兴奋性化学突触耦合的神经元的同步

基于动力系统的稳定性理论、数值计算分岔图和线性化系统的最大Lyapunov指数,研究了经兴奋性化学耦合的快峰神经元的同步动力学.研究表明,随着一些关键参数的改变,耦合神经元能呈现丰富的同步行为,如各种周期的同步和混沌的同步.研究结果对理解神经元系统的同步运动具有指导意义.     

时滞影响下的环式耦合混沌神经元同步

由于突触连接,神经元之间的信息传递普遍存在着时滞效应．本文讨论时滞对四个环式耦合混沌HR（Hindmarsh—Rose）神经元的同步动力学行为影响．通过计算同步差,发现适当的时滞会诱发或增强混沌神经元间的完全同步,即时滞的作用使耦合系统在较小的耦合强度下存在稳定的同步状态．进一步还发现时滞会诱发神经元间出现相位同步,增大在相同步窗口．此外,在完全同步出现可以观察到由时滞诱发的一些复杂现象,包括近似同步与相位同步之间的转迁等．     

Rough Neural Computing in Signal Analysis

This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.     

基于现场总线的智能楼宇温度测控网络设计

在传统的楼宇自动化系统中,由于采用封闭式通信协议而极易形成“自动化孤岛”,局部操作网(LonW orks)现场总线因通信协议的开放性和其特点而能很好地解决上述问题。结合LonW orks现场总线技术和单线数字温度传感器DS18B20,针对于一综合楼,利用神经元芯片CY7C53120和单片机AT89S52构成了基于主机的Neuron节点,设计了多点温度测控系统。测试结果表明:房间温度能被很好地控制在22~26℃,满足设计要求。     

自主式微小型移动机器人的自动避障行为研究

针对多微小型移动机器人工作环境的模型未知或不确定,以及该机器人本身 的某些限制,采用基于行为的研究方法,实现了自行设计的自主式微小型移动机器人在未知 、动态环境中的自动避障,设计了该机器人的障碍物回避行为,采用了电机神经元网络选择 机器人的自动避障动作,并用增强式学习的动作评判结果在线修改网络的权值,结合机器人 的漫步行为,采用机器人的安全漫步任务验证了该方法的有效性．     

神经网络自适应噪声对消的研究

基于神经网络中自适应线性单元(ADALINE),结合传统自适应噪声对消原理,提出了一种基于ADALINE的改进型自适应噪声对消器。经仿真和实例证明,具有很强的噪声滤除能力和鲁棒性。     

锻造操作机大车行走机构的单神经元自适应PID控制

为了解决锻造操作机在运送锻件过程中因惯量较大原因引起的响应滞后问题,通过建立锻造操作机大车行走机构动力学模型,提出了单神经元自适应PID控制算法.对锻造操作机大车行走机构单神经元自适应PID控制模型进行仿真.仿真结果表明:采用单神经元自适应PID控制时,超调量低于3s,小于传统PID临界超调量;大车在3s之后达到匀速运行状态,大车行走机构响应加速度达1 000mm·s-2,速度调整误差为±5mm·s-1,大车行走重复定位误差小于±10mm.     

Contributions of distinct prefrontal neuron classes in reward processing

The prefrontal cortex(PFC)is thought to be involved in higher order cognitive functions,such as in working memory,abstract categorization,and reward processing.It has been reported that two distinct neuron classes(putative pyramidal cells and interneurons)in the PFC played different functional roles in neural circuits involved in forming working memory and abstract categories.However,it remains elusive how the two types of neurons process reward information in the PFC.To investigate this issue,the activity of single neurons was extracellularly recorded in the PFC of the monkey performing a reward predicting task.PFC neurons were classified into putative pyramidal cells and interneurons,respectively,based on the waveforms of action potentials.Both the two types of neurons encoded reward information and discriminated two reward conditions(the preferred reward condition vs.the nonpreferred reward condition).However,the putative pyramidal neurons had better and more reliable discriminability than the putative interneurons.Also,the pyramidal cells represented reward information in the preferred reward condition,but not in the nonpreferred reward condition by raising their firing rates relative to the baseline rates.In contrast,the interneurons encoded reward information in the nonpreferred reward condition,but not in the preferred reward condition by inhibiting their discharge rates relative to the baseline rates.These results suggested that the putative pyramidal cells and interneurons had complementary functions in reward processing.These findings may help to clarify individual functions of each type of neurons in PFC neuronal circuits involved in reward processing.     

基于单神经元的永磁同步电机解耦控制

针对永磁同步电机(PMSM)磁场定向控制为代表的传统解耦策略难以实现高性能控制的问题,本文利用神经网络不依赖对象模型的特点以及出色的学习能力,提出了一种基于单神经元的永磁同步电机解耦控制策略.在传统磁场定向控制模型的基础上,构建了基于单神经元的永磁同步电机解耦控制系统,进行了仿真,并搭建以数字信号处理器为核心的电机控制实验平台上进行实验论证.结果表明,基于单神经元解耦的永磁同步电机控制系统具有快速响应能力,并且几乎达到无静差、无超调,实现了PMSM的高性能控制.