Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

Scopolamine causes delirium-like brain network dysfunction and reversible cognitive impairment without neuronal loss

Delirium is a severe acute neuropsychiatric syndrome that commonly occurs in the elderly and is considered an independent risk factor for later dementia.However,given its inherent complexity,few animal models of delirium have been established and the mechanism underlying the onset of delirium remains elusive.Here,we conducted a comparison of three mouse models of delirium induced by clinically relevant risk factors,including anesthesia with surgery(AS),systemic inflammation,and neurotransmission modulation.We found that both bacterial lipopolysaccharide(LPS)and cholinergic receptor antagonist scopolamine(Scop)induction reduced neuronal activities in the delirium-related brain network,with the latter presenting a similar pattern of reduction as found in delirium patients.Consistently,Scop injection resulted in reversible cognitive impairment with hyperactive behavior.No loss of cholinergic neurons was found with treatment,but hippocampal synaptic functions were affected.These findings provide further clues regarding the mechanism underlying delirium onset and demonstrate the successful application of the Scop injection model in mimicking delirium-like phenotypes in mice.

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology,a networked multi-agent control system is a key direction of modern industrial control systems,such as industrial Internet systems.This paper studies the tracking control problem of networked multi-agent systems with communication constraints,where each agent has no information on the dynamics of other agents except their outputs.A networked predictive proportional integral derivative(PPID)tracking scheme is proposed to achieve the desired tracking performance,compensate actively for communication delays,and simplify implementation in a distributed manner.This scheme combines the past,present and predictive information of neighbour agents to form a tracking error signal for each agent,and applies the proportional,integral,and derivative of the agent tracking error signal to control each individual agent.The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems.The effectiveness of the networked PPID tracking scheme is illustrated via an example.

Engrafted newborn neurons could functionally integrate into the host neuronal network

The limited capability to regenerate new neurons following injuries of the central neural system（CNS）still remains a major challenge for basic and clinical neuroscience.Neural stem cells（NSCs）could nearly have the potential to differentiate into all kinds of neural cells in vitro.

基于模糊神经网络的氢液化氦气压力PID控制

为了解决氢液化装置氦气压力调节系统超调量大、响应速度慢、调节时间长、控制参数无法在线整定等问题,针对系统具有非线性和时变性的特点,设计了基于模糊神经网络的PID控制器以及基于双曲正切函数的改进型激活函数。仿真结果表明:相比传统PID控制或模糊PID控制,采用模糊神经网络PID控制的系统动态性能显著改善,使得氢液化装置的氦气压力调节更加稳定可靠。

干涉式闭环光纤陀螺仪的PSO-PID控制优化方法

控制系统的设计会对响应速度快且应用范围较广的数字干涉式闭环光纤陀螺(ICFOG)动态性能产生影响。通过分析ICFOG的工作原理,推导出闭环离散控制系统,并利用粒子群优化算法(Particle Swarm Optimization,PSO)对传统的PID控制器参数进行优化。基于这个优化过程,设计一种新型的PSO-PID复合控制器,以取代传统的PID控制器。通过与其他BP神经网络、模糊控制等方法进行对比凸显该控制方法的优越。通过数字仿真分析显示,跟踪速度相较于BP-PID控制方法提高了1.91倍,相对于PID控制方法提高了3.5倍,相对于F-PID控制方法提高了1.75倍。同时,控制精度相对于BP-PID控制方法提高了46.03%,相对于PID控制方法提高了66.30%,相对于F-PID控制方法提高了45.27%。结果显示,采用PSO-PID控制器能够快速达到控制目标且具有较小的超调量。

基于反向传播神经网络PID的高功率微波炉温度控制

针对现有10 kW高功率工业微波炉,采用继电器作为控制执行器,在使用传统控制方法加热时,温度存在较大超调和明显振荡,系统温度稳定性较低,为解决上述问题将反向传播神经网络PID(BPNNPID)控制引入到该装置微波加热温度控制中,并以自来水为加热对象进行仿真对比与实验验证。首先,利用现有输入输出实验数据,建立工业微波炉温度控制模型;其次,运用MATLAB/SIMULINK搭建高功率工业微波炉温度控制系统并进行仿真对比实验;最后,实验验证BPNNPID控制方法在加热5 kg自来水时工业微波炉的温度控制性能,实验结果表明,较常规PID、模糊PID控制,该方法在微波加热过程中对媒质温度控制超调更小且未发生明显温度振荡,有效改善了高功率工业微波炉工作时的系统温度稳定性,有助于提高产品质量和安全性能。

飞机起落架自适应模糊神经PID控制方法的研究

针对传统PID控制与模糊PID控制的飞机起落架控制系统存在达不到理想控制精度以及控制速度的问题,提出一种基于模糊控制和神经网络的模糊神经PID控制算法。通过对起落架运动特点以及动力学相关的理论分析建立飞机起落架的运动模型,将此智能PID控制方法应用到飞机起落架的姿态控制系统中。利用MATLAB/Simulink软件进行仿真,并基于树莓派装置进行了起落架单腿实验。仿真和实验结果表明:模糊神经网络PID控制系统的响应速度和抗干扰能力相较于传统PID和模糊PID都有了较大的提升,系统稳定性更强。在飞机起落架控制系统中,应用模糊神经PID控制可进一步提升系统的响应速度,降低系统运动的惯性冲击,提高整体机构的稳定性。

四旋翼无人机预设性能自适应PID控制

针对四旋翼飞行器在轨迹跟踪过程中存在建模误差和外界干扰问题,设计了一种双闭环控制系统。内环姿态环采用自适应PID算法,用滑模算法作为自适应机制,结合梯度下降法克服传统PID需要手动调节参数的问题,并用RBF神经网络消除滑模控制过程中产生的抖振现象;外环位置环采用预设性能自适应PID算法,即在自适应PID算法的基础上加上预设性能控制,将误差用预设性能函数进行转换,使系统误差能够始终稳定在预设值,实现位置的快速跟踪;最后用Lyapunov函数证明系统的稳定性。从跟踪的快速性、稳定性和稳态性能方面,由仿真结果对比证明本文所设计的控制算法有很大的优越性,并能对不同形式的外部扰动表现出强抗干扰性。

One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units

Artificial neural networks(ANN) have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit(1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 p J/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies(> 90%) within a large range of dropout probabilities up to40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.

单神经元PID控制仿真与实现研究

经典PID控制参数整定方法繁琐并且容易对设备造成损伤,此外,当系统对象存在非线性、环境变化或者其他因素时,系统的稳定性和响应速度也会受到一定的影响。为了解决这些问题,该文引入神经网络与PID控制方法相结合,控制对象选择双容水箱,对系统进行多次仿真、对比,然后提出神经网络控制仿真参数设置方法,进而将神经网络PID控制算法应用于实际控制系统中,实验证明该创新方案不仅解决了PID控制方法存在的问题,还找到了一个低成本的升级方案,为控制系统提供了重要的技术支持。

基于可变增益PID控制器的车载以太网时钟同步算法研究

在车载以太网中,电机力矩、碰撞检测等控制类或关键突发信息的通信,由于其实时性要求比较高,需要借助时间敏感网络(Time Sensitive Net,TSN)协议,特别是流预留等机制来确保实时性或带宽,而要实现这些协议,网络节点间的高精度时钟同步是基础。通过设计基于根轨迹的PID控制器,对运行在从时钟节点上的时钟同步过程进行控制,并对控制器增益系数进行基于误差变化的自适应处理。实验结果表明,与未采用增益系数环节的控制器相比,采用自适应增益系数调整的控制器在同步过程的稳态误差绝对值均值降低了45%,稳态误差标准差降低了41%;与采用较小增益系数相比,采用自适应增益系数调整的同步过程在稳态过渡过程时间上降低了75%。实验结果证实了该方案可以有效平衡同步过程的稳态过渡过程时间和稳态同步精度。

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Rhythm dynamics of complex neuronal networks with mixed bursting neurons

The spatiotemporal order and rhythm dynamics of a complex neuronal network with mixed bursting neurons are studied in this paper. A quantitative characteristic, the width factor, is introduced to describe the rhythm dynamics of an individual neuron, and the average width factor is used to characterize the rhythm dynamics of a neuronal network. An r parameter is introduced to denote the ratio of the short bursting neurons in the network. Then we investigate the effect of the ratio on the rhythm dynamics of the neuronal network. The critical value of r is derived, and the neurons in the network always remain short bursting when the r ratio is larger than the critical value.

基于PSO改进BP算法的直流电子负载PID控制仿真

针对电子负载控制灵敏度低、稳定性差等问题,提出基于PSO-BP-PID的直流电子负载控制方法。分析电子负载基本结构,构建数学模型,分析电子负载在不同工作模式下的电流变化规律;建立三层BP网络模型,分别描述每层结构的输入与输出内容;为提高BP网络的学习能力,减少控制误差,将PSO算法作为学习算法,确定粒子群规模、惯性权重等重要参数,获得所有粒子适应度值,不断更新个体的位置与速度,当满足收敛条件时,输出最优解,实现控制参数的自适应调整;根据算法特征,设计控制器整体结构,利用该控制器即可实现直流电子负载控制。仿真结果表明,所提方法的控制误差小,响应速度快,且控制过程中能够有效抑制谐波。

薄膜复合粘接工艺的PID控制优化分析

为块状物薄膜复合粘接工艺提供精确可靠的控制方案,根据不同薄膜复合粘接工艺进行分析得到电机控制特征,并与传统的控制方法进行控制效率对比,对茶砖薄膜复合粘接的步骤加以确定并使用PLC实现粘接系统控制,通过PID策略分析和MATLAB仿真后,选择得到块状物粘接控制方案,粘接过程中的各阶段的阶跃响应曲线,下料时间有效缩短为其定重时间的80%以上,并缩短原有下料时间25%-34%。通过使用合适的PID控制方案有效地优化了原有粘接工艺,为实际应用于相关生产过程中提供科学的理论支持。

足式机器人腿部关节改进单神经网络PID控制算法研究

为了满足液压足式机器人在复杂环境中实现精确、快速的腿部关节控制需求,把单神经网络PID能够实时调节参数的优点运用到足式机器人液压机械腿关节的控制中,在单神经网络PID的基础上增加机械腿关节的位置和速度控制算法,形成改进单神经网络PID,实现了对神经元比例参数自调整、PID参数的自整定,能够较好地适应内、外参数的变化,增强了腿部关节的快速性、精确性。在Simulink中进行建模仿真以及在设计的以STM32为中央处理芯片的控制平台上进行实验测试,结果表明:改进单神经网络PID在足式液压机器人的腿部关节控制中具有响应速度快、超调量小、控制精度高、鲁棒性强等优点。

基于粒子群优化BP神经网络PID的供热控制系统仿真研究

供热系统技术属于清洁技术,但其能耗非常大,因此在供热系统中能源的损耗问题就显得尤为重要。与此同时,我国供暖过程多数是用传统PID对供暖系统进行控制,由于传统PID控制响应时间长、超调量高且受外界影响较大,造成能源未充分利用、浪费现象严重。因此针对此问题,提出了在供暖系统中采用一种基于粒子群优化BP神经网络PID的控制策略,不仅可以解决供暖时水温不稳定、水温上升时间长等问题,而且可以更好地解决能源未充分利用问题。本文建立供热系统的数学模型,然后利用Matlab中的Simulink设计并仿真粒子群BP神经网络PID控制器。实验结果表明,改进后的PID控制器抗干扰能力强且具有较好的鲁棒性,对供热控制系统有更好的控制效果。

基于RBF神经网络整定PID的电液比例系统位置控制研究

针对凿岩机械臂的电液比例系统位置控制精度问题,提出了一种基于径向基函数(RBF)神经网络整定PID的电液比例系统位置控制方法。首先,在AMESim中搭建了阀控非对称液压缸的电液比例系统简化模型,设置了各个模块的参数;然后,利用MATLAB/Simulink搭建了系统闭环控制模型,通过不断更新RBF网络模型并修正PID参数,实现了基于RBF神经网络整定PID的电液比例系统位置控制目的;结合AMESim搭建的电液比例系统模型和Simulink下搭建的控制器进行了联合仿真;最后,基于凿岩台车机械臂实验平台,进行了电液比例系统位置控制实验。仿真结果表明:在受到外部干扰的情况下,RBF神经网络整定PID控制系统能够在0.3 s内控制活塞杆重新运行至目标位置,平均响应时间为1.5 s,位置精度误差不超过5 mm。实验结果表明:与常规PID控制方法相比,RBF神经网络整定PID控制活塞杆位置精度误差降低了75%,位置精度误差在工程实际要求的10 mm范围以内,因此,RBF神经网络整定PID算法可以有效提高电液比例系统的位置控制精度,满足凿岩机械臂实际工作中对电液比例系统位置精度的控制要求。