基于LonWorks技术的变电站综合自动系统的通信技术

本文介绍了LonWorks技术的系统结构和技术特点，论述了变电站综合自动化系统现场总线应用LonWorks技术的设计方法。通过对网络可靠通信问题进行研究，得出了在网络节点负载较重的情况下，采用优先级对等和轮询相结合的通信方式，可较好地降低网络通信出错率，提高对紧急事件的响应速度。     

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter (Pressure) Closed-loop Process Control Systems

1IntroductionTheworkcontroloffluidmachinessuchasfans,waterPUInpsandair-Pressersetc.frequentlyemploypressureclosed-loopprocesscontrolSystems[11.IndustrialproducingandPracticingshowthatthesystemshaveaseriousPressurefluctUationPhenomenonduringworkingPro...     

NEURONC语言与事件驱动编程

介绍了ＬｏｎＷｏｒｋｓ网络系统中节点的主要开发语言ＮＥＵＲＯＮＣ的语言,着重阐明了该语言对事件驱动编程的支持,在说明事件调度策略的基础上,提出一种“链式激发”的事件驱动编程方法 。     

Symbolic dynamics of a chaotic neuron model

Chaotic neurons change their internal state according to a bimodal map, and when they communicate with other neurons their internal state is transformed into one of two separate outputs, firing or resting. We address and investigate the topological entropy of the two-valued output of the chaotic neuron from two different viewpoints: the dependence upon the parameters of the neurons, and the relationship to their threshold. From the former viewpoint, we clarify the mechanism that changes the shift space corresponding to the time series of the neuronal output. From the latter viewpoint, we examine the effect of small fluctuations on the threshold of the chaotic neuron. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

MLM形态神经网络在彩色图像恢复中的应用

目前彩色图像的恢复方法多集中在加强色彩相似性的研究上。提出了一种基于形态神经网络MLM的图像恢复法,利用非线性的形态神经元算子对噪声图像进行处理,以获得高质量的清晰图像。仿真结果表明,采用本法恢复的图像在连续性及细节的保护上优于其人方法。     

M‐matrices and global convergence of discontinuous neural networks

The paper considers a general class of neural networks possessing discontinuous neuron activations and neuron interconnection matrices belonging to the class of M‐matrices or H‐matrices. A number of results are established on global exponential convergence of the state and output solutions towards a unique equilibrium point. Moreover, by exploiting the presence of sliding modes, conditions are given under which convergence in finite time is guaranteed. In all cases, the exponential convergence rate, or the finite convergence time, can be quantitatively estimated on the basis of the parameters defining the neural network. As a by‐product, it is proved that the considered neural networks, although they are described by a system of differential equations with discontinuous right‐hand side, enjoy the property of uniqueness of the solution starting at a given initial condition. The results are proved by a generalized Lyapunov‐like approach and by using tools from the theory of differential equations with discontinuous right‐hand side. At the core of the approach is a basic lemma, which holds under the assumption of M‐matrices or H‐matrices, and enables to study the limiting behaviour of a suitably defined distance between any pair of solutions to the neural network. Copyright © 2006 John Wiley & Sons, Ltd.     

On the power of elementary features in spiking neural P systems

Since their first publication in 2006, spiking neural (SN) P systems have already attracted the attention of a lot of researchers. This might be owing to the fact that this abstract computing device follows basic principles known from spiking neural nets, but its implementation is discrete, using membrane computing background. Among the elementary properties which confer SN P systems their computational power one can count the unbounded fan-in (indegree) and fan-out (outdegree) of each “neuron”, synchronicity of the whole system, the possibility of delaying and/or removing spikes in neurons, the capability of evaluating arbitrary regular expressions in neurons in constant time and some others. In this paper we focus on the power of these elementary features. Particularly, we study the power of the model when some of these features are disabled. Rather surprisingly, even very restricted SN P systems keep their universal computational power. Certain important questions regarding this topic still remain open.     

含时滞和Ih流的神经元的同步放电行为

本文研究了时滞和超极化激活的阳离子流Ih对抑制耦合的水蛭神经元的同步放电行为的调控.通过数值仿真揭示了时滞、耦合强度和Ih流都能诱发丰富的同步转迁行为,如从同步的周期-6簇放电到同步的周期-1簇放电.借助ISI分岔和快慢变量分离方法获得了Ih流诱导同步转迁行为的动力学原因.研究结果表明,时滞和Ih流都是影响水蛭神经元同步行为的重要因素.     

A Generalized Algorithm of Training a Formal Neuron

Activation functions of neurons are represented in the form of power series. An algorithm that controls the slope of an activation function is proposed. The results of numerical experiments are given.     

Modeling and simulation of silicon neuron-to-ISFET junction

Modeling and simulation of the silicon neuron-to-ISFET junction is presented. The neuronal electrical activity, extracellularly recorded by the ISFET, was simulated as a function of the neuro-electronic junction parameters such as the seal resistance, double-layer capacitance, and general adhesion conditions. This goal was achieved with a configuration consisting of “silicon neurons” i.e., assemblies of CMOS circuits that mimic the generation of the equivalents of the ionic currents and of the action potentials of real (biological) neurons; “silicon synapses” whose performances simulate those of their biological counterpart; depletion-mode MOSFET-based ISFETs that simulate the signal recording devices; passive component-based circuits that model the neuro-electronic junction. The models of the neuron, synapse, coupling interface, and ISFET were implemented in HSPICE and used to simulate the behavior of the junction between stimulated neurons (described by the compartmental model) and ISFETs.