The dynamics of a mobile phone network

The empirical study of network dynamics has been limited by the lack of longitudinal data. Here we introduce a quantitative indicator of link persistence to explore the correlations between the structure of a mobile phone network and the persistence of its links. We show that persistent links tend to be reciprocal and are more common for people with low degree and high clustering. We study the redundancy of the associations between persistence, degree, clustering and reciprocity and show that reciprocity is the strongest predictor of tie persistence. The method presented can be easily adapted to characterize the dynamics of other networks and can be used to identify the links that are most likely to survive in the future.     

Neural network processing for adaptive line enhancement

This paper describes the inverstigation devoted to establish suitable weights in a feed-forward neural network realizing the narrow-band filtering map in the case of adaptive line enhancement(ALE) by the utility of the optimum common learning rate back propagation (OCLR BP) algorithm. It is found that a feed-forward network with 64 linear input and output neurons, and 8 odd sigmoid neurons in the hidden layer, i.e. an (64→8→64) architecture, could establish the specific input-output function in the case of relatively low signal-to-noise radio. Only is an input signal consisting of mixed periodic and broad-band components available to the network system. After learning, both the "fanning-in-connection patterns", each of which consists of weights fanning into a hidden-neuron From all the outputs of input-neurons, and the "fanning-out-connection patterns", each of which consists of weights fanning out from a hidden-neuron to all the inputs of output-neurons, are tuned to the periodic signals. The nonline     

Neural network painting defect classification using Karhunen–Loeve transformation

This paper deals with the problem of painting defect detection on reflecting surface objects. The problem has been approached with an optical inspecting method. A laser beam hits the object surface. The light scattered from the rough surface generates a digital speckle. The speckle is affected by the painting defect. Using the Karhunen–Loeve transformation, the speckle pattern is transformed into a feature vector. This information is used to train the neural-networks in recovering the defect. The reliability and effectiveness of a prototype is validated by experimental results. At the end, the proposed method is compared with another optical inspection method.     

A study of network dynamics

The dynamics of a discrete-time neural network model are investigated. First, a numerical survey of network power spectra is reported for networks of varying size with random weight matrices and initial states. The steepness of the logistic function and a symmetry measure of the weight matrix are taken as control parameters. Summary statistics are presented to give gross measures of the model's temporal activity in parameter space. Second, a detailed study of the dynamics of a particular network is described. Complex dynamical behavior is observed, including Hopf bifurcations, the Ruelle-Takens-Newhouse route to chaos (showing mode-locking at rational winding numbers and the destruction of an invariant torus), and the period-doubling route to chaos.     

Neural network prediction of cycle-to-cycle power variability in a spark-ignited internal combustion engine

Cycle-to-cycle variation (CCV) limits how lean a spark-ignited (SI) internal combustion engine (ICE) can stably operate at, restricts efficiency, and increases emissions through incomplete combustion. Therefore, a way to cleaner, more efficient SI ICEs is to minimize the CCV. Current methods to study CCV include experimental investigations and CFD-based numerical simulations. This study, in contrast, investigates the ability of neural networks to accurately model the indicated mean effective pressure (IMEP) and its coefficient of variation (COV of IMEP). Experimental data from a previous study of spark-ignited propane/air combustion in the TCC-III engine was used to train and evaluate a neural network. An optimized network was generated that utilizes 109 experimental inputs and is operated with 15 neurons in one hidden layer to determine IMEP for 18 engine operating conditions, with 625 individual consecutive engine cycles for each condition. The impact of training set size and the number of input parameters was also investigated. The average deviation for IMEP from the experimental measurements is 0.7–2.2% for the training data set and less than 12% for the entire predicted range of operating conditions. Data sets consisted of tests under rich, lean, and stoichiometric conditions without and with 9% nitrogen dilution. Predicted COV of IMEP strongly correlates with experimental data (R²?=?0.8453). However, a systematic over prediction of COV of IMEP for low COVs was observed while higher COVs were under-predicted by the neural network. The cause for this systematic behavior has not yet been identified but histograms of the predicted IMEP data indicate that this could be related to missing physical parameters that have a significant impact on combustion variability.     

Neural networks: A biased overview

An overview of recent activity in the field of neural networks is presented. The long-range aim of this research is to understand how the brain works. First some of the problems are stated and terminology defined; then an attempt is made to explain why physicists are drawn to the field, and their main potential contribution. In particular, in recent years some interesting models have been introduced by physicists. A small subset of these models is described, with particular emphasis on those that are analytically soluble. Finally a brief review of the history and recent developments of single- and multilayer perceptrons is given, bringing the situation up to date regarding the central immediate problem of the field: search for a learning algorithm that has an associated convergence theorem.     

Feedback between node and network dynamics can produce real-world network properties

Real-world networks are characterized by common features, including among others a scale-free degree distribution, a high clustering coefficient and a short typical distance between nodes. These properties are usually explained by the dynamics of edge and node addition and deletion.     

溶解氧浓度的神经网络控制研究

污水生化处理过程常常受到入水流量水质变化而处于动态过程, 溶解氧浓度作为系统运行过程的一个关键变量, 采用经典的PI控制器难以保证良好的控制效果. 针对污水处理过程的溶解氧浓度控制问题, 提出了基于单神经元自适应PID算法和基于RBF神经网络两种控制器. 在国际基准Benchmark Simulation Model No.1 (BSM1)的仿真平台上进行仿真实验, 与经典PI控制器的运行结果对比, 表明了在所提出的两种控制器作用下, 溶解氧浓度具有更好的跟踪给定值能力, 控制系统具有更好的综合性能指标值.     

Communities and dynamical processes in a complex software network

Complex technological networks represent a growing challenge to support and maintain as their number of elements become higher and their interdependencies more involved. On the other hand, for networks that grow in a decentralized manner, it is possible to observe certain patterns in their overall structure that may be taken into account for a more tractable analysis. An example of such a pattern is the spontaneous formation of communities or modules. An important question regarding the detection of communities is if these are really representative of any internal network feature. In this work, we explore the community structure of a real complex software network, and correlate this modularity information with the internal dynamical processes that the network is designed to support. Our results show that the dependence between community structure and internal dynamical processes is remarkable, supporting the fact that a community division of this complex network is helpful in the assessment of the underlying dynamical structure, and thus is a useful tool to achieve a simpler representation of the complexity of the network.     

Synchronization regimes in a map-based model neural network

The dynamical activity of a neural network model composed of electrically connected map-based neurons is investigated. After detailing the behavior of the isolated neuron for a wide parameter range, collective network states are depicted using the activity, spatial correlation and time phase distribution as measures. A detailed discussion on the stability of global and partial synchronization states is presented.     

Prediction of outdoor sound transmission loss with an artificial neural network

An artificial neural network is developed for rapid prediction of sound transmission loss (TL) during propagation outdoors. The network predicts TL for a nonturbulent atmosphere from inputs involving the source/receiver propagation geometry (height range: 0-5 m, horizontal separation distance: 100-900 m), source frequency (range: 20-200 Hz), ground properties, and atmospheric refractive profile characteristics. A parabolic equation (PE) code generates the training and test data sets for the network. To ensure that a minimal set of input parameters is used in the network training, a nondimensional version of the PE and accompanying boundary, initial, and atmospheric conditions is developed. A total of 10 independent, nondimensional input parameters are found to be necessary for the training. Approximately 27,000 random cases involving these 10 parameters are generated used to train networks with varying numbers of neurons. The root mean square (RMS) error between random test cases solved by the PE and corresponding neural network predictions was 2.42 dB when a sufficient number of neurons (about 44) are included in the hidden layer. Also, only 18% of the cases resulted in RMS errors that were greater than 2 dB.     

Neural network study of hidden-charm pentaquark resonances

Recently,the LHCb experiment announced the observation of hidden-charm pentaquark states P,(4312),P_c(4440),and P_c(4457)near Σ_cD and Σ_cD~* thresholds.In this present work,we studied these pentaquarks in the framework of the nonrelativistic quark model with four types of potential.We solved five-body Schrodinger equation by using the artificial neural network method and made predictions of parities for these states,which are not yet determined by experiment.The mass of another possible pentaquark state near the D~*Σ_c~* with J~P=5/2~-is also calculated.     

Neural networks and bounded rationality

Traditionally the emphasis in neural network research has been on improving their performance as a means of pattern recognition. Here we take an alternative approach and explore the remarkable similarity between the under-performance of neural networks trained to behave optimally in economic situations and observed human performance in the laboratory under similar circumstances. In particular, we show that neural networks are consistent with observed laboratory play in two very important senses. Firstly, they select a rule for behavior which appears very similar to that used by laboratory subjects. Secondly, using this rule they perform optimally only approximately 60% of the time.     

Multistability of clustered states in a globally inhibitory network

We study a network of m identical excitatory cells projecting excitatory synaptic connections onto a single inhibitory interneuron, which is reciprocally coupled to all excitatory cells through inhibitory synapses possessing short-term synaptic depression. We find that such a network with global inhibition possesses multiple stable activity patterns with distinct periods, characterized by the clustering of the excitatory cells into synchronized sub-populations. We prove the existence and stability of n-cluster solutions in a m-cell network. Using methods of geometric singular perturbation theory, we show that any n-cluster solution must satisfy a set of consistency conditions that can be geometrically derived. We then characterize the basin of attraction of each solution. Although frequency dependent depression is not necessary for multistability, we discuss how it plays a key role in determining network behavior. We find a functional relationship between the level of synaptic depression, the number of clusters and the interspike interval between neurons. This relationship is much less pronounced in the absence of depression. Implications for temporal coding and memory storage are discussed.     

神经网络方法构建苯硫酚三维非绝热势能面

本文利用神经网络方法构建了苯硫酚三维的基态(S₀)绝热势能面以及激发态¹ππ^*和¹πσ^*耦合的非绝热势能面. 特别地,¹ππ^*和¹πσ^*非绝热势能面的透热化是通过神经网络拟合实现的,拟合仅基于其绝热势能,但对非绝热势能矩阵的非对角元施加了对称性的正确限制. 神经网络拟合的3个态的势能面的拟合均方根误差都非常小(<4 meV),体现了神经网络方法的高精度. 在神经网络构建的势能面上计算得到了S₀态的低位能级和S₁ 态0⁰ 的寿命,结果均与早前非绝热势能面上的结果吻合,验证了神经网络方法构建的绝热和非绝热势能面的高精度和可靠性.     

X射线光谱与神经网络中单组分型神经群结构研究

研究、比较了神经群结构与常规神经网络算法的预测性能,考察了过拟合与最佳拟合态等的关系。结果表明,在多元体系中,将神经网络单组分预测模型应用于Ｘ射线荧光光谱分析时,在预测准确度、模型稳定性和外推预测能力方面,神经群结构优于常规神经网络模型。     

Object reconstruction in multilayer neural network based profilometry using grating structure comprising two regions with different spatial periods

Sampling of the Fourier transforms of fingerprints is studied with neural networks to detect regions useful for their classification. Ring-wedge detector (RWD) is modified and simulated to sample such regions. The output of the detector is propagated through a three-layer feedforward-backpropagation neural network for checking the classification performance. Modified detector's performance is also compared with that of RWD. It has been found that fingerprints scanned at 500 dpi resolution and cropped to a size of 200×200 contain useful information for their classification in a band of width 20 pixels with inner radius approx. 60 pixels.     

Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles

Dimensionality reduction can be of crucial importance in the application of inversion schemes to atmospheric remote sensing data. In this study the problem of dimensionality reduction in the retrieval of ozone concentration profiles from the radiance measurements provided by the instrument Global Ozone Monitoring Experiment (GOME) on board of ESA satellite ERS-2 is considered. By means of radiative transfer modelling, neural networks and pruning algorithms, a complete procedure has been designed to extract the GOME spectral ranges most crucial for the inversion. The quality of the resulting retrieval algorithm has been evaluated by comparing its performance to that yielded by other schemes and co-located profiles obtained with lidar measurements.     

Artificial Neural Networks for Resonant Frequency Calculation of Rectangular Microstrip Antennas with Thin and Thick Substrates

Neural models based on multilayered perceptrons for computing the resonant frequency of rectangular microstrip antennas with thin and thick substrates are presented. Eleven learning algorithms, Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, conjugate gradient of Powell-Beale, bayesian regularization, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, resilient backpropagation, conjugate of Polak-Ribiére, backpropagation with adaptive learning rate, one-step secant, and backpropagation with momentum, are used to train the multilayered perceptrons. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by Levenberg-Marquardt algorithm.     

State generators and complex neural memories

The mechanism of self-indexing for feedback neural networks that generates memories from short subsequences is generalized so that a single bit together with an appropriate update order suffices for each memory. This mechanism explains how stimulating an appropriate neuron can recall a memory. Although information is distributed in this model, yet our self-indexing mechanism makes it appear localized. Also a new complex valued neuron model is presented to generalize McCulloch-Pitts neurons.