regulating the dynamic behavior of neural networks, and the introduction of memristive synapse into the biological neural network will provide useful clues for revealing the memory behavior of the neural system in human brain.     

Multiple synchronization transitions due to periodic coupling strength in delayed Newman–Watts networks of chaotic bursting neurons

In this paper, we numerically study the effect of time-periodic coupling strength on the synchronization of firing activity in delayed Newman–Watts networks of chaotic bursting neurons. We first examine how the firing synchronization transitions induced by time delay under fixed coupling strength changes in the presence of time-periodic coupling strength, and then focus on how time-periodic coupling strength induces synchronization transitions in the networks. It is found that time delay can induce more synchronization transitions in the presence of time-periodic coupling strength compared to fixed coupling strength. As the frequency of time-periodic coupling strength is varied, the firing exhibits multiple synchronization transitions between spiking antiphase synchronization and in-phase synchronization of various firing behaviors including bursting, spiking, and both bursting and spiking, depending on the values of time delay. These results show that time-periodic coupling strength can increase the synchronization transitions by time delay and can induce multiple synchronization transitions of various firing behaviors in the neuronal networks. This means that time-periodic coupling strength plays an important role in the information processing and transmission in neural systems.     

阵发型神经元网络的联想记忆与分割

以Hindmarsh-Rose(HR)神经元组成网络,研究这些网络的记忆(或模式)存储与时间分割问题．给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地分割出每一种模式．如果输入的模式是缺损的,系统能够恢复为原型,即网络具有联想记忆功能．模拟也得到据作者所知至今还未报道的一些现象．     

基于智能驾驶电动汽车的多车?桥梁耦合系统动力学特性研究

迫于能源和环保问题的压力, 电动汽车及智能驾驶受到了各国高度重视. 轮毂电机驱动电动汽车车轮振动剧烈, 与桥梁路面动力学相互作用更加突出, 现有研究主要针对传统汽车, 关于电动车轮与公路桥梁接触动力学相互作用及智能驾驶车队的多车?桥梁耦合作用研究尚不多见. 本文以轮毂电机驱动电动汽车为研究对象, 考虑车轮和桥面多点接触关系, 研究了两个智能驾驶汽车过桥时的车桥耦合动力学特性. 分析了电机质量、电机激励、轮胎悬架刚度非线性、车距、车速对系统振动特性的影响, 以及桥面不平顺激励、三重耦合激励对电动汽车平顺性的影响. 研究表明: 车距和车速是影响车?桥系统振动特性的重要因素, 在车?桥耦合动态设计中, 车距和车速的影响应重点关注; 桥面越平坦, 电机激励及桥面二次激励对车辆平顺性和道路友好性影响越加显著, 当汽车行驶在平坦桥面时两种激励对轮毂电机驱动电动汽车的影响不容忽视. 所建模型有望为智能驾驶电动汽车与桥梁的耦合作用研究提供理论参考.      

关于注意和记忆的神经动力学机制

利用随机的相变动力学理论研究了一个具有不同相位的神经振子群模型,并考察神经 振子群对刺激信息的处理及神经编码的动态演化. 通过对动力学模型的数值分析,在二维相 空间上描述了神经元集群内不同振子簇发放动作电位时,数密度随时间演化的图像. 数值分 析的结果表明该模型能够用来描述注意和记忆的神经动力学机制,并且证明了只有高维的神 经动力学模型才能更深刻地描述神经元集群的动力学特性,而以往的编码模型丢失了大量有 用的神经信息.     

等p路径十砂土本构关系的归一化特性及数值建模方法

用神经网络对土体进行建模能反映应力路径相关性、反映土的剪胀剪缩以及反映体应力、剪应力对体应变、剪应变的交互影响,因而成为一种比较理想的建模方式.能否在样本有限的情况下获得精度比较高的本构模型正是主要的研究目的.通过研究中密砂在等p路径下的三轴试验曲线,发现其应力-应变关系曲线在常规应力范围内具有归一化特性.选择合适的归一化指标对砂土三轴试验数据进行归一化,以归一化的试验数据为训练样本进行神经网络训练,得到了比较理想的砂土的神经网络本构模型.本构模型仿真值与试验值符合较好,表明所给出的建模方法是合理的.提出的建模方法可以在所有试验数据的基础上自动实现概率寻优,能有效降低噪声信号的干扰、减小试验数据的分散造成的影响.     

工程结构优化的神经网络方法

本文阐述了神经网络优化计算的基本原理，构造了工程结构优化的神经网络模型。采用模拟退火技术进行模型求解，且巧妙地将退火温度Ｔ的倒数作为Ｌａｇｒａｎｇｅ乘子，以改善增广目标函数的收敛性。实例计算表明，由非线性模拟神经元组成的大规模并行、互连的网络在工程结构的优化设计中是可行且有效的。     

时滞耦合系统非线性动力学的研究进展

随着对自然界客观规律的深入认识,工程系统设计的精细化和复杂性要求也与日剧增.在许多耦合的动态系统设计过程中要考虑由耦合过程的时滞所引发的动力学行为,该时滞来自于与传感系统、作动系统和控制系统耦合的过程.耦合时滞也广泛存在于交通、系统生物学、电子通讯、神经和信息网络等技术中.本文首先从耦合时滞出发,在以时滞为中心的耦合系统复杂动力学机制、时滞镇定耦合系统的实验基础和实现、快慢变时滞耦合系统动力学和时滞神经网络同步和去同步4个方面,对耦合时滞诱发的动力学研究进展进行综述.着重介绍了时滞耦合系统中耦合时滞诱发的高余维分岔奇异性及新的定量分析方法、中立型时滞微分方程的规范型计算、具有耦合时滞的非线性系统中耦合时滞和非线性参数的辨识方法与实验实现、快慢变时滞耦合系统的张弛振荡、耦合时滞诱发的网络系统的同步模式切换等问题的研究进展;然后在应用方面重点介绍了车床磨削加工过程中耦合时滞诱发的颤振及其机理、具有惯性项和耦合时滞的神经网络系统中耦合时滞诱发的高余维分岔和复杂动力学、时滞动力吸振器与隔振装置的设计与实验实现.最后,从耦合时滞系统的一般性理论和工程应用两个方面展望了近期值得关注的一些问题.     

脉动型神经元网络的联想记忆与分割

以广泛讨论的Hodgk in-Hux ley神经元节点组成脉动神经元网络,从神经系统空时模式编码理论研究网络的记忆(或模式)存储与时间分割问题。给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地在时间域分割出每一种模式。如果输入的模式是缺损的,系统能够把它们恢复成完好的原型,即神经网络的联想记忆功能。     

用人工神经网络预测摩擦学系统磨损趋势

人工神经网络具有高度的并行分布式、联想记忆、自组织及自学习能力和极强的非线性映射能力，在许多领域显示了广阔的应用前景．但是，将神经网络用于摩擦学行为预测的研究报道却还鲜见．在对基于神经网络的单变量时间序列预测方法与过程进行分析之后，提出了摩擦学系统磨损趋势神经网络预测模型．采用定量铁谱参数中的总磨损Ｑ作为预测磨损趋势的特征参数，讨论了磨损趋势神经网络预测的单步预测法和多步预测法，并用其对ＣＤ４０齿轮泵的磨损趋势进行了预测，预测值与实测值吻合较好     

STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES＊

The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached:High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.     

Dynamic characteristic analysis for clutch engagement process of series–parallel hybrid electric vehicle

Variable driving conditions can cause an integrated starter generator hybrid powertrain to switch between multiple drive modes. The addition of a permanent magnet synchronous motor (PMSM) gives hybrid powertrains complex electromechanical coupling characteristics. The effects of excitation sources, such as the engine and PMSM, may cause unstable behavior in the drive system, such as speed fluctuations during mode switches due to electromechanical coupling characteristics. Although traditional mode switch strategies and methods have achieved measurable results, they are difficult to improve. To solve this problem, we first considered the combination and separation of the clutch and establish a nonlinear model of mode switches for series–parallel hybrid electric vehicles. Then, we predicted the instability boundary of the drive system during mode switches. Experimental results indicated that the proposed instability boundary has higher accuracy. Numerical results showed that the three-mode switches have different thresholds of instability for the clutch structure gap. The decrease in electromagnetic torque and the increase in load excitation amplitude will improve the critical value of the clutch structure gap. The increase in load excitation frequency causes the critical value of the clutch structure gap to drop first and then rise.

     

基于动态神经元网络的激光陀螺输出误差模型

惯性敏感器误差补偿技术对提高武器装备的性能具有重要的意义,而误差补偿的关键在于误差模型的辨识。由于动态神经元网络是在前馈网络的节点引入前馈和反馈环节,理论上已证明其具有很强的动态逼近能力,可用来描述任意的非线性动态系统。根据惯性敏感器误差的动态特性,本探讨将动态神经元网络引入到激光陀螺误差建模中去,详细介绍了网络结构和对应的动态梯度算法。通过仿真算例说明,动态神经元网络在激光陀螺输出误差建模时具有一定的优点：网络收敛速度快、较好的跟踪性能、稳定性好。     

异常天气条件下潮位过程神经网络补遗预测方法的研究

以异常天气条件下的实测潮位过程为研究对象,根据其为非平稳时间序列的特点,以人工神经网络BP算法作为预测工具,对潮位时间序列中缺失的数据进行补遗建立:差分方法人工神经网络模型;同一海域邻近潮汐测站潮位过程对应预测的去平均值法人工神经网络模型;对增减水现象潮汐过程预测的气象资料数据库人工神经网络模型。以实测资料验证上述方法的可行性,并取得了很好的预测结果。     

基于模糊神经网络的结构非模型主动控制

本文将模糊人工神经网络运用到结构的主动控制中，用模糊联想记忆（ＦＡＭ）神经网络作为控制器，对在地震力下的结构振动进行主动控制，控制过程中无须引入结构的运动模型和精确参数。数值仿真的结果表明，该控制系统具有适应能力强，消振迅速且效果良好的特点。     

Lateral vibrations of soft mounted converter-fed induction motors caused by dynamic air gap torques: a mathematical analysis of orbital movements

This paper shows that dynamic air gap torques in converter-fed induction motors may not only cause torsional vibrations in the drive train, but may also cause lateral vibrations of the motor itself—including bending vibrations of the rotor shaft—, if the motor is mounted on a soft foundation. Based on a simplified analytical model, the mathematical correlation between rotor-stator interaction, oil film characteristics of sleeve bearings, the influence of a soft foundation—coupling angular and lateral displacement of the stator—and excitation from dynamic air gap torques is shown. In addition to the absolute orbits of the rotor, stator, shaft journals and bearing housings, also the relative orbits between rotor and stator and between shaft journals and bearing housings are mathematically described. Further, the paper shows the influence of the oil film coefficients of the sleeve bearings on the elliptical shape of the orbits. Therefore, the paper presents a possibility to optimize the whole system—converter, motor and foundation—concerning lateral vibrations, caused by dynamic air gap torques.     

Resonance induced by mixed couplings in a three-node motif

Nonlinear Dynamics - Inspired by the coexistence of excitatory and inhibitory neurons in real neural networks, we propose a motif of three coupled nodes, one with positive coupling and two with...