浮栅技术及其应用

介绍了浮栅技术的基本原理及应用情况。井对2种应用了浮栅技术的典型器件-浮栅MOS晶体管和神MOS晶体管做了详细介绍，分析了他们的基本结构和工作原理，以及建立浮栅MOS晶体管的等效模型，并说明了他们的应用情况及存在的不足。     

基于复数权值神经元的数字逻辑最稳健设计

复数权值神经元由于引入了多阈值逻辑而具有更强的性能,文中根据其数学模型,结合二进神经元稳健性的概念,提出了基于该神经元的稳健性定义.并结合定义,实现了基于单个神经元的基本数字逻辑以及异或运算的最稳健设计方案,从而证明了该稳健神经元实现任意数字逻辑的有效性和可行性,说明了其强大的处理能力.     

Enantiomeric Composition of Monoterpene Hydrocarbons in Some Conifers and Receptor Neuron Discrimination of α-Pinene and Limonene Enantiomers in the Pine Weevil, Hylobius abietis

The enantiomeric composition of seven monoterpene hydrocarbons in headspace volatiles of spruce sawdust and seedlings (Picea abies), pine seedlings (Pinus sylvestris), and branches of juniper (Juniperus communis) was determined by gas chromatographic separation on a -Cyclodextrin column. For the six monoterpenes, -pinene, camphene, -pinene, sabinene, limonene, and -phellandrene, both enantiomers were present, whereas for 3-carene only the (+)-configuration was found. The amount of each enantiomer varied considerably both in relation to total amount of all of them, and for the six pairs also in relation to the opposite enantiomer. One olfactory receptor neuron in the pine weevil (Hylobius abietis) showed a strong response to -pinene when stimulated with all four headspace materials via a GC equipped with a DB-WAX column. The same neuron was subsequently tested with repeated stimulations via the GC effluent containing the (+)- or (–)-enantiomer. A marked better response to (+)- than to (–)--pinene was elicited. Another olfactory receptor neuron that responded strongly to limonene when stimulated with the spruce volatiles was tested for enantiomers of limonene. This neuron responded more strongly to (–)- than to (+)-limonene, when stimulated alternately with each of the limonene enantiomers. Discrimination between enantiomers by plant olfactory receptor neurons suggests that the enantiomeric ratios of volatile compounds may be important in host location by the pine weevil.     

用于队列管理的模糊增益神经元自适应控制器

针对具有参数时变及非线性特性的网络拥塞控制系统,提出了一种模糊增益神经元主动队列管理算法(FNAQM). 采用路由器队列长度及数据流速作为拥塞度量,在检测当前拥塞信息的同时,预测未来拥塞的状况. 结合神经元控制和模糊控制的优点,利用单神经元计算数据包标记概率,采用有监督的Hebb学习规则在线调整加权系数. 设计的模糊控制器可动态调整神经元增益,能获得更好的控制性能. FNAQM具有结构简单、易于实现、自适应能力强等优点. 仿真实验结果表明,FNAQM能快速将队列调整至目标值,并维持较小的队列抖动,对动态数据流和非响应流具有良好的鲁棒性.     

感知神经网络的信息传输及电路实现研究

为提高神经元网络信号的传输效率,改善人们对外界刺激的感知能力,本文采用平均互信息量的评价方法,研究了费茨休夫-南云感知神经元模型中信息传输的随机共振现象,探讨了并联单层神经元网络和双层神经元网络的信息处理功能,并用PSpice电路对神经元网络的信息传输进行了实验验证。数值模拟与电路仿真结果表明,FHN神经元并联模型和双层神经元网络具有随机共振现象,在一定的噪声强度范围内,双层神经元网络对输入信号的响应好于单层神经元网络,噪声能够提高神经元网络的输入输出互信息量,平均互信息量的最大值对应最优的传输性能,证明噪声对神经网络信息传输具有增强作用。该研究对感知神经元群体的信息传输具有重要的启示意义。     

基于PCNN的自动图像分割

脉冲耦合神经网络(PCNN)模型在数字图像分割中得到了广泛的应用,但对网络参数的确定以及最优结果的选则一直是一个难题,主要是以人工经验为主,虽然提出了一些参数自动设置的算法,但都缺乏对模型本身数学理论的研究和分析.通过对PCNN网络神经元点火特性的分析,提出了一种基于图像最大灰度值最小时刻点火的参数自适应设定算法,并针对该算法构造了一种新的用于最优结果选择的判定准则.将其用于lena等图像分割中,取得了与主观评价相一致的结果,而且表现出更快的速度和较好的鲁棒性.     

Probabilistic Bit Stream Neural Chip: Theory

A design for a neural network chip is proposed using probabilistic bit streams to represent real values. This paper analyzes the performance of the proposed neurons in this design and demonstrates that very simple operations can be used to obtain the desired functionality. It is also shown that a suitable ‘activation function’ for neurons of this type can be obtained using the interaction of two probability distributions. Finally, the paper introduces a variant of the back-propagation learning algorithm which involves computing the derivatives of the output with respect to individual weights in a network of such units.     

Stability Analysis for Delayed Neural Networks with Discontinuous Neuron Activations

This paper considers a class of delayed neural networks with discontinuous neuron activations. Based on the theory of differential equations with discontinuous right‐hand sides, some novel sufficient conditions are derived that ensure the existence and global exponential stability of the equilibrium point. Moreover, by adopting the concept of convergence in measure, convergence behavior for the output is discussed. The obtained results are independent of the delay parameter and can be thought of as a generalization of previous results established for delayed neural networks with Lipschtz continuous neuron activations to the discontinuous case. Finally, we give a numerical example to illustrate the effectiveness and novelty of our results by comparing our results with those in the early literature.     

基于C8051F340的温控仪表设计

针对一类温控对象的特点,采用改进型单神经元PID算法,设计一种以C8051F340为核心的温度控制仪表。得到了在仿真环境下的的测试数据和图形,经实验证实该温控仪表对加热炉等温控对象具有很好的控制效果。     

一种量子神经网络模型学习算法及应用

提出一种量子神经网络模型及学习算法. 首先基于生物神经元信息处理机制和量子计算原理构造出一种量子神经元, 该神经元由加权、聚合、活化、激励四部分组成. 然后由量子神经元构造出三层量子神经网络模型, 其输入和输出为实值向量, 权值和活性值为量子比特. 基于梯度下降法构造了该模型的超线性收敛学习算法. 通过模式识别和函数逼近两种仿真结果表明该模型及算法是有效的.