A preferential attachment model with Poisson growth for scale-free networks

We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.     

Analysis on global exponential robust stability of reaction-diffusion neural networks with S-type distributed delays

To avoid the unstable phenomena caused by time delays and perturbations, we investigate the sufficient conditions to ensure the global exponential robust stability with a convergence rate for the reaction-diffusion neural networks with S-type distributed delays. Because S-type distributed delays lead to some difficulty, we also introduce a new generalized Halanay inequality and a novel method-system-approximation method into the qualitative research of neural networks. Moreover, the sufficient criteria provided here, which are rather accessible and feasible, have wider adaptive range.     

An algorithmic study of the Maximum Flow problem: A comparative statistical analysis

In this paper, we show the results of an experimental study about the most important algorithms proposed to solve the Maximum Flow problem. The appropriate statistical analysis not only allows us to justify comparisons between the different procedures but also to obtain classifications of their practical efficiency. Furthermore, an empirical experiment allows us to identify the influence of several parameters that are not included in a theoretical study.     

Redundant paths and reliability bounds in gamma networks

Multistage Interconnection Networks (MINs) are network systems providing fast and efficient communications at a reasonable cost. A gamma network is a specific class of MINs, which provides redundant paths in the system. In a gamma network, information from source nodes is transmitted through a specific set of routes to destination nodes. Reliability of an MIN is used as a measure of system’s ability to transform information from input to output devices. Due to the complexity of network configuration and availability of redundant paths, reliability bounds to estimate the exact reliability of a gamma network is proposed. A numerical example of an 8 × 8 gamma network is presented to demonstrate the accuracy of the reliability bounds. When the lower bound reliability provides sufficient assurance that the system will be operational at some specified time and closely approximates the exact reliability, then no further effort for obtaining the exact reliability expression is necessary.     

Stability analysis for neural networks with inverse Lipschitzian neuron activations and impulses

In this paper, a new concept called α-inverse Lipschitz function is introduced. Based on the topological degree theory and Lyapunov functional method, we investigate global convergence for a novel class of neural networks with impulses where the neuron activations belong to the class of α-inverse Lipschitz functions. Some sufficient conditions are derived which ensure the existence, and global exponential stability of the equilibrium point of neural networks. Furthermore, we give two results which are used to check the stability of uncertain neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of results obtained in this paper.     

A new method for characterizing patterns of neural spike trains and its application

A method for characterizing and identifying firing patterns of neural spike trains is presented. Based on the time evolution of a neural spike train, the counting process is constructed as a time-dependent stair-like function. Three characteristic variables defined at sequential moments, including two formal derivatives and the integration of the counting process, are introduced to reflect the temporal patterns of a spike train. The reconstruction of a spike train with these variables verify the validity of this method. And a model of cold receptor is used as an example to investigate the temporal patterns under different temperature conditions. The most important contribution of our method is that it not only can reflect the features of spike train patterns clearly by using their geometrical properties, but also it can reflect the trait of time, especially the change of bursting of spike train. So it is a useful complementarity to conventional method of averaging.     

基于TDLAS技术的CH4气体检测与温度补偿方法

CH₄气体的精准检测对防止矿井瓦斯爆炸,确保安全生产至关重要。目前基于可调谐半导体激光吸收光谱技术（TDLAS）存在因温度变化导致气体浓度测量误差较大。探究了基于TDLAS的CH₄气体检测系统与温度补偿方法,分析温度对CH₄气体吸收谱线的影响,通过算法补偿模型消除环境温度对CH₄气体检测的影响。依据TDLAS技术原理及相关理论,对系统发射单元、吸收池、信号接收单元、数据处理单元进行设计,搭建了基于TDLAS技术的CH₄气体浓度检测系统,实验检测了不同环境温度（10~50 ℃）时0.04%CH₄气体浓度,分析温度变化对CH₄气体在波长为1.653 μm处吸收谱线强度和半宽度的影响。为消除温度对CH₄气体检测的影响并提高补偿效果,采用粒子群优化算法（PSO）优化BP神经网络（BPNN）的最佳权值和阈值,建立CH₄气体的PSO-BP温度补偿模型,克服了BP神经网络收敛速度慢、易陷入局部最优的缺点。结果表明：（1）基于TDLAS的CH₄气体检测浓度随环境温度升高而下降,整个实验温度内相对误差范围为4.25%~12.13%,不同环境温度下CH₄气体检测浓度与温度之间的关系可用一元三次多项式表示;（2）CH₄气体的吸收强度和半宽度随着温度的升高而下降,与温度变化之间的关系为单调递减函数,温度对CH₄气体吸收谱线强度的相对变化率大于吸收谱线半宽度的相对变化率,CH₄气体吸收谱线的强度更容易受温度变化的影响;（3）BP神经网络和PSO-BP模型测试样本的绝对平均误差（MAE）分别为12.88%和1.81%,平均绝对百分比误差（MAPE）分别为2.3%和0.3%,均方根误差（RMSE）分别为15.96%和2.69%,相关系数R2分别为0.980 6和0.999 6。通过建立PSO-BP温度补偿模型,补偿效果大部分分布在±1.0%的误差范围内,MAE,MAPE,RMSE和R2等评价指标均大幅度提升,对提高TDLAS技术在矿井CH₄的精准检测具有一定的参考意义。     

An adaptive critic neural network for motion control of a wheeled mobile robot

In this paper, we propose a new application of the adaptive critic methodology for the feedback control of wheeled mobile robots, based on a critic signal provided by a neural network (NN). The adaptive critic architecture uses a high-level supervisory NN adaptive critic element (ACE), to generate the reinforcement signal to optimise the associative search element (ASE), which is applied to approximate the non-linear functions of the mobile robot. The proposed tracking controller is derived from Lyapunov stability theory and can guarantee tracking performance and stability. A series of computer simulations have been used to emulate the performance of the proposed solution for a wheeled mobile robot.     

Sulfonated polyaniline network grafted multi-wall carbon nanotubes for enzyme immobilization, direct electrochemistry and biosensing of glucose

A new nanomaterial was prepared by grafting a layer of sulfonated polyaniline network (SPAN-NW) on to the surface of multi-walled carbon nanotube (MWNT) and effectively utilized for immobilization of an enzyme and for the fabrication of a biosensor. SPAN-NW was formed on the surface of MWNT by polymerizing a mixture of diphenyl amine 4-sulfonic acid (DPASA), 4-vinyl aniline (VA) and 2-acrylamido-2-methyl-1-propane sulfonic acid (APASA) in the presence of amine functionalized MWNT (MWNT-NH₂). The MWNT-g-SPAN-NW was immobilized with glucose oxidase (GOx) to fabricate the SPAN-NW/GOx biosensor. MWNT-g-SPAN-NW/GOx electrode showed direct electron transfer (DET) for GOx with a fast heterogeneous electron transfer rate constant (k_s) of 4.11 s^− 1. The amperometric current response of MWNT-g-SPAN-NW/GOx biosensor shows linearity up to 9 mM of glucose, with a correlation coefficient of 0.99 and a detection limit of 0.11 μM (S/N = 3). At a low applied potential of − 0.1 V, MWNT-g-SPAN-NW/GOx electrode possesses high sensitivity (4.34 μA mM^− 1) and reproducibility towards glucose.