Standard representation and unified stability analysis for dynamic artificial neural network models.

Science.gov (United States)

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Stability prediction of berm breakwater using neural network

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; Rao, S.; Manjunath, Y.R.

In the present study, an artificial neural network method has been applied to predict the stability of berm breakwaters. Four neural network models are constructed based on the parameters which influence the stability of breakwater. Training...

Stability of Boolean multilevel networks.

Science.gov (United States)

Cozzo, Emanuele; Arenas, Alex; Moreno, Yamir

2012-09-01

The study of the interplay between the structure and dynamics of complex multilevel systems is a pressing challenge nowadays. In this paper, we use a semiannealed approximation to study the stability properties of random Boolean networks in multiplex (multilayered) graphs. Our main finding is that the multilevel structure provides a mechanism for the stabilization of the dynamics of the whole system even when individual layers work on the chaotic regime, therefore identifying new ways of feedback between the structure and the dynamics of these systems. Our results point out the need for a conceptual transition from the physics of single-layered networks to the physics of multiplex networks. Finally, the fact that the coupling modifies the phase diagram and the critical conditions of the isolated layers suggests that interdependency can be used as a control mechanism.

Financial stability from a network perspective

NARCIS (Netherlands)

Leon Rincon, C.E.

2015-01-01

This thesis consists of six chapters related to applications of network analysis’ methods for financial stability. The first chapter introduces the network perspective as a new mapping technique for studying and understanding financial markets’architecture. The second chapter breaks down the

The Stability of Multi-modal Traffic Network

International Nuclear Information System (INIS)

Han Linghui; Sun Huijun; Zhu Chengjuan; Jia Bin; Wu Jianjun

2013-01-01

There is an explicit and implicit assumption in multimodal traffic equilibrium models, that is, if the equilibrium exists, then it will also occur. The assumption is very idealized; in fact, it may be shown that the quite contrary could happen, because in multimodal traffic network, especially in mixed traffic conditions the interaction among traffic modes is asymmetric and the asymmetric interaction may result in the instability of traffic system. In this paper, to study the stability of multimodal traffic system, we respectively present the travel cost function in mixed traffic conditions and in traffic network with dedicated bus lanes. Based on a day-to-day dynamical model, we study the evolution of daily route choice of travelers in multimodal traffic network using 10000 random initial values for different cases. From the results of simulation, it can be concluded that the asymmetric interaction between the cars and buses in mixed traffic conditions can lead the traffic system to instability when traffic demand is larger. We also study the effect of travelers' perception error on the stability of multimodal traffic network. Although the larger perception error can alleviate the effect of interaction between cars and buses and improve the stability of traffic system in mixed traffic conditions, the traffic system also become instable when the traffic demand is larger than a number. For all cases simulated in this study, with the same parameters, traffic system with dedicated bus lane has better stability for traffic demand than that in mixed traffic conditions. We also find that the network with dedicated bus lane has higher portion of travelers by bus than it of mixed traffic network. So it can be concluded that building dedicated bus lane can improve the stability of traffic system and attract more travelers to choose bus reducing the traffic congestion. (general)

UNMANNED AIR VEHICLE STABILIZATION BASED ON NEURAL NETWORK REGULATOR

Directory of Open Access Journals (Sweden)

S. S. Andropov

2016-09-01

Full Text Available A problem of stabilizing for the multirotor unmanned aerial vehicle in an environment with external disturbances is researched. A classic proportional-integral-derivative controller is analyzed, its flaws are outlined: inability to respond to changing of external conditions and the need for manual adjustment of coefficients. The paper presents an adaptive adjustment method for coefficients of the proportional-integral-derivative controller based on neural networks. A neural network structure, its input and output data are described. Neural networks with three layers are used to create an adaptive stabilization system for the multirotor unmanned aerial vehicle. Training of the networks is done with the back propagation method. Each neural network produces regulator coefficients for each angle of stabilization as its output. A method for network training is explained. Several graphs of transition process on different stages of learning, including processes with external disturbances, are presented. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described adjustment method for coefficients can be used in remote control of unmanned aerial vehicles, operating in the changing environment.

Stability of Drinking Water Distribution Network

DEFF Research Database (Denmark)

Leth, Tobias; Kallesøe, Carsten Skovmose; Sloth, Christoffer

2016-01-01

We strive to prove stability of a hydraulic network, where the pressure at the end user is controlled with PI control. The non-polynomial model is represented by numerous polynomial systems defined on sub-sets of R^n. The sub-sets are defined by compact basic semi-algebraic sets. The stability...

Information spread in networks: Games, optimal control, and stabilization

Science.gov (United States)

Khanafer, Ali

on the network. To this end, we propose a distributed version of the classical logic-based supervisory control scheme. Given a network of agents whose dynamics contain unknown parameters, the distributed supervisory control scheme is used to assist the agents to converge to a certain set-point without requiring them to have explicit knowledge of that set-point. Unlike the classical supervisory control scheme where a centralized supervisor makes switching decisions among the candidate controllers, in our scheme, each agent is equipped with a local supervisor that switches among the available controllers. The switching decisions made at a certain agent depend only on the information from its neighboring agents. We provide sufficient conditions for stabilization and apply our framework to the distributed averaging problem in the presence of large modeling uncertainty. For infected networks, we study the stability properties of a susceptible-infected-susceptible (SIS) diffusion model, so-called the n-intertwined Markov model, over arbitrary network topologies. Similar to the majority of infection spread dynamics, this model exhibits a threshold phenomenon. When the curing rates in the network are high, the all-healthy state is the unique equilibrium over the network. Otherwise, an endemic equilibrium state emerges, where some infection remains within the network. Using notions from positive systems theory, we provide conditions for the global asymptotic stability of the equilibrium points in both cases over strongly and weakly connected directed networks based on the value of the basic reproduction number, a fundamental quantity in the study of epidemics. Furthermore, we demonstrate that the n-intertwined Markov model can be viewed as a best-response dynamical system of a concave game among the nodes. This characterization allows us to cast new infection spread dynamics; additionally, we provide a sufficient condition, for the global convergence to the all-healthy state

Use of additive material to stabilize the soil swelling

Science.gov (United States)

Parsaee, B.; Estabragh, A. R.; Bordbar, A. T.; Eskandari, G. H.

2009-04-01

Change volume increasing of soil, because of increase in its humidity content causes appearing of swelling phenomenon in the soil. This phenomenon has created a lot of damages in the building which is constructed on this kind of soils. Usage the additive materials which stabilize the swelling, has been the subject of many researches. In this research the Potential expansibility of the expansive soils, which were stabilized by additive materials such as Lime, cement and coal ash, was investigated. To get this purpose, by preparing soil samples mixed with upper additive material, changes of potential swelling of stabilized soils were compared. The results revealed that usage of these stabilizing materials caused the decrease in destructive effects due to swelling of soils to some extent. Keywords: swelling, soil stabilizing, additive material, coal ash

Game Analysis of Determinants of Stability of Semiconductor Modular Production Networks

Directory of Open Access Journals (Sweden)

Wei He

2014-07-01

Full Text Available In today’s rapidly changing environment, semiconductor manufacturers compete more in the area of modular production networks. However, the instability of semiconductor modular production networks can to a large extent lead to the failure of these networks. The aim of this paper is to discuss the significance and explore the maintenance of the stability of these semiconductor modular production networks. Firstly, this paper qualitatively and quantitatively defines the stability of semiconductor modular production networks. Based on this, by establishing game models, this paper analyzes the influence mechanism of the main factors: external market fluctuation, the internal benefit allocation mechanism, and opportunism, which can jeopardize the stability of these networks. We find that: the greater the benefits a member enterprise derives from the common benefits, the more likely it is the member enterprise will not exit the modular production network; the adaptive ability of the networks to the external environment is closely related to the stability of the modular production networks; the supervision and punishment in networks can be substituted for each other and the level of supervision, punishment and trust can exert great influence on the stability of semiconductor modular production networks. Lastly, we propose some specific suggestions.

Static Voltage Stability Analysis by Using SVM and Neural Network

Directory of Open Access Journals (Sweden)

Mehdi Hajian

2013-01-01

Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

Asymptotic stability of a genetic network under impulsive control

International Nuclear Information System (INIS)

Li Fangfei; Sun Jitao

2010-01-01

The study of the stability of genetic network is an important motif for the understanding of the living organism at both molecular and cellular levels. In this Letter, we provide a theoretical method for analyzing the asymptotic stability of a genetic network under impulsive control. And the sufficient conditions of its asymptotic stability under impulsive control are obtained. Finally, an example is given to illustrate the effectiveness of the obtained method.

Global stability of discrete-time recurrent neural networks with impulse effects

International Nuclear Information System (INIS)

Zhou, L; Li, C; Wan, J

2008-01-01

This paper formulates and studies a class of discrete-time recurrent neural networks with impulse effects. A stability criterion, which characterizes the effects of impulse and stability property of the corresponding impulse-free networks on the stability of the impulsive networks in an aggregate form, is established. Two simplified and numerically tractable criteria are also provided

Approximation methods for the stability analysis of complete synchronization on duplex networks

Science.gov (United States)

Han, Wenchen; Yang, Junzhong

2018-01-01

Recently, the synchronization on multi-layer networks has drawn a lot of attention. In this work, we study the stability of the complete synchronization on duplex networks. We investigate effects of coupling function on the complete synchronization on duplex networks. We propose two approximation methods to deal with the stability of the complete synchronization on duplex networks. In the first method, we introduce a modified master stability function and, in the second method, we only take into consideration the contributions of a few most unstable transverse modes to the stability of the complete synchronization. We find that both methods work well for predicting the stability of the complete synchronization for small networks. For large networks, the second method still works pretty well.

Stabilization Strategies of Supply Networks with Stochastic Switched Topology

Directory of Open Access Journals (Sweden)

Shukai Li

2013-01-01

Full Text Available In this paper, a dynamical supply networks model with stochastic switched topology is presented, in which the stochastic switched topology is dependent on a continuous time Markov process. The goal is to design the state-feedback control strategies to stabilize the dynamical supply networks. Based on Lyapunov stability theory, sufficient conditions for the existence of state feedback control strategies are given in terms of matrix inequalities, which ensure the robust stability of the supply networks at the stationary states and a prescribed H∞ disturbance attenuation level with respect to the uncertain demand. A numerical example is given to illustrate the effectiveness of the proposed method.

Stability analysis of fractional-order Hopfield neural networks with time delays.

Science.gov (United States)

Wang, Hu; Yu, Yongguang; Wen, Guoguang

2014-07-01

This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

Deciphering the imprint of topology on nonlinear dynamical network stability

International Nuclear Information System (INIS)

Nitzbon, J; Schultz, P; Heitzig, J; Kurths, J; Hellmann, F

2017-01-01

Coupled oscillator networks show complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrised to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields. (paper)

Neutral insulin solutions physically stabilized by addition of Zn2+.

Science.gov (United States)

Brange, J; Havelund, S; Hommel, E; Sørensen, E; Kühl, C

1986-01-01

Commercial neutral insulin solutions, all of which contain 2-3 zinc atoms per hexameric unit of insulin, have a relatively limited physical stability when exposed to heat and movement, as for example in insulin infusion pumps. Physical stabilization of neutral insulin solutions has been obtained by addition of two extra Zn2+ per hexamer of insulin. This addition stabilizes porcine and human neutral solutions equally well and does not affect the chemical stability of the insulin. The stabilization is probably obtained by a further strengthening of the hexameric structure of insulin, so that the formation of insoluble insulin fibrils (via the dissociation into the insulin monomer or dimer) is impeded or prevented. The addition of an extra 2 Zn2+ has been shown to be without influence on the insulin immunogenicity in rabbits or on the rate of absorption after subcutaneous injection in diabetic patients. It is concluded that neutral insulin solution can be physically stabilized by addition of extra Zn2+ without affecting other qualities of the insulin preparation including chemical stability, immunogenicity, and duration of action after injection.

Robust and global delay-dependent stability for genetic regulatory networks with parameter uncertainties.

Science.gov (United States)

Tian, Li-Ping; Wang, Jianxin; Wu, Fang-Xiang

2012-09-01

The study of stability is essential for designing or controlling genetic regulatory networks, which can be described by nonlinear differential equations with time delays. Much attention has been paid to the study of delay-independent stability of genetic regulatory networks and as a result, many sufficient conditions have been derived for delay-independent stability. Although it might be more interesting in practice, delay-dependent stability of genetic regulatory networks has been studied insufficiently. Based on the linear matrix inequality (LMI) approach, in this study we will present some delay-dependent stability conditions for genetic regulatory networks. Then we extend these results to genetic regulatory networks with parameter uncertainties. To illustrate the effectiveness of our theoretical results, gene repressilatory networks are analyzed .

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

Science.gov (United States)

Song, Kaida; Wang, Rui; Liu, Yi; Qian, Depei; Zhang, Han; Cai, Jihong

2015-01-01

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

Directory of Open Access Journals (Sweden)

Kaida Song

Full Text Available Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Sintering nanodisperse zirconium powders with various stabilizing additives

Directory of Open Access Journals (Sweden)

Antsiferov V.N.

2011-01-01

Full Text Available Effect of various stabilizing additives on sintering kinetics of nanodisperse powders was studied by thermomechanical analysis. Temperature ranges of the most intense shrinking, characteristic points of shrinking rate changes were established. Peaks characterizing the most intense shrinking of nanodisperse zirconium powder samples were shown to allow to arrange the stabilizing additives as follows: Y2O3→CeO2→TiO2.

Co-extinction in a host-parasite network: identifying key hosts for network stability.

Science.gov (United States)

Dallas, Tad; Cornelius, Emily

2015-08-17

Parasites comprise a substantial portion of total biodiversity. Ultimately, this means that host extinction could result in many secondary extinctions of obligate parasites and potentially alter host-parasite network structure. Here, we examined a highly resolved fish-parasite network to determine key hosts responsible for maintaining parasite diversity and network structure (quantified here as nestedness and modularity). We evaluated four possible host extinction orders and compared the resulting co-extinction dynamics to random extinction simulations; including host removal based on estimated extinction risk, parasite species richness and host level contributions to nestedness and modularity. We found that all extinction orders, except the one based on realistic extinction risk, resulted in faster declines in parasite diversity and network structure relative to random biodiversity loss. Further, we determined species-level contributions to network structure were best predicted by parasite species richness and host family. Taken together, we demonstrate that a small proportion of hosts contribute substantially to network structure and that removal of these hosts results in rapid declines in parasite diversity and network structure. As network stability can potentially be inferred through measures of network structure, our findings may provide insight into species traits that confer stability.

Exponential stability of delayed fuzzy cellular neural networks with diffusion

International Nuclear Information System (INIS)

Huang Tingwen

2007-01-01

The exponential stability of delayed fuzzy cellular neural networks (FCNN) with diffusion is investigated. Exponential stability, significant for applications of neural networks, is obtained under conditions that are easily verified by a new approach. Earlier results on the exponential stability of FCNN with time-dependent delay, a special case of the model studied in this paper, are improved without using the time-varying term condition: dτ(t)/dt < μ

Exponential stability of neural networks with asymmetric connection weights

International Nuclear Information System (INIS)

Yang Jinxiang; Zhong Shouming

2007-01-01

This paper investigates the exponential stability of a class of neural networks with asymmetric connection weights. By dividing the network state variables into various parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Lyapunov function and using the method of the variation of constant. The new conditions are associated with the initial values and are described by some blocks of the interconnection matrix, and do not depend on other blocks. Examples are given to further illustrate the theory

Stability analysis for cellular neural networks with variable delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2006-01-01

Some sufficient conditions for the global exponential stability of cellular neural networks with variable delay are obtained by means of a method based on delay differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result

Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method.

Science.gov (United States)

Li, Xuanying; Li, Xiaotong; Hu, Cheng

2017-12-01

In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Global robust stability of delayed recurrent neural networks

International Nuclear Information System (INIS)

Cao Jinde; Huang Deshuang; Qu Yuzhong

2005-01-01

This paper is concerned with the global robust stability of a class of delayed interval recurrent neural networks which contain time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. A new sufficient condition is presented for the existence, uniqueness, and global robust stability of equilibria for interval neural networks with time delays by constructing Lyapunov functional and using matrix-norm inequality. An error is corrected in an earlier publication, and an example is given to show the effectiveness of the obtained results

Entropies of the automata networks with additive rule

Institute of Scientific and Technical Information of China (English)

Guo－qingGU; GeCHEN; 等

1996-01-01

The matrix presentation for automata networks with additive rule are described.A set of entropy theorems of additive automata network are proved and an analytic formula of its entropy is built.For example,we proved that the topological entropy is identically equal to metric entropy for an additive antomata network.

Stability of a giant connected component in a complex network

Science.gov (United States)

Kitsak, Maksim; Ganin, Alexander A.; Eisenberg, Daniel A.; Krapivsky, Pavel L.; Krioukov, Dmitri; Alderson, David L.; Linkov, Igor

2018-01-01

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

Science.gov (United States)

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

Global Mittag-Leffler stability analysis of fractional-order impulsive neural networks with one-side Lipschitz condition.

Science.gov (United States)

Zhang, Xinxin; Niu, Peifeng; Ma, Yunpeng; Wei, Yanqiao; Li, Guoqiang

2017-10-01

This paper is concerned with the stability analysis issue of fractional-order impulsive neural networks. Under the one-side Lipschitz condition or the linear growth condition of activation function, the existence of solution is analyzed respectively. In addition, the existence, uniqueness and global Mittag-Leffler stability of equilibrium point of the fractional-order impulsive neural networks with one-side Lipschitz condition are investigated by the means of contraction mapping principle and Lyapunov direct method. Finally, an example with numerical simulation is given to illustrate the validity and feasibility of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving the Robustness of Deep Neural Networks via Stability Training

OpenAIRE

Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian

2016-01-01

In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...

Stability analysis for stochastic BAM nonlinear neural network with delays

Science.gov (United States)

Lv, Z. W.; Shu, H. S.; Wei, G. L.

2008-02-01

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria.

Stability analysis for stochastic BAM nonlinear neural network with delays

International Nuclear Information System (INIS)

Lv, Z W; Shu, H S; Wei, G L

2008-01-01

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria

Global exponential stability for nonautonomous cellular neural networks with delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2006-01-01

In this Letter, by utilizing Lyapunov functional method and Halanay inequalities, we analyze global exponential stability of nonautonomous cellular neural networks with delay. Several new sufficient conditions ensuring global exponential stability of the network are obtained. The results given here extend and improve the earlier publications. An example is given to demonstrate the effectiveness of the obtained results

Boundedness, Mittag-Leffler stability and asymptotical ω-periodicity of fractional-order fuzzy neural networks.

Science.gov (United States)

Wu, Ailong; Zeng, Zhigang

2016-02-01

We show that the ω-periodic fractional-order fuzzy neural networks cannot generate non-constant ω-periodic signals. In addition, several sufficient conditions are obtained to ascertain the boundedness and global Mittag-Leffler stability of fractional-order fuzzy neural networks. Furthermore, S-asymptotical ω-periodicity and global asymptotical ω-periodicity of fractional-order fuzzy neural networks is also characterized. The obtained criteria improve and extend the existing related results. To illustrate and compare the theoretical criteria, some numerical examples with simulation results are discussed in detail. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Transcriptional delay stabilizes bistable gene networks.

Science.gov (United States)

Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R

2013-08-02

Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.

Minimum Additive Waste Stabilization (MAWS)

International Nuclear Information System (INIS)

1994-02-01

In the Minimum Additive Waste Stabilization(MAWS) concept, actual waste streams are utilized as additive resources for vitrification, which may contain the basic components (glass formers and fluxes) for making a suitable glass or glassy slag. If too much glass former is present, then the melt viscosity or temperature will be too high for processing; while if there is too much flux, then the durability may suffer. Therefore, there are optimum combinations of these two important classes of constituents depending on the criteria required. The challenge is to combine these resources in such a way that minimizes the use of non-waste additives yet yields a processable and durable final waste form for disposal. The benefit to this approach is that the volume of the final waste form is minimized (waste loading maximized) since little or no additives are used and vitrification itself results in volume reduction through evaporation of water, combustion of organics, and compaction of the solids into a non-porous glass. This implies a significant reduction in disposal costs due to volume reduction alone, and minimizes future risks/costs due to the long term durability and leach resistance of glass. This is accomplished by using integrated systems that are both cost-effective and produce an environmentally sound waste form for disposal. individual component technologies may include: vitrification; thermal destruction; soil washing; gas scrubbing/filtration; and, ion-exchange wastewater treatment. The particular combination of technologies will depend on the waste streams to be treated. At the heart of MAWS is vitrification technology, which incorporates all primary and secondary waste streams into a final, long-term, stabilized glass wasteform. The integrated technology approach, and view of waste streams as resources, is innovative yet practical to cost effectively treat a broad range of DOE mixed and low-level wastes

Networked Timetable Stability Improvement Based on a Bilevel Optimization Programming Model

Directory of Open Access Journals (Sweden)

Xuelei Meng

2014-01-01

Full Text Available Train timetable stability is the possibility to recover the status of the trains to serve as arranged according to the original timetable when the trains are disturbed. To improve the train timetable stability from the network perspective, the bilevel programming model is constructed, in which the upper level programming is to optimize the timetable stability on the network level and the lower is to improve the timetable stability on the dispatching railway segments. Timetable stability on the network level is defined with the variances of the utilization coefficients of the section capacity and station capacity. Weights of stations and sections are decided by the capacity index number and the degrees. The lower level programming focuses on the buffer time distribution plan of the trains operating on the sections and stations, taking the operating rules of the trains as constraints. A novel particle swarm algorithm is proposed and designed for the bilevel programming model. The computing case proves the feasibility of the model and the efficiency of the algorithm. The method outlined in this paper can be embedded in the networked train operation dispatching system.

Influence of additives on the stability of the phases of alumina

International Nuclear Information System (INIS)

Rosario, D.C.C.; Gouvea, D.

2011-01-01

Problems with the stability of gamma alumina in catalytic reactions have been solved with the inclusion of additives during the synthesis of alumina. These additives stabilize the temperature of phase transition allowing the use of metastable alumina at high temperatures, but the mechanisms of action of additives are not well defined. It is known that each family of additive or additives behaves in different ways for this stabilization. This work aimed to study the performance of MgO and ZrO 2 , respectively at different concentrations in alumina synthesized via Pechini. The samples were analyzed by DSC, X-ray diffraction, measurement of specific surface area by BET analysis, and infrared analysis. The results showed an increase in transition temperature for both additives, and a different changes for specific surface area, showing that MgO and ZrO 2 work on improving the stability but with distinct mechanisms. (author)

Structural stability of interaction networks against negative external fields

Science.gov (United States)

Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

2018-04-01

We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

Periodicity and stability for variable-time impulsive neural networks.

Science.gov (United States)

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Globally exponential stability of neural network with constant and variable delays

International Nuclear Information System (INIS)

Zhao Weirui; Zhang Huanshui

2006-01-01

This Letter presents new sufficient conditions of globally exponential stability of neural networks with delays. We show that these results generalize recently published globally exponential stability results. In particular, several different globally exponential stability conditions in the literatures which were proved using different Lyapunov functionals are generalized and unified by using the same Lyapunov functional and the technique of inequality of integral. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed neural networks. Those conditions are less restrictive than those given in the earlier references

Base Stabilization Guidance and Additive Selection for Pavement Design and Rehabilitation

Science.gov (United States)

2017-12-01

Significant improvements have been made in base stabilization practice that include design specifications and methodology, experience with the selection of stabilizing additives, and equipment for distribution and uniform blending of additives. For t...

Industrial Wastes as Auxiliary Additives to Cement/Lime Stabilization of Soils

Directory of Open Access Journals (Sweden)

Jijo James

2016-01-01

Full Text Available Chemical stabilization involves the use of chemical agents for initiating reactions within the soil for modification of its geotechnical properties. Cement and lime stabilization have been the most common stabilization methods adopted for soil treatment. Cement stabilization results in good compressive strengths and is preferred for cohesionless to moderately cohesive soil but loses effectiveness when the soil is highly plastic. Lime stabilization is the most preferred method for plastic clays; however, it proves to be ineffective in sulphate rich clays and performs poorly under extreme conditions. With such drawbacks, lots of researches have been undertaken to address the issues faced with each stabilization method, in particular, the use of solid wastes for soil stabilization. Solid waste reuse has gained high momentum for achieving sustainable waste management in recent times. Research has shown that the use of solid wastes as additives with and replacement for conventional stabilizers has resulted in better results than the performance of either individually. This review provides insight into some of the works done by earlier researchers on lime/cement stabilization with industrial wastes as additives and helps to form a sound platform for further research on industrial wastes as additives to conventional stabilizers.

Fluctuating interaction network and time-varying stability of a natural fish community

Science.gov (United States)

Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

2018-02-01

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Exponential stability of uncertain stochastic neural networks with mixed time-delays

International Nuclear Information System (INIS)

Wang Zidong; Lauria, Stanislao; Fang Jian'an; Liu Xiaohui

2007-01-01

This paper is concerned with the global exponential stability analysis problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability analysis problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov-Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria

Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks

International Nuclear Information System (INIS)

Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.

2012-01-01

This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.

The stability of the international oil trade network from short-term and long-term perspectives

Science.gov (United States)

Sun, Qingru; Gao, Xiangyun; Zhong, Weiqiong; Liu, Nairong

2017-09-01

To examine the stability of the international oil trade network and explore the influence of countries and trade relationships on the trade stability, we construct weighted and unweighted international oil trade networks based on complex network theory using oil trading data between countries from 1996 to 2014. We analyze the stability of international oil trade network (IOTN) from short-term and long-term aspects. From the short-term perspective, we find that the trade volumes play an important role on the stability. Moreover, the weighted IOTN is stable; however, the unweighted networks can better reflect the actual evolution of IOTN. From the long-term perspective, we identify trade relationships that are maintained during the whole sample period to reveal the situation of the whole international oil trade. We provide a way to quantitatively measure the stability of complex network from short-term and long-term perspectives, which can be applied to measure and analyze trade stability of other goods or services.

Novel results for global robust stability of delayed neural networks

International Nuclear Information System (INIS)

Yucel, Eylem; Arik, Sabri

2009-01-01

This paper investigates the global robust convergence properties of continuous-time neural networks with discrete time delays. By employing suitable Lyapunov functionals, some sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point are derived. The conditions can be easily verified as they can be expressed in terms of the network parameters only. Some numerical examples are also given to compare our results with previous robust stability results derived in the literature.

Development of thermal stability additive packages for JP-8

Energy Technology Data Exchange (ETDEWEB)

Anderson, S.D.; Harrison, W.E. III; Edwards, T.; Morris, R.W.; Shouse, D.T. [USAF Wright Lab., Wright-Paterson AFB, OH (United States)

1995-05-01

Advanced military aircraft use fuel as the primary heat sink to cool engine and airframe components. As the fuel is thermally stressed, thermal oxidative reactions take place that result in the formation of deposits. These deposits degrade aircraft performance and ultimately lead to premature servicing of the affected components. The frequency of these incidents, coupled with the projected cooling requirements for future systems, demonstrates that current thermal stability limits are inadequate. In response to this situation, the United States Air Force (USAF) has embarked on a program to improve thermal stability using specially formulated additive packages. Results indicate that additives offer significant thermal stability improvement. This paper describes the USAF program to develop and deploy an improved JP-8 for fleet-wide use by 1998.

Globally exponential stability condition of a class of neural networks with time-varying delays

International Nuclear Information System (INIS)

Liao, T.-L.; Yan, J.-J.; Cheng, C.-J.; Hwang, C.-C.

2005-01-01

In this Letter, the globally exponential stability for a class of neural networks including Hopfield neural networks and cellular neural networks with time-varying delays is investigated. Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived. The condition is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation. Furthermore, the exponential stability degree is more easily assigned than those reported in the literature. Some examples are given to demonstrate validity and excellence of the presented stability condition herein

Stability and stabilisation of a class of networked dynamic systems

Science.gov (United States)

Liu, H. B.; Wang, D. Q.

2018-04-01

We investigate the stability and stabilisation of a linear time invariant networked heterogeneous system with arbitrarily connected subsystems. A new linear matrix inequality based sufficient and necessary condition for the stability is derived, based on which the stabilisation is provided. The obtained conditions efficiently utilise the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Moreover, a sufficient condition only dependent on each individual subsystem is also presented for the stabilisation of the networked systems with a large scale. Numerical simulations show that these conditions are computationally valid in the analysis and synthesis of a large-scale networked system.

A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach

International Nuclear Information System (INIS)

Cao Jinde; Ho, Daniel W.C.

2005-01-01

In this paper, global asymptotic stability is discussed for neural networks with time-varying delay. Several new criteria in matrix inequality form are given to ascertain the uniqueness and global asymptotic stability of equilibrium point for neural networks with time-varying delay based on Lyapunov method and Linear Matrix Inequality (LMI) technique. The proposed LMI approach has the advantage of considering the difference of neuronal excitatory and inhibitory efforts, which is also computationally efficient as it can be solved numerically using recently developed interior-point algorithm. In addition, the proposed results generalize and improve previous works. The obtained criteria also combine two existing conditions into one generalized condition in matrix form. An illustrative example is also given to demonstrate the effectiveness of the proposed results

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

Science.gov (United States)

Syed Ali, M.; Balasubramaniam, P.

2008-07-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2008-01-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB

FAST TCP over optical burst switched networks: Modeling and stability analysis

KAUST Repository

Shihada, Basem

2013-04-01

FAST TCP is important for promoting data-intensive applications since it can cleverly react to both packet loss and delay for detecting network congestion. This paper provides a continuous time model and extensive stability analysis of FAST TCP congestion-control mechanism in bufferless Optical Burst Switched Networks (OBS). The paper first shows that random burst contentions are essential to stabilize the network, but cause throughput degradation in FAST TCP flows when a burst with all the packets from a single round is dropped. Second, it shows that FAST TCP is vulnerable to burst delay and fails to detect network congestion due to the little variation of round-trip time, thus unstable. Finally it shows that introducing extra delays by implementing burst retransmission stabilizes FAST TCP over OBS. The paper proves that FAST TCP is not stable over barebone OBS. However, it is locally, exponentially, and asymptotically stable over OBS with burst retransmission.

Effect of placement of droop based generators in distribution network on small signal stability margin and network loss

DEFF Research Database (Denmark)

Dheer, D.K.; Doolla, S.; Bandyopadhyay, S.

2017-01-01

, small signal stability margin is on the fore. The present research studied the effect of location of droop-controlled DGs on small signal stability margin and network loss on a modified IEEE 13 bus system, an IEEE 33-bus distribution system and a practical 22-bus radial distribution network. A complete...... loss and stability margin is further investigated by identifying the Pareto fronts for modified IEEE 13 bus, IEEE 33 and practical 22-bus radial distribution network with application of Reference point based Non-dominated Sorting Genetic Algorithm (R-NSGA). Results were validated by time domain......For a utility-connected system, issues related to small signal stability with Distributed Generators (DGs) are insignificant due to the presence of a very strong grid. Optimally placed sources in utility connected microgrid system may not be optimal/stable in islanded condition. Among others issues...

Sync in Complex Dynamical Networks: Stability, Evolution, Control, and Application

OpenAIRE

Li, Xiang

2005-01-01

In the past few years, the discoveries of small-world and scale-free properties of many natural and artificial complex networks have stimulated significant advances in better understanding the relationship between the topology and the collective dynamics of complex networks. This paper reports recent progresses in the literature of synchronization of complex dynamical networks including stability criteria, network synchronizability and uniform synchronous criticality in different topologies, ...

Delay-dependent asymptotic stability of mobile ad-hoc networks: A descriptor system approach

International Nuclear Information System (INIS)

Yang Juan; Yang Dan; Zhang Xiao-Hong; Huang Bin; Luo Jian-Lu

2014-01-01

In order to analyze the capacity stability of the time-varying-propagation and delay-dependent of mobile ad-hoc networks (MANETs), in this paper, a novel approach is proposed to explore the capacity asymptotic stability for the delay-dependent of MANETs based on non-cooperative game theory, where the delay-dependent conditions are explicitly taken into consideration. This approach is based on the Lyapunov—Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique. A corresponding Lyapunov—Krasovskii functional is introduced for the stability analysis of this system with use of the descriptor and “neutral-type” model transformation without producing any additional dynamics. The delay-dependent stability criteria are derived for this system. Conditions are given in terms of linear matrix inequalities, and for the first time referred to neutral systems with the time-varying propagation and delay-dependent stability for capacity analysis of MANETs. The proposed criteria are less conservative since they are based on an equivalent model transformation. Furthermore, we also provide an effective and efficient iterative algorithm to solve the constrained stability control model. Simulation experiments have verified the effectiveness and efficiency of our algorithm. (general)

Stability of a neural network model with small-world connections

International Nuclear Information System (INIS)

Li Chunguang; Chen Guanrong

2003-01-01

Small-world networks are highly clustered networks with small distances among the nodes. There are many biological neural networks that present this kind of connection. There are no special weightings in the connections of most existing small-world network models. However, this kind of simply connected model cannot characterize biological neural networks, in which there are different weights in synaptic connections. In this paper, we present a neural network model with weighted small-world connections and further investigate the stability of this model

Stability of Almost Periodic Solution for a General Class of Discontinuous Neural Networks with Mixed Time-Varying Delays

Directory of Open Access Journals (Sweden)

Yingwei Li

2013-01-01

Full Text Available The global exponential stability issues are considered for almost periodic solution of the neural networks with mixed time-varying delays and discontinuous neuron activations. Some sufficient conditions for the existence, uniqueness, and global exponential stability of almost periodic solution are achieved in terms of certain linear matrix inequalities (LMIs, by applying differential inclusions theory, matrix inequality analysis technique, and generalized Lyapunov functional approach. In addition, the existence and asymptotically almost periodic behavior of the solution of the neural networks are also investigated under the framework of the solution in the sense of Filippov. Two simulation examples are given to illustrate the validity of the theoretical results.

Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2009-01-01

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Bidirectional Associative Memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by LMI optimization algorithms to guarantee the exponential stability of uncertain BAM neural networks with time-varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.

Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays

International Nuclear Information System (INIS)

Zhao Hongyong; Ding Nan; Chen Ling

2009-01-01

This paper is concerned with the problem of exponential stability analysis for fuzzy cellular neural network with delays. By constructing suitable Lyapunov functional and using stochastic analysis we present some sufficient conditions ensuring almost sure exponential stability for the network. Moreover, an example is given to demonstrate the advantages of our method.

Stability of Intelligent Transportation Network Dynamics: A Daily Path Flow Adjustment considering Travel Time Differentiation

Directory of Open Access Journals (Sweden)

Ming-Chorng Hwang

2015-01-01

Full Text Available A theoretic formulation on how traffic time information distributed by ITS operations influences the trajectory of network flows is presented in this paper. The interactions between users and ITS operator are decomposed into three parts: (i travel time induced path flow dynamics (PFDTT; (ii demand induced path flow dynamics (PFDD; and (iii predicted travel time dynamics for an origin-destination (OD pair (PTTDOD. PFDTT describes the collective results of user’s daily route selection by pairwise comparison of path travel time provided by ITS services. The other two components, PTTDOD and PFDD, are concentrated on the evolutions of system variables which are predicted and observed, respectively, by ITS operators to act as a benchmark in guiding the target system towards an expected status faster. In addition to the delivered modelings, the stability theorem of the equilibrium solution in the sense of Lyapunov stability is also provided. A Lyapunov function is developed and employed to the proof of stability theorem to show the asymptotic behavior of the aimed system. The information of network flow dynamics plays a key role in traffic control policy-making. The evaluation of ITS-based strategies will not be reasonable without a well-established modeling of network flow evolutions.

Stability issues in reconstitution by weapon addition

Energy Technology Data Exchange (ETDEWEB)

Canavan, G.H.

1997-08-01

Reconstitution of strategic forces by the unilateral uploading of additional weapons from initially symmetric modest force levels reduces first strike stability. These changes are quantified and traced to changes in first and second strike costs in a model of missile exchanges in which both strikes are optimized analytically.

On global exponential stability of high-order neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Baoyong; Xu Shengyuan; Li Yongmin; Chu Yuming

2007-01-01

This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria

On global exponential stability of high-order neural networks with time-varying delays

Energy Technology Data Exchange (ETDEWEB)

Zhang Baoyong [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China)]. E-mail: baoyongzhang@yahoo.com.cn; Xu Shengyuan [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China)]. E-mail: syxu02@yahoo.com.cn; Li Yongmin [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China) and Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)]. E-mail: ymlwww@163.com; Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)

2007-06-18

This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria.

Transient stability analysis of a distribution network with distributed generators

NARCIS (Netherlands)

Xyngi, I.; Ishchenko, A.; Popov, M.; Sluis, van der L.

2009-01-01

This letter describes the transient stability analysis of a 10-kV distribution network with wind generators, microturbines, and CHP plants. The network being modeled in Matlab/Simulink takes into account detailed dynamic models of the generators. Fault simulations at various locations are

Evolution of regulatory networks towards adaptability and stability in a changing environment

Science.gov (United States)

Lee, Deok-Sun

2014-11-01

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

Global robust exponential stability for interval neural networks with delay

International Nuclear Information System (INIS)

Cui Shihua; Zhao Tao; Guo Jie

2009-01-01

In this paper, new sufficient conditions for globally robust exponential stability of neural networks with either constant delays or time-varying delays are given. We show the sufficient conditions for the existence, uniqueness and global robust exponential stability of the equilibrium point by employing Lyapunov stability theory and linear matrix inequality (LMI) technique. Numerical examples are given to show the approval of our results.

Some criteria for robust stability of Cohen-Grossberg neural networks with delays

International Nuclear Information System (INIS)

Xiong Weili; Xu Baoguo

2008-01-01

This paper considers the problem of robust stability of Cohen-Grossberg neural networks with time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Comparisons between our results and previous results admits our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks. Numerical examples are given to illustrate the effectiveness of our results

Robust exponential stability and domains of attraction in a class of interval neural networks

International Nuclear Information System (INIS)

Yang Xiaofan; Liao Xiaofeng; Bai Sen; Evans, David J

2005-01-01

This paper addresses robust exponential stability as well as domains of attraction in a class of interval neural networks. A sufficient condition for an equilibrium point to be exponentially stable is established. And an estimate on the domains of attraction of exponentially stable equilibrium points is presented. Both the condition and the estimate are formulated in terms of the parameter intervals, the neurons' activation functions and the equilibrium point. Hence, they are easily checkable. In addition, our results neither depend on monotonicity of the activation functions nor on coupling conditions between the neurons. Consequently, these results are of practical importance in evaluating the performance of interval associative memory networks

Robust stability of bidirectional associative memory neural networks with time delays

Science.gov (United States)

Park, Ju H.

2006-01-01

Based on the Lyapunov Krasovskii functionals combined with linear matrix inequality approach, a novel stability criterion is proposed for asymptotic stability of bidirectional associative memory neural networks with time delays. A novel delay-dependent stability criterion is given in terms of linear matrix inequalities, which can be solved easily by various optimization algorithms.

Robust stability of bidirectional associative memory neural networks with time delays

International Nuclear Information System (INIS)

Park, Ju H.

2006-01-01

Based on the Lyapunov-Krasovskii functionals combined with linear matrix inequality approach, a novel stability criterion is proposed for asymptotic stability of bidirectional associative memory neural networks with time delays. A novel delay-dependent stability criterion is given in terms of linear matrix inequalities, which can be solved easily by various optimization algorithms

Global asymptotic stability of delayed Cohen-Grossberg neural networks

International Nuclear Information System (INIS)

Wu Wei; Cui Baotong; Huang Min

2007-01-01

In this letter, the global asymptotic stability of a class of Cohen-Grossberg neural networks with time-varying delays is discussed. A new set of sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence. Our criteria represent an extension of the existing results in literatures. An example is also presented to compare our results with the previous results

Master stability functions reveal diffusion-driven pattern formation in networks

Science.gov (United States)

Brechtel, Andreas; Gramlich, Philipp; Ritterskamp, Daniel; Drossel, Barbara; Gross, Thilo

2018-03-01

We study diffusion-driven pattern formation in networks of networks, a class of multilayer systems, where different layers have the same topology, but different internal dynamics. Agents are assumed to disperse within a layer by undergoing random walks, while they can be created or destroyed by reactions between or within a layer. We show that the stability of homogeneous steady states can be analyzed with a master stability function approach that reveals a deep analogy between pattern formation in networks and pattern formation in continuous space. For illustration, we consider a generalized model of ecological meta-food webs. This fairly complex model describes the dispersal of many different species across a region consisting of a network of individual habitats while subject to realistic, nonlinear predator-prey interactions. In this example, the method reveals the intricate dependence of the dynamics on the spatial structure. The ability of the proposed approach to deal with this fairly complex system highlights it as a promising tool for ecology and other applications.

THERMOOXIDATIVE STABILITY OF JET FUEL WITH FULLERENES AS AN ADDITIVE

Directory of Open Access Journals (Sweden)

С.В. Іванов

2012-10-01

Full Text Available  Heating of fuels in presence of oxygen reduces their thermal-oxidative stability, leads to a solid phase in the form of sludge and tar, which, sedimented at the details of the fuel system, change its characteristics and cause contamination of fuel filters and injectors, spool control sticking, reduce efficiency of heat exchangers. Nanomaterials, performance of which is considerably superior to the natural materials, are the basis for the movement of humanity's progress. Therefore, with a develpoment of technologies it has become necessary to carry out a research of modified additives – fullerens, to improve an oxidative stability of fuels. We have carried out an investigation of thermal-oxidative stability of fuel RT as a function of additive C60 concentration. The results has shown that even 0,043 g/l fullerene addition as an antioxidant, reduces the amount of sediment in the fuel almost by half. Usage of fullerenes for improvement of petroleum products performance properties is a promising area of research.

Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks.

Science.gov (United States)

Chen, Wu-Hua; Lu, Xiaomei; Zheng, Wei Xing

2015-04-01

This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.

46 CFR 173.025 - Additional intact stability standards: Counterballasted vessels.

Science.gov (United States)

2010-10-01

...) SUBDIVISION AND STABILITY SPECIAL RULES PERTAINING TO VESSEL USE Lifting § 173.025 Additional intact stability standards: Counterballasted vessels. (a) Each vessel equipped to counterballast while lifting must be shown... loading and operation and at each combination of hook load and crane radius. (b) When doing the...

Global exponential stability for discrete-time neural networks with variable delays

International Nuclear Information System (INIS)

Chen Wuhua; Lu Xiaomei; Liang Dongying

2006-01-01

This Letter provides new exponential stability criteria for discrete-time neural networks with variable delays. The main technique is to reduce exponential convergence estimation of the neural network solution to that of one component of the corresponding solution by constructing Lyapunov function based on M-matrix. By introducing the tuning parameter diagonal matrix, the delay-independent and delay-dependent exponential stability conditions have been unified in the same mathematical formula. The effectiveness of the new results are illustrated by three examples

Stability Analysis of Neural Networks-Based System Identification

Directory of Open Access Journals (Sweden)

Talel Korkobi

2008-01-01

Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

Radiolytic stabilization of poly(methyl methacrylate) using commercial additives

International Nuclear Information System (INIS)

Aquino, Katia Aparecida da Silva

2000-04-01

Poly(methyl methacrylate), PMMA, Acrigel, a Brazilian polymer, is used in the manufacture of medical supplies sterelizable by ionizing radiation. However, when PMMA is gamma-irradiated it undergoes main chain scissions, which promote molecular degradation causing reduction in its mechanical properties. Therefore, radiolytic of PMMA is important for it to become commercially radiosterizable. In this work some commercial additives, originally used in photo-and thermo-oxidate stabilization of polymers, were tested. Only two additives, type HALS (Hindered Amine Light Stabilizer), denoted Scavenger, showed a good protective quality. The investigation of radiation-induced main scissions was carried out by viscosimetric method. The most effective additive, added to the polymer system at 0.3 w/w%, promotes a great molecular radioprotection of 93%. That means a reduction of G-value (scissions/100 eV) from 0.611 to 0.053. In addition, the glassy transition temperature (T g ) of PMMA (no additive) significantly changed by radiation does not change when PMMA (with additive) is irradiated. The spectroscopy analysis, FT-IR and NMR ( 1 H), showed that the radioprotector added to the system does not change the PMMA structure. (author)

Global stability of stochastic high-order neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Wang Zidong; Fang Jianan; Liu Xiaohui

2008-01-01

High-order neural networks can be considered as an expansion of Hopfield neural networks, and have stronger approximation property, faster convergence rate, greater storage capacity, and higher fault tolerance than lower-order neural networks. In this paper, the global asymptotic stability analysis problem is considered for a class of stochastic high-order neural networks with discrete and distributed time-delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are derived, which guarantee the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the stochastic high-order delayed neural networks under consideration are globally asymptotically stable in the mean square if two linear matrix inequalities (LMIs) are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also shown that the main results in this paper cover some recently published works. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria

Stability and synchronization control of stochastic neural networks

CERN Document Server

Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

2016-01-01

This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

Stability of Delayed Hopfield Neural Networks with Variable-Time Impulses

Directory of Open Access Journals (Sweden)

Yangjun Pei

2014-01-01

Full Text Available In this paper the globally exponential stability criteria of delayed Hopfield neural networks with variable-time impulses are established. The proposed criteria can also be applied in Hopfield neural networks with fixed-time impulses. A numerical example is presented to illustrate the effectiveness of our theoretical results.

Stabilization of Networked Control Systems Under Feedback-based Communication

National Research Council Canada - National Science Library

Zhang, Lei; Hristu-Varsakelis, Dimitrios

2004-01-01

We study the stabilization of a networked control system (NSC) in which multiple sensors and actuators of a physical plant share a communication medium to exchange information with a remote controller...

Event-Based Stabilization over Networks with Transmission Delays

Directory of Open Access Journals (Sweden)

Xiangyu Meng

2012-01-01

Full Text Available This paper investigates asymptotic stabilization for linear systems over networks based on event-driven communication. A new communication logic is proposed to reduce the feedback effort, which has some advantages over traditional ones with continuous feedback. Considering the effect of time-varying transmission delays, the criteria for the design of both the feedback gain and the event-triggering mechanism are derived to guarantee the stability and performance requirements. Finally, the proposed techniques are illustrated by an inverted pendulum system and a numerical example.

Strong-stability-preserving additive linear multistep methods

KAUST Repository

Hadjimichael, Yiannis

2018-02-20

The analysis of strong-stability-preserving (SSP) linear multistep methods is extended to semi-discretized problems for which different terms on the right-hand side satisfy different forward Euler (or circle) conditions. Optimal perturbed and additive monotonicity-preserving linear multistep methods are studied in the context of such problems. Optimal perturbed methods attain larger monotonicity-preserving step sizes when the different forward Euler conditions are taken into account. On the other hand, we show that optimal SSP additive methods achieve a monotonicity-preserving step-size restriction no better than that of the corresponding nonadditive SSP linear multistep methods.

FAST TCP over optical burst switched networks: Modeling and stability analysis

KAUST Repository

Shihada, Basem; El-Ferik, Sami; Ho, Pin-Han

2013-01-01

congestion-control mechanism in bufferless Optical Burst Switched Networks (OBS). The paper first shows that random burst contentions are essential to stabilize the network, but cause throughput degradation in FAST TCP flows when a burst with all the packets

Multi-stability and almost periodic solutions of a class of recurrent neural networks

International Nuclear Information System (INIS)

Liu Yiguang; You Zhisheng

2007-01-01

This paper studies multi-stability, existence of almost periodic solutions of a class of recurrent neural networks with bounded activation functions. After introducing a sufficient condition insuring multi-stability, many criteria guaranteeing existence of almost periodic solutions are derived using Mawhin's coincidence degree theory. All the criteria are constructed without assuming the activation functions are smooth, monotonic or Lipschitz continuous, and that the networks contains periodic variables (such as periodic coefficients, periodic inputs or periodic activation functions), so all criteria can be easily extended to fit many concrete forms of neural networks such as Hopfield neural networks, or cellular neural networks, etc. Finally, all kinds of simulations are employed to illustrate the criteria

Stability analysis of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time varying delays

International Nuclear Information System (INIS)

Ali, M. Syed

2014-01-01

In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen—Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples

Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks

NARCIS (Netherlands)

Thebault, E.M.C.; Fontaine, C.

2010-01-01

Research on the relationship between the architecture of ecological networks and community stability has mainly focused on one type of interaction at a time, making difficult any comparison between different network types. We used a theoretical approach to show that the network architecture favoring

H∞ Control for a Networked Control Model of Systems with Two Additive Time-Varying Delays

Directory of Open Access Journals (Sweden)

Hanyong Shao

2014-01-01

Full Text Available This paper is concerned with H∞ control for a networked control model of systems with two additive time-varying delays. A new Lyapunov functional is constructed to make full use of the information of the delays, and for the derivative of the Lyapunov functional a novel technique is employed to compute a tighter upper bound, which is dependent on the two time-varying delays instead of the upper bounds of them. Then the convex polyhedron method is proposed to check the upper bound of the derivative of the Lyapunov functional. The resulting stability criteria have fewer matrix variables but less conservatism than some existing ones. The stability criteria are applied to designing a state feedback controller, which guarantees that the closed-loop system is asymptotically stable with a prescribed H∞ disturbance attenuation level. Finally examples are given to show the advantages of the stability criteria and the effectiveness of the proposed control method.

Mean square exponential stability of stochastic delayed Hopfield neural networks

International Nuclear Information System (INIS)

Wan Li; Sun Jianhua

2005-01-01

Stochastic effects to the stability property of Hopfield neural networks (HNN) with discrete and continuously distributed delay are considered. By using the method of variation parameter, inequality technique and stochastic analysis, the sufficient conditions to guarantee the mean square exponential stability of an equilibrium solution are given. Two examples are also given to demonstrate our results

Influence of additives on phase stabilization of scandia-doped zirconia

Energy Technology Data Exchange (ETDEWEB)

Muccillo, Eliana Navarro dos Santos; Grosso, Robson Lopes; Reis, Shirley Leite dos; Muccillo, Reginaldo, E-mail: enavarro@usp.br, E-mail: roblopeg@usp.br, E-mail: shirley.reis@usp.br, E-mail: muccillo@usp.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2017-04-15

The effects of small additions of tin, zinc, calcium and boron oxides on phase composition and electrical conductivity of zirconia-10 mol% scandia were investigated. Compounds containing 1 mol% zinc, tin and calcium oxides and 1, 3 and 5 wt.% boron oxide were prepared by solid state reaction and characterized by X-ray diffraction, density measurements, scanning electron microscopy and impedance spectroscopy. Full stabilization of the cubic structure at room temperature was obtained with additions of 1 mol% calcium oxide and 2 wt.% boron oxide. Partially stabilized compounds exhibit herringbone structure, characteristic of the β- rhombohedric phase. Specimens with calcium as additive show total conductivity of 23.8 mS.cm{sup -1} at 750 deg C with activation energy of 1.13 eV. Liquid phase sintering by boron oxide addition is effective to enhance the densification of the solid electrolyte. (author)

Global asymptotic stability of Cohen-Grossberg neural networks with constant and variable delays

International Nuclear Information System (INIS)

Wu Wei; Cui Baotong; Huang Min

2007-01-01

Global asymptotic stability of Cohen-Grossberg neural networks with constant and variable delays is studied. Some sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence by using different Lyapunov functionals. Our criteria represent an extension of the existing results in literatures. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks. Those conditions are less restrictive than those given in the earlier reference

Change, Stability and Ambidexterity in Business Networks -In a Offshore Wind Farm Context

DEFF Research Database (Denmark)

Lutz, Salla; Brink, Tove; Madsen, Svend Ole

The article sheds light on, how actors can cope with stability and change in business networks. By applying theoretical elements from the industrial network approach, and the organizational ambidexterity the phenomenon was studied in the context of operation and maintenance in offshore wind farms....... We employed a qualitative research approach through a focus group interview and 20 semi-structured interviews. The findings revealed both activities and resources that can enhance the necessary development of stability. At the same time changes are necessary when striving for well performing wind...... farms. In order to achieve both stability and change knowledge sharing and collaboration play a crucial role in all the levels, i.e. in organizations, in relationship dyads and in the network....

How plants connect pollination and herbivory networks and their contribution to community stability.

Science.gov (United States)

Sauve, Alix M C; Thébault, Elisa; Pocock, Michael J O; Fontaine, Colin

2016-04-01

Pollination and herbivory networks have mainly been studied separately, highlighting their distinct structural characteristics and the related processes and dynamics. However, most plants interact with both pollinators and herbivores, and there is evidence that both types of interaction affect each other. Here we investigated the way plants connect these mutualistic and antagonistic networks together, and the consequences for community stability. Using an empirical data set, we show that the way plants connect pollination and herbivory networks is not random and promotes community stability. Analyses of the structure of binary and quantitative networks show different results: the plants' generalism with regard to pollinators is positively correlated to their generalism with regard to herbivores when considering binary interactions, but not when considering quantitative interactions. We also show that plants that share the same pollinators do not share the same herbivores. However, the way plants connect pollination and herbivory networks promotes stability for both binary and quantitative networks. Our results highlight the relevance of considering the diversity of interaction types in ecological communities, and stress the need to better quantify the costs and benefits of interactions, as well as to develop new metrics characterizing the way different interaction types are combined within ecological networks.

Adaptive neural networks control for camera stabilization with active suspension system

Directory of Open Access Journals (Sweden)

Feng Zhao

2015-08-01

Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

Stochastic stability analysis for delayed neural networks of neutral type with Markovian jump parameters

International Nuclear Information System (INIS)

Lou Xuyang; Cui Baotong

2009-01-01

In this paper, the problem of stochastic stability for a class of delayed neural networks of neutral type with Markovian jump parameters is investigated. The jumping parameters are modelled as a continuous-time, discrete-state Markov process. A sufficient condition guaranteeing the stochastic stability of the equilibrium point is derived for the Markovian jumping delayed neural networks (MJDNNs) with neutral type. The stability criterion not only eliminates the differences between excitatory and inhibitory effects on the neural networks, but also can be conveniently checked. The sufficient condition obtained can be essentially solved in terms of linear matrix inequality. A numerical example is given to show the effectiveness of the obtained results.

Stability and voltage rise at operating frequency in ehv networks at 500 to 700 kV

Energy Technology Data Exchange (ETDEWEB)

Glavitsch, H

1964-01-01

The importance of stability is stressed in the case of long transmission lines interconnecting different systems. To achieve better stability one requires improvement of network stability and generator stability. The methods of excitation and electric braking for improving the dynamic stability of generators are described. The stability of two networks connected by a line is examined theoretically. The various causes of voltage rises at operating frequency, e.g., loading, load dropping etc., and the usual types of transients are discussed.

Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

International Nuclear Information System (INIS)

Wan Li; Zhou Qinghua

2007-01-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established

Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

Science.gov (United States)

Wan, Li; Zhou, Qinghua

2007-11-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established.

Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Yan, Ji; Bao-Tong, Cui

2010-01-01

In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that they have fewer matrix variables yet less conservatism. In addition, a numerical example is provided to illustrate the applicability of the result using the linear matrix inequality toolbox in MATLAB. (general)

Foam-film-stabilized liquid bridge networks in evaporative lithography and wet granular matter

KAUST Repository

Vakarelski, Ivan Uriev

2013-04-23

Evaporative lithography using latex particle templates is a novel approach for the self-assembly of suspension-dispersed nanoparticles into ordered microwire networks. The phenomenon that drives the self-assembly process is the propagation of a network of interconnected liquid bridges between the template particles and the underlying substrate. With the aid of video microscopy, we demonstrate that these liquid bridges are in fact the border zone between the underlying substrate and foam films vertical to the substrate, which are formed during the evaporation of the liquid from the suspension. The stability of the foam films and thus the liquid bridge network stability are due to the presence of a small amount of surfactant in the evaporating solution. We show that the same type of foam-film-stabilized liquid bridge network can also propagate in 3D clusters of spherical particles, which has important implications for the understanding of wet granular matter. © 2013 American Chemical Society.

Synchronized stability in a reaction–diffusion neural network model

Energy Technology Data Exchange (ETDEWEB)

Wang, Ling; Zhao, Hongyong, E-mail: hongyongz@126.com

2014-11-14

The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability.

Synchronized stability in a reaction–diffusion neural network model

International Nuclear Information System (INIS)

Wang, Ling; Zhao, Hongyong

2014-01-01

The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability

Delay-dependent exponential stability of cellular neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2005-01-01

The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate the effectiveness of the obtained results

Global stability of a susceptible-infected-susceptible epidemic model on networks with individual awareness

International Nuclear Information System (INIS)

Li Ke-Zan; Xu Zhong-Pu; Zhu Guang-Hu; Ding Yong

2014-01-01

Recent research results indicate that individual awareness can play an important influence on epidemic spreading in networks. By local stability analysis, a significant conclusion is that the embedded awareness in an epidemic network can increase its epidemic threshold. In this paper, by using limit theory and dynamical system theory, we further give global stability analysis of a susceptible-infected-susceptible (SIS) epidemic model on networks with awareness. Results show that the obtained epidemic threshold is also a global stability condition for its endemic equilibrium, which implies the embedded awareness can enhance the epidemic threshold globally. Some numerical examples are presented to verify the theoretical results. (interdisciplinary physics and related areas of science and technology)

Improved Lower Bounds on the Price of Stability of Undirected Network Design Games

Science.gov (United States)

Bilò, Vittorio; Caragiannis, Ioannis; Fanelli, Angelo; Monaco, Gianpiero

Bounding the price of stability of undirected network design games with fair cost allocation is a challenging open problem in the Algorithmic Game Theory research agenda. Even though the generalization of such games in directed networks is well understood in terms of the price of stability (it is exactly H n , the n-th harmonic number, for games with n players), far less is known for network design games in undirected networks. The upper bound carries over to this case as well while the best known lower bound is 42/23 ≈ 1.826. For more restricted but interesting variants of such games such as broadcast and multicast games, sublogarithmic upper bounds are known while the best known lower bound is 12/7 ≈ 1.714. In the current paper, we improve the lower bounds as follows. We break the psychological barrier of 2 by showing that the price of stability of undirected network design games is at least 348/155 ≈ 2.245. Our proof uses a recursive construction of a network design game with a simple gadget as the main building block. For broadcast and multicast games, we present new lower bounds of 20/11 ≈ 1.818 and 1.862, respectively.

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

Science.gov (United States)

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new delay-independent condition for global robust stability of neural networks with time delays.

Science.gov (United States)

Samli, Ruya

2015-06-01

This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bounded. By employing the results of Lyapunov stability theory and matrix theory, new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for delayed neural networks are presented. The results reported in this paper can be easily tested by checking some special properties of symmetric matrices associated with the parameter uncertainties of neural networks. We also present a numerical example to show the effectiveness of the proposed theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Network structure and thermal stability study of high temperature seal glass

Science.gov (United States)

Lu, K.; Mahapatra, M. K.

2008-10-01

High temperature seal glass has stringent requirement on glass thermal stability, which is dictated by glass network structures. In this study, a SrO-La2O3-Al2O3-B2O3-SiO2 based glass system was studied using nuclear magnetic resonance, Raman spectroscopy, and x-ray diffraction for solid oxide cell application purpose. Glass structural unit neighboring environment and local ordering were evaluated. Glass network connectivity as well as silicon and boron glass former coordination were calculated for different B2O3:SiO2 ratios. Thermal stability of the borosilicate glasses was studied after thermal treatment at 850 °C. The study shows that high B2O3 content induces BO4 and SiO4 structural unit ordering, increases glass localized inhomogeneity, decreases glass network connectivity, and causes devitrification. Glass modifiers interact with either silicon- or boron-containing structural units and form different devitrified phases at different B2O3:SiO2 ratios. B2O3-free glass shows the best thermal stability among the studied compositions, remaining stable after thermal treatment for 200 h at 850 °C.

Monitoring of stability of ASG-EUPOS network coordinates

Science.gov (United States)

Figurski, M.; Szafranek, K.; Wrona, M.

2009-04-01

ASG-EUPOS (Active Geodetic Network - European Position Determination System) is the national system of precise satellite positioning in Poland, which increases a density of regional and global GNSS networks and is widely used by public administration, national institutions, entrepreneurs and citizens (especially surveyors). In near future ASG-EUPOS is to take role of main national network. Control of proper activity of stations and realization of ETRS'89 is a necessity. User of the system needs to be sure that observations quality and coordinates accuracy are high enough. Coordinates of IGS (International GNSS Service) and EPN (European Permanent Network) stations are precisely determined and any changes are monitored all the time. Observations are verified before they are archived in regional and global databases. The same applies to ASG-EUPOS. This paper concerns standardization of GNSS observations from different stations (uniform adjustment), examination of solutions correctness according to IGS and EPN standards and stability of solutions and sites activity

Valency stabilization of Polyvalent Iron Ions in Solution By some Organic additives during Gamma Irradiation

International Nuclear Information System (INIS)

Barakat, M.F.; Abdel Hamid, M.M.

2012-01-01

Valency stabilization of polyvalent ions in gamma irradiated aqueous solutions is sometimes necessary for the success of some chemical operations. In some previous publications valency stabilization of some polyvalent ions in solution upon gamma irradiation was achieved by using additives capable of interacting with the oxidizing or reducing species formed by water radiolysis in the medium. The results showed that the duration of valency stabilization depends on the concentration of the additives used.In the present work, a series of some organic additives has been used to investigate their capability in inducing valency stabilization of polyvalent iron ions when subjected to extended gamma irradiation periods. The results showed that the efficiency of valency stabilization depends on the amount and chemical structure of the organic additive used

Global robust exponential stability analysis for interval recurrent neural networks

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.; Zou Yun

2004-01-01

This Letter investigates the problem of robust global exponential stability analysis for interval recurrent neural networks (RNNs) via the linear matrix inequality (LMI) approach. The values of the time-invariant uncertain parameters are assumed to be bounded within given compact sets. An improved condition for the existence of a unique equilibrium point and its global exponential stability of RNNs with known parameters is proposed. Based on this, a sufficient condition for the global robust exponential stability for interval RNNs is obtained. Both of the conditions are expressed in terms of LMIs, which can be checked easily by various recently developed convex optimization algorithms. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition

EFFECT OF PHYTOGENIC ADDITIVES ON OXIDATION STABILITY OF FROZEN CHICKEN MEAT

Directory of Open Access Journals (Sweden)

Marek Bobko

2016-10-01

Full Text Available In this study, oxidative stability of frozen chicken breast and thigh muscle after application of feed mixtures enriched by phytogenic additives was investigated. The 150 pieces one-day-old chicks of Cobb 500 hybrid combination were divided into three groups: C - control group, G1 – experimental group with addition 1000 mg kg-1 Biostrong 510 + FortiBac and G2 – experimental group with addition 1000 mg kg-1 Agolin Acid.  The broiler chickens were fed during 42 days by ad libitum. Samples of chicken breast and thigh muscle were analysed in the 1st day and after 1st, 2nd, 3rd, 4th, 5th and 6th month of frozen storage at -18 °C. During testing period we recorded positive influence of phytogenic additives on oxidative stability of chicken meat in experimental groups (G1, G2. After 6th month of frozen storage, we found higher malondialdehyde (MDA values and lower oxidative stability of breast muscle in control group (0.167 mg.kg-1 compared to experimental groups G1 (0.149 mg.kg-1 and G2 (0.145 mg.kg-1. Similar tendency of oxidative changes as in the breast muscle was recorded in the thigh muscle. At the end of frozen storage MDA average values of thigh muscle were higher in control group (0.181 mg.kg-1 compared to experimental groups (G1 - 0.163 mg.kg-1 and G2 - 0.160 mg.kg-1.  Based on the obtained results we can stated, that phytogenic additives applied in chicken nutrition had positive influence of, namely on oxidation stability of fatty substances.

Robust stability for stochastic bidirectional associative memory neural networks with time delays

Science.gov (United States)

Shu, H. S.; Lv, Z. W.; Wei, G. L.

2008-02-01

In this paper, the asymptotic stability is considered for a class of uncertain stochastic bidirectional associative memory neural networks with time delays and parameter uncertainties. The delays are time-invariant and the uncertainties are norm-bounded that enter into all network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov-Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed criteria.

Analysis on voltage stability of PFN in modulator using De-Qing network

International Nuclear Information System (INIS)

Wang Dong; Zhang Yongming; Zhu Fuquan

1987-01-01

Using the numerical simulation of PFN charging circuit and De-Qing network, a study of voltage stability of BEPC 80 MW klystron pulse modulator has been carried out. The results presented in the paper indicate the quantitative correlation between leakage inductance and voltage stability

Stability Analysis and Application for Delayed Neural Networks Driven by Fractional Brownian Noise.

Science.gov (United States)

Zhou, Wuneng; Zhou, Xianghui; Yang, Jun; Zhou, Jun; Tong, Dongbing

2018-05-01

This paper deals with two types of the stability problem for the delayed neural networks driven by fractional Brownian noise (FBN). The existence and the uniqueness of the solution to the main system with respect to FBN are proved via fixed point theory. Based on Hilbert-Schmidt operator theory and analytic semigroup principle, the mild solution of the stochastic neural networks is obtained. By applying the stochastic analytic technique and some well-known inequalities, the asymptotic stability criteria and the exponential stability condition are established. Both numerical example and practical application for synchronization control of multiagent system are provided to illustrate the effectiveness and potential of the proposed techniques.

Additive Feed Forward Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1999-01-01

This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

Global robust stability of bidirectional associative memory neural networks with multiple time delays.

Science.gov (United States)

Senan, Sibel; Arik, Sabri

2007-10-01

This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.

Novel global robust stability criterion for neural networks with delay

International Nuclear Information System (INIS)

Singh, Vimal

2009-01-01

A novel criterion for the global robust stability of Hopfield-type interval neural networks with delay is presented. An example illustrating the improvement of the present criterion over several recently reported criteria is given.

Time-dependent deformation of polymer network in polymer-stabilized cholesteric liquid crystals (Conference Presentation)

Science.gov (United States)

Lee, Kyung Min; Tondiglia, Vincent P.; Bunning, Timothy J.; White, Timothy J.

2017-02-01

Recently, we reported direct current (DC) field controllable electro-optic (EO) responses of negative dielectric anisotropy polymer stabilized cholesteric liquid crystals (PSCLCs). A potential mechanism is: Ions in the liquid crystal mixtures are trapped in/on the polymer network during the fast photopolymerization process, and the movement of ions by the application of the DC field distorts polymer network toward the negative electrode, inducing pitch variation through the cell thickness, i.e., pitch compression on the negative electrode side and pitch expansion on positive electrode side. As the DC voltage is directly applied to a target voltage, charged polymer network is deformed and the reflection band is tuned. Interestingly, the polymer network deforms further (red shift of reflection band) with time when constantly applied DC voltage, illustrating DC field induced time dependent deformation of polymer network (creep-like behavior). This time dependent reflection band changes in PSCLCs are investigated by varying the several factors, such as type and concentration of photoinitiators, liquid crystal monomer content, and curing condition (UV intensity and curing time). In addition, simple linear viscoelastic spring-dashpot models, such as 2-parameter Kelvin and 3-parameter linear models, are used to investigate the time-dependent viscoelastic behaviors of polymer networks in PSCLC.

Addition of sodium caseinate to skim milk increases nonsedimentable casein and causes significant changes in rennet-induced gelation, heat stability, and ethanol stability.

Science.gov (United States)

Lin, Yingchen; Kelly, Alan L; O'Mahony, James A; Guinee, Timothy P

2017-02-01

The protein content of skim milk was increased from 3.3 to 4.1% (wt/wt) by the addition of a blend of skim milk powder and sodium caseinate (NaCas), in which the weight ratio of skim milk powder to NaCas was varied from 0.8:0.0 to 0.0:0.8. Addition of NaCas increased the levels of nonsedimentable casein (from ∼6 to 18% of total casein) and calcium (from ∼36 to 43% of total calcium) and reduced the turbidity of the fortified milk, to a degree depending on level of NaCas added. Rennet gelation was adversely affected by the addition of NaCas at 0.2% (wt/wt) and completely inhibited at NaCas ≥0.4% (wt/wt). Rennet-induced hydrolysis was not affected by added NaCas. The proportion of total casein that was nonsedimentable on centrifugation (3,000 × g, 1 h, 25°C) of the rennet-treated milk after incubation for 1 h at 31°C increased significantly on addition of NaCas at ≥0.4% (wt/wt). Heat stability in the pH range 6.7 to 7.2 and ethanol stability at pH 6.4 were enhanced by the addition of NaCas. It is suggested that the negative effect of NaCas on rennet gelation is due to the increase in nonsedimentable casein, which upon hydrolysis by chymosin forms into small nonsedimentable particles that physically come between, and impede the aggregation of, rennet-altered para-casein micelles, and thereby inhibit the development of a gel network. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Delay-slope-dependent stability results of recurrent neural networks.

Science.gov (United States)

Li, Tao; Zheng, Wei Xing; Lin, Chong

2011-12-01

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

Local stability and Hopf bifurcation in small-world delayed networks

International Nuclear Information System (INIS)

Li Chunguang; Chen Guanrong

2004-01-01

The notion of small-world networks, recently introduced by Watts and Strogatz, has attracted increasing interest in studying the interesting properties of complex networks. Notice that, a signal or influence travelling on a small-world network often is associated with time-delay features, which are very common in biological and physical networks. Also, the interactions within nodes in a small-world network are often nonlinear. In this paper, we consider a small-world networks model with nonlinear interactions and time delays, which was recently considered by Yang. By choosing the nonlinear interaction strength as a bifurcation parameter, we prove that Hopf bifurcation occurs. We determine the stability of the bifurcating periodic solutions and the direction of the Hopf bifurcation by applying the normal form theory and the center manifold theorem. Finally, we show a numerical example to verify the theoretical analysis

Local stability and Hopf bifurcation in small-world delayed networks

Energy Technology Data Exchange (ETDEWEB)

Li Chunguang E-mail: cgli@uestc.edu.cn; Chen Guanrong E-mail: gchen@ee.cityu.edu.hk

2004-04-01

The notion of small-world networks, recently introduced by Watts and Strogatz, has attracted increasing interest in studying the interesting properties of complex networks. Notice that, a signal or influence travelling on a small-world network often is associated with time-delay features, which are very common in biological and physical networks. Also, the interactions within nodes in a small-world network are often nonlinear. In this paper, we consider a small-world networks model with nonlinear interactions and time delays, which was recently considered by Yang. By choosing the nonlinear interaction strength as a bifurcation parameter, we prove that Hopf bifurcation occurs. We determine the stability of the bifurcating periodic solutions and the direction of the Hopf bifurcation by applying the normal form theory and the center manifold theorem. Finally, we show a numerical example to verify the theoretical analysis.

Hydraulic Stability of Heat Networks for Connection of New Consumers

Science.gov (United States)

Seminenko, A. S.; Sheremet, E. O.; Gushchin, S. V.; Elistratova, J. V.; Kireev, V. M.

2018-03-01

Nowadays due to intensive urban construction, there is a need to connect new consumers to existing heating networks. Often the connection of new consumers leads to a hydraulic misalignment of the network, which in turn affects supplying existing consumers with heat. In order to minimize the possibility of misalignment, appropriate recommendations are needed that can be obtained during the research. In the article, the authors carried out a required experiment aimed at revealing the influence of the new consumers’ connection on the hydraulic stability of the entire network. The result of the research is relevant recommendations that will be useful for engineering specialists both for the design of new networks and the reconstruction of the old ones.

New exponential stability criteria for stochastic BAM neural networks with impulses

International Nuclear Information System (INIS)

Sakthivel, R; Samidurai, R; Anthoni, S M

2010-01-01

In this paper, we study the global exponential stability of time-delayed stochastic bidirectional associative memory neural networks with impulses and Markovian jumping parameters. A generalized activation function is considered, and traditional assumptions on the boundedness, monotony and differentiability of activation functions are removed. We obtain a new set of sufficient conditions in terms of linear matrix inequalities, which ensures the global exponential stability of the unique equilibrium point for stochastic BAM neural networks with impulses. The Lyapunov function method with the Ito differential rule is employed for achieving the required result. Moreover, a numerical example is provided to show that the proposed result improves the allowable upper bound of delays over some existing results in the literature.

New exponential stability criteria for stochastic BAM neural networks with impulses

Science.gov (United States)

Sakthivel, R.; Samidurai, R.; Anthoni, S. M.

2010-10-01

In this paper, we study the global exponential stability of time-delayed stochastic bidirectional associative memory neural networks with impulses and Markovian jumping parameters. A generalized activation function is considered, and traditional assumptions on the boundedness, monotony and differentiability of activation functions are removed. We obtain a new set of sufficient conditions in terms of linear matrix inequalities, which ensures the global exponential stability of the unique equilibrium point for stochastic BAM neural networks with impulses. The Lyapunov function method with the Itô differential rule is employed for achieving the required result. Moreover, a numerical example is provided to show that the proposed result improves the allowable upper bound of delays over some existing results in the literature.

Robust stability bounds for multi-delay networked control systems

Science.gov (United States)

Seitz, Timothy; Yedavalli, Rama K.; Behbahani, Alireza

2018-04-01

In this paper, the robust stability of a perturbed linear continuous-time system is examined when controlled using a sampled-data networked control system (NCS) framework. Three new robust stability bounds on the time-invariant perturbations to the original continuous-time plant matrix are presented guaranteeing stability for the corresponding discrete closed-loop augmented delay-free system (ADFS) with multiple time-varying sensor and actuator delays. The bounds are differentiated from previous work by accounting for the sampled-data nature of the NCS and for separate communication delays for each sensor and actuator, not a single delay. Therefore, this paper expands the knowledge base in multiple inputs multiple outputs (MIMO) sampled-data time delay systems. Bounds are presented for unstructured, semi-structured, and structured perturbations.

Stability analysis of delayed genetic regulatory networks with stochastic disturbances

Energy Technology Data Exchange (ETDEWEB)

Zhou Qi, E-mail: zhouqilhy@yahoo.com.c [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Xu Shengyuan [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China); Chen Bing [Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong (China); Li Hongyi [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)

2009-10-05

This Letter considers the problem of stability analysis of a class of delayed genetic regulatory networks with stochastic disturbances. The delays are assumed to be time-varying and bounded. By utilizing Ito's differential formula and Lyapunov-Krasovskii functionals, delay-range-dependent and rate-dependent (rate-independent) stability criteria are proposed in terms of linear matrices inequalities. An important feature of the proposed results is that all the stability conditions are dependent on the upper and lower bounds of the delays. Another important feature is that the obtained stability conditions are less conservative than certain existing ones in the literature due to introducing some appropriate free-weighting matrices. A simulation example is employed to illustrate the applicability and effectiveness of the proposed methods.

Improved asymptotic stability analysis for uncertain delayed state neural networks

International Nuclear Information System (INIS)

Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.

2009-01-01

This paper presents a new linear matrix inequality (LMI) based approach to the stability analysis of artificial neural networks (ANN) subject to time-delay and polytope-bounded uncertainties in the parameters. The main objective is to propose a less conservative condition to the stability analysis using the Gu's discretized Lyapunov-Krasovskii functional theory and an alternative strategy to introduce slack matrices. Two computer simulations examples are performed to support the theoretical predictions. Particularly, in the first example, the Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability. The second example is presented to illustrate how the proposed approach can provide better stability performance when compared to other ones in the literature

Dynamic stability analysis of fractional order leaky integrator echo state neural networks

Science.gov (United States)

Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Tenreiro Machado, J. A.

2017-06-01

The Leaky integrator echo state neural network (Leaky-ESN) is an improved model of the recurrent neural network (RNN) and adopts an interconnected recurrent grid of processing neurons. This paper presents a new proof for the convergence of a Lyapunov candidate function to zero when time tends to infinity by means of the Caputo fractional derivative with order lying in the range (0, 1). The stability of Fractional-Order Leaky-ESN (FO Leaky-ESN) is then analyzed, and the existence, uniqueness and stability of the equilibrium point are provided. A numerical example demonstrates the feasibility of the proposed method.

Exponential stability of delayed recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Wang Zidong; Liu Yurong; Yu Li; Liu Xiaohui

2006-01-01

In this Letter, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions

Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

International Nuclear Information System (INIS)

Choobbasti, A J; Farrokhzad, F; Barari, A

2009-01-01

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity. (author)

Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

International Nuclear Information System (INIS)

Lou, X.; Cui, B.

2008-01-01

In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

Organic additives stabilize RNA aptamer binding of malachite green.

Science.gov (United States)

Zhou, Yubin; Chi, Hong; Wu, Yuanyuan; Marks, Robert S; Steele, Terry W J

2016-11-01

Aptamer-ligand binding has been utilized for biological applications due to its specific binding and synthetic nature. However, the applications will be limited if the binding or the ligand is unstable. Malachite green aptamer (MGA) and its labile ligand malachite green (MG) were found to have increasing apparent dissociation constants (Kd) as determined through the first order rate loss of emission intensity of the MGA-MG fluorescent complex. The fluorescent intensity loss was hypothesized to be from the hydrolysis of MG into malachite green carbinol base (MGOH). Random screening organic additives were found to reduce or retain the fluorescence emission and the calculated apparent Kd of MGA-MG binding. The protective effect became more apparent as the percentage of organic additives increased up to 10% v/v. The mechanism behind the organic additive protective effects was primarily from a ~5X increase in first order rate kinetics of MGOH→MG (kMGOH→MG), which significantly changed the equilibrium constant (Keq), favoring the generation of MG, versus MGOH without organic additives. A simple way has been developed to stabilize the apparent Kd of MGA-MG binding over 24h, which may be beneficial in stabilizing other triphenylmethane or carbocation ligand-aptamer interactions that are susceptible to SN1 hydrolysis. Copyright © 2016 Elsevier B.V. All rights reserved.

A hardness result for core stability in additive hedonic games

NARCIS (Netherlands)

Woeginger, G.J.

2013-01-01

We investigate the computational complexity of a decision problem in hedonic coalition formation games. We prove that core stability in additive hedonic games is complete for the second level of the polynomial hierarchy.

Network stability is a balancing act of personality, power, and conflict dynamics in rhesus macaque societies.

Directory of Open Access Journals (Sweden)

Brenda McCowan

Full Text Available Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex and external factors (e.g., rank dynamics, sex ratio were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups.

Network stability is a balancing act of personality, power, and conflict dynamics in rhesus macaque societies.

Science.gov (United States)

McCowan, Brenda; Beisner, Brianne A; Capitanio, John P; Jackson, Megan E; Cameron, Ashley N; Seil, Shannon; Atwill, Edward R; Fushing, Hsieh

2011-01-01

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups.

Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

International Nuclear Information System (INIS)

Sahoo, N.C.; Prasad, K.

2006-01-01

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration

Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Tran Dinh Hieu

2016-05-01

Full Text Available A new generation of wireless sensor networks that harvest energy from environmental sources such as solar, vibration, and thermoelectric to power sensor nodes is emerging to solve the problem of energy limitation. Based on the photo-voltaic model, this research proposes a stability-aware geographic routing for reliable data transmissions in energy-harvesting wireless sensor networks (EH-WSNs to provide a reliable routes selection method and potentially achieve an unlimited network lifetime. Specifically, the influences of link quality, represented by the estimated packet reception rate, on network performance is investigated. Simulation results show that the proposed method outperforms an energy-harvesting-aware method in terms of energy consumption, the average number of hops, and the packet delivery ratio.

Interconnection and transportation networks: adjustments and stability; Reseaux d'interconnexion et de transport: reglages et stabilite

Energy Technology Data Exchange (ETDEWEB)

Bornard, P. [Reseau de Transport d' Electricite (RTE), Div. Systeme Electrique, 92 - Paris la Defense (France); Pavard, M. [Electricite de France (EDF), 75 - Paris (France); Testud, G. [Reseau de Transport d' Electricite (RTE), Dept. Exploitation du Systeme Electrique, 92 - Paris la Defense (France)

2005-10-01

Keeping the mastery of the safety of a power transportation system and respecting the contractual commitments with respect to the network users implies the implementation of efficient frequency and voltage adjustment systems. This article presents a synthetic overview of the methods and means implemented to ensure the adjustment of the voltage and frequency and the stability of very-high voltage power transportation networks: 1 - recalls of the general problem; 2 - frequency and active power adjustment: adapting generation to consumption, adapting consumption to generation; 3 - voltage and reactive power adjustment: duality of the voltage-reactive compensation adjustment, compensation of the reactive power, voltage adjustment chain, voltage adjustment of very high voltage networks, collapse of the voltage plan; 4 - alternators stability: static stability, transient stability, numerical simulation methods, stability improvement; 5 - conclusion. (J.S.)

Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties.

Science.gov (United States)

Song, Qiankun; Yu, Qinqin; Zhao, Zhenjiang; Liu, Yurong; Alsaadi, Fuad E

2018-07-01

In this paper, the boundedness and robust stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust stability of equilibrium point is derived for the considered uncertain neural networks. The obtained robust stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

Science.gov (United States)

Yang, Shiju; Li, Chuandong; Huang, Tingwen

2016-03-01

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Global exponential stability of BAM neural networks with delays and impulses

International Nuclear Information System (INIS)

Li Yongkun

2005-01-01

Sufficient conditions are obtained for the existence and global exponential stability of a unique equilibrium of a class of two-layer heteroassociative networks called bidirectional associative memory (BAM) networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. An illustrative example is given to demonstrate the effectiveness of the obtained results

Stability results for stochastic delayed recurrent neural networks with discrete and distributed delays

Science.gov (United States)

Chen, Guiling; Li, Dingshi; Shi, Lin; van Gaans, Onno; Verduyn Lunel, Sjoerd

2018-03-01

We present new conditions for asymptotic stability and exponential stability of a class of stochastic recurrent neural networks with discrete and distributed time varying delays. Our approach is based on the method using fixed point theory, which do not resort to any Liapunov function or Liapunov functional. Our results neither require the boundedness, monotonicity and differentiability of the activation functions nor differentiability of the time varying delays. In particular, a class of neural networks without stochastic perturbations is also considered. Examples are given to illustrate our main results.

Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays.

Science.gov (United States)

Arik, Sabri

2005-05-01

This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all continuous nonmonotonic neuron activation functions. It is shown that in some special cases of the results, the stability criteria can be easily checked. Some examples are also given to compare the results with the previous results derived in the literature.

Global Stability of Complex-Valued Genetic Regulatory Networks with Delays on Time Scales

Directory of Open Access Journals (Sweden)

Wang Yajing

2016-01-01

Full Text Available In this paper, the global exponential stability of complex-valued genetic regulatory networks with delays is investigated. Besides presenting conditions guaranteeing the existence of a unique equilibrium pattern, its global exponential stability is discussed. Some numerical examples for different time scales.

Effects of time delays on stability and Hopf bifurcation in a fractional ring-structured network with arbitrary neurons

Science.gov (United States)

Huang, Chengdai; Cao, Jinde; Xiao, Min; Alsaedi, Ahmed; Hayat, Tasawar

2018-04-01

This paper is comprehensively concerned with the dynamics of a class of high-dimension fractional ring-structured neural networks with multiple time delays. Based on the associated characteristic equation, the sum of time delays is regarded as the bifurcation parameter, and some explicit conditions for describing delay-dependent stability and emergence of Hopf bifurcation of such networks are derived. It reveals that the stability and bifurcation heavily relies on the sum of time delays for the proposed networks, and the stability performance of such networks can be markedly improved by selecting carefully the sum of time delays. Moreover, it is further displayed that both the order and the number of neurons can extremely influence the stability and bifurcation of such networks. The obtained criteria enormously generalize and improve the existing work. Finally, numerical examples are presented to verify the efficiency of the theoretical results.

Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen-Grossberg BAM neural networks with impulses.

Science.gov (United States)

Yang, Wengui; Yu, Wenwu; Cao, Jinde; Alsaadi, Fuad E; Hayat, Tasawar

2018-02-01

This paper investigates the stability and lag synchronization for memristor-based fuzzy Cohen-Grossberg bidirectional associative memory (BAM) neural networks with mixed delays (asynchronous time delays and continuously distributed delays) and impulses. By applying the inequality analysis technique, homeomorphism theory and some suitable Lyapunov-Krasovskii functionals, some new sufficient conditions for the uniqueness and global exponential stability of equilibrium point are established. Furthermore, we obtain several sufficient criteria concerning globally exponential lag synchronization for the proposed system based on the framework of Filippov solution, differential inclusion theory and control theory. In addition, some examples with numerical simulations are given to illustrate the feasibility and validity of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

Energy Technology Data Exchange (ETDEWEB)

Sahoo, N.C. [Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia); Prasad, K. [Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia)

2006-11-15

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration. (author)

Diagnostic of Gravitropism-like Stabilizer of Inspection Drone Using Neural Networks

Science.gov (United States)

Kruglova, Tatyana; Sayfeddine, Daher; Bulgakov, Alexey

2018-03-01

This paper discusses the enhancement of flight stability of using an inspection drone to scan the condition of buildings on low and high altitude. Due to aerial perturbations and wakes, the drone starts to shake and may be damaged. One of the mechanical optimization methods it so add a built-in stabilizing mechanism. However, the performance of this supporting device becomes critical on certain flying heights, thus to avoid losing the drone. The paper is divided in two parts: the description of the gravitropism-like stabilizer and the diagnostic of its status using wavelet transformation and neural network classification.

Asymptotic stability and disturbance attenuation properties for a class of networked control systems

Institute of Scientific and Technical Information of China (English)

无

2006-01-01

In this paper, stability and disturbance attenuation issues for a class of Networked Control Systems (NCSs)under uncertain access delay and packet dropout effects are considered. Our aim is to find conditions on the delay and packet dropout rate, under which the system stability and H∞ disturbance attenuation properties are preserved to a desired level. The basic idea in this paper is to formulate such Networked Control System as a discrete-time switched system. Then the NCSs' stability and performance problems can be reduced to the corresponding problems for switched systems, which have been studied for decades and for which a number of results are available in the literature. The techniques in this paper are based on recent progress in the discrete-time switched systems and piecewise Lyapunov functions.

Automotive mechatronics automotive networking, driving stability systems, electronics

CERN Document Server

2015-01-01

As the complexity of automotive vehicles increases this book presents operational and practical issues of automotive mechatronics. It is a comprehensive introduction to controlled automotive systems and provides detailed information of sensors for travel, angle, engine speed, vehicle speed, acceleration, pressure, temperature, flow, gas concentration etc. The measurement principles of the different sensor groups are explained and examples to show the measurement principles applied in different types. Contents Basics of mechatronics.- Architecture.- Electronic control unit.- Software development.- Basic principles of networking.- Automotive networking.- Bus systems.- Automotive sensors.- Sensor measuring principles.- Sensor types.- Electric actuators.- Electrohydraulic actuators.- Electronic transmission control.- Electronic transmission control unit.- Modules for transmission control.- Antilock braking system.- Traction control system.- Electronic stability program.- Automatic brake functions.- Hydraulic modu...

Effect of different phytogenic additives on oxidation stability of chicken meat

Directory of Open Access Journals (Sweden)

Marek Bobko

2016-05-01

Full Text Available The aim of the study was to evaluate the oxidative stability (TBARS method of breast and thigh muscle after application of feed mixtures enriched by phytogenic additives. The experiment started with 150 pieces one-day-old chicks of Cobb 500 hybrid combination. They were divided into one control (C and two experimental groups (1st EG and 2nd EG. Each group included 50 chicks. In experimental groups, feed additives were applied as followed: 100 mg.kg-1 Agolin Poultry (in the 1st EG and 500 mg.kg-1 Agolin Tannin Plus (in the 2nd EG. Experimental broiler chickens were fed during 42 days by ad libitum. Chicken meat samples of breast and thigh muscle were analysed in the 1st day, 1st, 2nd, 3rd, 4th, 5th and 6th month of storage in frozen storage at -18 °C. We recorded positive influence on chicken meat oxidative stability in all experimental groups with application of phytogenic feed additives. Obtained results showed that applied phytogenic additives had positive influence on oxidative stability of breast and thigh muscles. At the end of frozen storage (in 6th month, we found higher malondialdehyde (MDA values and lower oxidative stability (p <0.05 of breast muscle in control group (0.167 mg.kg-1 compared to experimental groups (from 0.150 mg.kg-1 in 1. EG to 0.155 mg.kg-1 in 2. EG. In the thigh muscle, we found similar tendency of oxidative changes as in the breast muscle. At the end of frozen storage (in the 6th month, MDA average values of thigh muscle were higher (p <0.05 in control group (0.181 mg.kg-1 compared to experimental groups (1. EG 0.164 mg.kg-1 and 2. EG 0.169 mg.kg-1. Significant differences (p <0.05 between the control and experimental groups were found from the 5th month of storage in thigh and breast muscle. Obtained results indicate positive influence of phytogenic additives applied in chicken nutrition, namely on stabilization of fatty substance to degradation processes. Normal 0 21 false false false SK X-NONE X-NONE Normal 0

Network Physics - the only company to provide physics-based network management - secures additional funding and new executives

CERN Multimedia

2003-01-01

"Network Physics, the only provider of physics-based network management products, today announced an additional venture round of $6 million in funding, as well as the addition of David Jones as president and CEO and Tom Dunn as vice president of sales and business development" (1 page).

Improved result on stability analysis of discrete stochastic neural networks with time delay

International Nuclear Information System (INIS)

Wu Zhengguang; Su Hongye; Chu Jian; Zhou Wuneng

2009-01-01

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.

Improved Robust Stability Criterion of Networked Control Systems with Transmission Delays and Packet Loss

Directory of Open Access Journals (Sweden)

Shenping Xiao

2014-01-01

Full Text Available The problem of stability analysis for a class of networked control systems (NCSs with network-induced delay and packet dropout is investigated in this paper. Based on the working mechanism of zero-order holder, the closed-loop NCS is modeled as a continuous-time linear system with input delay. By introducing a novel Lyapunov-Krasovskii functional which splits both the lower and upper bounds of the delay into two subintervals, respectively, and utilizes reciprocally convex combination technique, a new stability criterion is derived in terms of linear matrix inequalities. Compared with previous results in the literature, the obtained stability criterion is less conservative. Numerical examples demonstrate the validity and feasibility of the proposed method.

On the stability of surface-confined nanoporous molecular networks

Energy Technology Data Exchange (ETDEWEB)

Ghijsens, Elke; Adisoejoso, Jinne, E-mail: Jinne.adisoejoso@chem.kuleuven.be, E-mail: tobe@chem.es.osaka-u.ac.jp, E-mail: Steven.DeFeyter@chem.kuleuven.be; Van Gorp, Hans; Destoop, Iris; Ivasenko, Oleksandr; Van der Auweraer, Mark; De Feyter, Steven, E-mail: Jinne.adisoejoso@chem.kuleuven.be, E-mail: tobe@chem.es.osaka-u.ac.jp, E-mail: Steven.DeFeyter@chem.kuleuven.be [Department of Chemistry, Division of Molecular Imaging and Photonics, KU Leuven—University of Leuven, Celestijnenlaan 200 F, B-3001 Leuven (Belgium); Noguchi, Aya; Tahara, Kazukuni; Tobe, Yoshito, E-mail: Jinne.adisoejoso@chem.kuleuven.be, E-mail: tobe@chem.es.osaka-u.ac.jp, E-mail: Steven.DeFeyter@chem.kuleuven.be [Graduate School of Engineering Science, Division of Frontier Materials Science, Osaka University, Toyonaka, Osaka 560-8531 (Japan)

2015-03-14

Self-assembly of molecular building blocks into two-dimensional nanoporous networks has been a topic of broad interest for many years. However, various factors govern the specific outcome of the self-assembly process, and understanding and controlling these are key to successful creation. In this work, the self-assembly of two alkylated dehydrobenzo[12]annulene building blocks was compared at the liquid-solid interface. It turned out that only a small chemical modification within the building blocks resulted in enhanced domain sizes and stability of the porous packing relative to the dense linear packing. Applying a thermodynamic model for phase transition revealed some key aspects for network formation.

Enhancing the crumb rubber modified asphalt’s storage stability through the control of its internal network structure

Directory of Open Access Journals (Sweden)

Mohyeldin Ragab

2018-01-01

Full Text Available The current research investigated the effect of the internal network structure developed in the crumb rubber modified asphalt (CRMA on its storage stability. The authors investigated the influence of asphalt-crumb rubber modifier (CRM interaction parameters (interaction time, interaction speed, and interaction temperature on the development of the internal network structure in CRMA. The authors found that the existence of three dimensional (3D network structures in the CRMA enhanced its storage stability. Fourier Transform Infrared (FTIR Spectroscopy was utilized to determine the nature of CRM components responsible for the development of 3D network structure in the liquid phase of CRMA. This was achieved by monitoring the changes of the IR distinctive peaks in the CRMA liquid phase. Dissolution tests and thermo gravimetric analysis (TGA were carried out on the extracted CRM after interaction with asphalt to determine the role of CRM dissolved amounts and released components on the development of 3D network structure in CRMA. The asphalt-CRM interaction parameters were found to be essential to induce the formation of the 3D network structure within the liquid phase of the CRMA through controlling the swelling, dissolution and release of CRM components into the asphalt liquid phase. The existence of 3D network structure in the CRMA had determinant impact on the enhancement of its storage stability. Keywords: Storage stability, Three dimensional (3D network, Crumb rubber modified asphalt

Non-Archimedean Hyers-Ulam Stability of an Additive-Quadratic Mapping

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Hassan Azadi Kenary

2012-01-01

Full Text Available Using fixed point method and direct method, we prove the Hyers-Ulam stability of the following additive-quadratic functional equation 2((++/+2((−+/+2((+−/+2((−++/=4(+4(+4(, where is a positive real number, in non-Archimedean normed spaces.

Robust stability of interval bidirectional associative memory neural network with time delays.

Science.gov (United States)

Liao, Xiaofeng; Wong, Kwok-wo

2004-04-01

In this paper, the conventional bidirectional associative memory (BAM) neural network with signal transmission delay is intervalized in order to study the bounded effect of deviations in network parameters and external perturbations. The resultant model is referred to as a novel interval dynamic BAM (IDBAM) model. By combining a number of different Lyapunov functionals with the Razumikhin technique, some sufficient conditions for the existence of unique equilibrium and robust stability are derived. These results are fairly general and can be verified easily. To go further, we extend our investigation to the time-varying delay case. Some robust stability criteria for BAM with perturbations of time-varying delays are derived. Besides, our approach for the analysis allows us to consider several different types of activation functions, including piecewise linear sigmoids with bounded activations as well as the usual C1-smooth sigmoids. We believe that the results obtained have leading significance in the design and application of BAM neural networks.

Existence and exponential stability of almost periodic solution for stochastic cellular neural networks with delay

International Nuclear Information System (INIS)

Huang Zaitang; Yang Qigui

2009-01-01

The paper considers the problems of existence of quadratic mean almost periodic and global exponential stability for stochastic cellular neural networks with delays. By employing the Holder's inequality and fixed points principle, we present some new criteria ensuring existence and uniqueness of a quadratic mean almost periodic and global exponential stability. These criteria are important in signal processing and the design of networks. Moreover, these criteria are also applied in others stochastic biological neural systems.

pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

Science.gov (United States)

Wang, Fen; Chen, Yuanlong; Liu, Meichun

2018-02-01

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays.

Science.gov (United States)

Zhang, Zhengqiu; Liu, Wenbin; Zhou, Dongming

2012-01-01

In this paper, we first discuss the existence of a unique equilibrium point of a generalized Cohen-Grossberg BAM neural networks of neutral type delays by means of the Homeomorphism theory and inequality technique. Then, by applying the existence result of an equilibrium point and constructing a Lyapunov functional, we study the global asymptotic stability of the equilibrium solution to the above Cohen-Grossberg BAM neural networks of neutral type. In our results, the hypothesis for boundedness in the existing paper, which discussed Cohen-Grossberg neural networks of neutral type on the activation functions, are removed. Finally, we give an example to demonstrate the validity of our global asymptotic stability result for the above neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo

2008-01-01

In this paper, the global exponential stability and periodicity for a class of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are addressed by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential converge to 0 of the difference between any two solutions of the original reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Furthermore, we prove periodicity of the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Sufficient conditions ensuring the global exponential stability and the existence of periodic oscillatory solutions for the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are given. These conditions are easy to check and have important leading significance in the design and application of reaction-diffusion recurrent neural networks with delays. Finally, two numerical examples are given to show the effectiveness of the obtained results

Boundedness and stability for recurrent neural networks with variable coefficients and time-varying delays

International Nuclear Information System (INIS)

Liang Jinling; Cao Jinde

2003-01-01

In this Letter, the problems of boundedness and stability for a general class of non-autonomous recurrent neural networks with variable coefficients and time-varying delays are analyzed via employing Young inequality technique and Lyapunov method. Some simple sufficient conditions are given for boundedness and stability of the solutions for the recurrent neural networks. These results generalize and improve the previous works, and they are easy to check and apply in practice. Two illustrative examples and their numerical simulations are also given to demonstrate the effectiveness of the proposed results

Robust stabilization of burn conditions in subignited fusion reactors using artificial neural networks

International Nuclear Information System (INIS)

Vitela, E. Javier; Martinell, J. Julio

2000-01-01

In this work it is shown that robust burn control in long pulse operations of thermonuclear reactors can be successfully achieved with artificial neural networks. The results reported here correspond to a volume averaged zero-dimensional nonlinear model of a subignited fusion device using the design parameters of the tokamak EDA-ITER group. A Radial Basis Neural Network (RBNN) was trained to provide feedback stabilization at a fixed operating point independently of any particular scaling law that the reactor confinement time may follow. A numerically simulated transient is used to illustrate the stabilization capabilities of the resulting RBNN when the reactor follows an ELMy scaling law corrupted with Gaussian noise. (author)

On global stability criterion for neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Park, Ju H.

2006-01-01

Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new delay-dependent criterion for neural networks with discrete and distributed delays is derived to guarantee global asymptotic stability. The criterion is expressed in terms of LMIs, which can be solved easily by various convex optimization algorithms. Some numerical examples are given to show the effectiveness of proposed method

Time development in the early history of social networks: link stabilization, group dynamics, and segregation.

Science.gov (United States)

Bruun, Jesper; Bearden, Ian G

2014-01-01

Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks' communities show that while student movement between groups are erratic in the beginning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately

Addition of Fish Oil to Cream Cheese Affects Lipid Oxidation, Sensory Stability and Microstructure

Directory of Open Access Journals (Sweden)

Andy Horsewell

2012-11-01

Full Text Available The objective of this study was to investigate the differences in the oxidative stability during storage of fish oil enriched cream cheeses when fish oil was added either as neat oil or pre-emulsified oil with sodium caseinate, whey protein isolate, or a combination of milk proteins and phospholipids as emulsifier. Results showed that the addition of fish oil decreased the oxidative stability of cream cheeses regardless of the addition method, especially when the cheese was stored longer than five weeks. The oxidative stability of fish oil enriched cream cheeses was highest when fish oil was added as neat oil or in a delivery emulsion prepared with a combination of milk proteins and phospholipids. Adding the fish oil in a delivery emulsion prepared with whey protein or caseinate resulted in a less oxidative stable product. It was furthermore shown that the microstructure of the cream cheeses was affected by fish oil addition, and it was suggested that the change in microstructure was partly responsible for the oxidative stability of the cream cheeses.

Global exponential stability of BAM neural networks with time-varying delays: The discrete-time case

Science.gov (United States)

Raja, R.; Marshal Anthoni, S.

2011-02-01

This paper deals with the problem of stability analysis for a class of discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient conditions is proposed for the global exponential stability of discrete-time BAM neural networks. The proposed LMI based results can be easily checked by LMI control toolbox. Moreover, an example is also provided to demonstrate the effectiveness of the proposed method.

Insulin analog with additional disulfide bond has increased stability and preserved activity

DEFF Research Database (Denmark)

Vinther, Tine N.; Norrman, Mathias; Ribel, Ulla

2013-01-01

Insulin is a key hormone controlling glucose homeostasis. All known vertebrate insulin analogs have a classical structure with three 100% conserved disulfide bonds that are essential for structural stability and thus the function of insulin. It might be hypothesized that an additional disulfide...... bond may enhance insulin structural stability which would be highly desirable in a pharmaceutical use. To address this hypothesis, we designed insulin with an additional interchain disulfide bond in positions A10/B4 based on Cα-Cα distances, solvent exposure, and side-chain orientation in human insulin...... (HI) structure. This insulin analog had increased affinity for the insulin receptor and apparently augmented glucodynamic potency in a normal rat model compared with HI. Addition of the disulfide bond also resulted in a 34.6°C increase in melting temperature and prevented insulin fibril formation...

The Lyapunov-Krasovskii theorem and a sufficient criterion for local stability of isochronal synchronization in networks of delay-coupled oscillators

Science.gov (United States)

Grzybowski, J. M. V.; Macau, E. E. N.; Yoneyama, T.

2017-05-01

This paper presents a self-contained framework for the stability assessment of isochronal synchronization in networks of chaotic and limit-cycle oscillators. The results were based on the Lyapunov-Krasovskii theorem and they establish a sufficient condition for local synchronization stability of as a function of the system and network parameters. With this in mind, a network of mutually delay-coupled oscillators subject to direct self-coupling is considered and then the resulting error equations are block-diagonalized for the purpose of studying their stability. These error equations are evaluated by means of analytical stability results derived from the Lyapunov-Krasovskii theorem. The proposed approach is shown to be a feasible option for the investigation of local stability of isochronal synchronization for a variety of oscillators coupled through linear functions of the state variables under a given undirected graph structure. This ultimately permits the systematic identification of stability regions within the high-dimensionality of the network parameter space. Examples of applications of the results to a number of networks of delay-coupled chaotic and limit-cycle oscillators are provided, such as Lorenz, Rössler, Cubic Chua's circuit, Van der Pol oscillator and the Hindmarsh-Rose neuron.

Improved Criteria on Delay-Dependent Stability for Discrete-Time Neural Networks with Interval Time-Varying Delays

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O. M. Kwon

2012-01-01

Full Text Available The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delay-dependent criteria for the stability of the networks are derived in terms of linear matrix inequalities (LMIs by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method.

Simulation of longitudinal differential protection of transmission lines with additional stabilization and APU system

Directory of Open Access Journals (Sweden)

Rajić Tomislav D.

2016-01-01

Full Text Available This paper explains the algorithm for the longitudinal differential protection of transmission lines with automatic reclosing. Classic stabilization is not sufficient for avoiding of unnecessary operations caused by saturation of current transformer. This problem can occur during the fault plased outside of the protected zone of the transmission line. It is shown how unnecessary operation can occur during the outside fault whithout using of additional stabilization. The different types of faults were simulated and comparison of relay operations with and without additional stabilization is presented. The simulations were performed on the three-phase model of the transmission line formed by using of MATLAB/Simulink program.

Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks

Science.gov (United States)

Csermely, Peter

Why do women stabilize our societies? Why can we enjoy and understand Shakespeare? Why are fruitflies uniform? Why do omnivorous eating habits aid our survival? Why is Mona Lisa's smile beautiful? -- Is there any answer to these questions? This book shows that the statement: "weak links stabilize complex systems" holds the answers to all of the surprising questions above. The author (recipientof several distinguished science communication prizes) uses weak (low affinity, low probability) interactions as a thread to introduce a vast varietyof networks from proteins to ecosystems.

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

Science.gov (United States)

Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

2009-01-01

In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

2008-01-01

In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

Effects of additives on the stability of electrolytes for all-vanadium redox flow batteries

International Nuclear Information System (INIS)

Zhang, Jianlu; Li, Liyu; Nie, Zimin; Chen, Baowei; Vijayakumar, M.; Kim, Soowhan; Wang, Wei; Schwenzer, Birgit; Liu, Jun; Yang, Zhenguo

2011-01-01

The stability of the electrolytes for all-vanadium redox flow battery was investigated with ex-situ heating/cooling treatment and in-situ flow-battery testing methods. The effects of inorganic and organic additives have been studied. The additives containing the ions of potassium, phosphate, and polyphosphate are not suitable stabilizing agents because of their reactions with V(V) ions, forming precipitates of KVSO6 or VOPO4. Of the chemicals studied, polyacrylic acid and its mixture with CH3SO3H are the most promising stabilizing candidates which can stabilize all the four vanadium ions (V2+, V3+, VO2+, and VO2+) in electrolyte solutions up to 1.8 M. However, further effort is needed to obtain a stable electrolyte solution with >1.8 M V5+ at temperatures higher than 40 C.

Finite-time stability of neutral-type neural networks with random time-varying delays

Science.gov (United States)

Ali, M. Syed; Saravanan, S.; Zhu, Quanxin

2017-11-01

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

Ab initio study of effects of substitutional additives on the phase stability of γ-alumina

International Nuclear Information System (INIS)

Jiang Kaiyun; Music, Denis; Sarakinos, Kostas; Schneider, Jochen M

2010-01-01

Using ab initio calculations, we have evaluated two structural descriptions of γ-Al 2 O 3 , spinel and tetragonal hausmannite, and explored the relative stability of γ-Al 2 O 3 with respect to α-Al 2 O 3 with 2.5 at.% of Si, Cr, Ti, Sc, and Y additives to identify alloying element induced electronic structure changes that impede the γ to α transition. The total energy calculations indicate that Si stabilizes γ-Al 2 O 3 , while Cr stabilizes α-Al 2 O 3 . As Si is added, a bond length increase in α-Al 2 O 3 is observed, while strong and short Si-O bonds are formed in γ-Al 2 O 3 , consequently stabilizing this phase. On the other hand, Cr additions induce a smaller bond length increase in α-Al 2 O 3 than in γ-Al 2 O 3 , therefore stabilizing the α-phase. The bulk moduli of γ-Al 2 O 3 with these additives show no significant changes. The phase stability and elastic property data discussed here underline the application potential of Si alloyed γ-Al 2 O 3 for applications at elevated temperatures. Furthermore it is evident that the tetragonal hausmannite structure is a suitable description for γ-Al 2 O 3 .

OXIDATIVE STABILITY OF CHICKEN MEAT AFTER APPLICATION PHYTOGENIC ADDITIVES IN THEIR DIET

Directory of Open Access Journals (Sweden)

Marek Bobko

2015-02-01

Full Text Available The aim of the study was to evaluate the oxidative stability (TBARS method of breast and thigh muscle after application of feed mixtures enriched by phytogenic additives. The experiment started with 250 pieces one-day-old chicks of Cobb 500 hybrid combination. They were divided into one control (C and four experimental groups (1st EG, 2nd EG, 3rd EG, 4th EG. Each group included 50 chicks. In experimental groups, feed additives were applied as followed: 100 mg kg-1 Agolin Poultry (in the 1st EG, 500 mg kg-1 Agolin Tannin Plus (in the 2nd EG, 1000 mg kg-1 Biostrong 510 + FortiBac (in the 3rd EG and 1000 mg kg-1 Agolin Acid (in the 4th EG. We recorded positive influence on chicken meat oxidative stability in all experimental groups with application of plant feed additives. Experimental broiler chickens were fed during 42 days by ad libitum. Chicken meat samples of breast and thigh muscle were analyzed in the 1st, 3rd, 5th and 7th day of storage in cold conditions at 4 °C. Obtained results showed that applied phytogenic additives had positive influence on oxidative stability of breast and thigh muscles. At the end of cold store (in 7th day, we found higher malondialdehyde (MDA values and lower oxidative stability (P<0.05 of breast muscle in control group (0.157 mg kg-1 compared to experimental groups (from 0.124 mg kg-1 in the 3rd EG to 0.133 mg kg-1 in the 1st EG. In the thigh muscle, we found similar tendency of oxidative changes as in the breast muscle. At the end of cold store (in the 7th day, MDA average values of thigh muscle were higher (P<0.05 in control group (0.179 mg kg-1 compared to experimental groups (from 0.136 mg kg-1 in the 4th EG to 0.141 mg kg-1 in the 1st EG. Significant differences (P<0.05 between the control and experimental groups were found from the 5th day of storage in thigh muscle in contrast to breast muscle. Obtained results indicate positive influence of phytogenic additives applied in chicken nutrition, namely on

Temporal stability in human interaction networks

Science.gov (United States)

Fabbri, Renato; Fabbri, Ricardo; Antunes, Deborah Christina; Pisani, Marilia Mello; de Oliveira, Osvaldo Novais

2017-11-01

This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.

Nash Stability in Additively Separable Hedonic Games and Community Structures

DEFF Research Database (Denmark)

Olsen, Martin

2009-01-01

  We prove that the problem of deciding whether a Nash stable   partition exists in an Additively Separable Hedonic Game is   NP-complete. We also show that the problem of deciding whether a   non trivial Nash stable partition exists in an   Additively Separable Hedonic Game with   non......-negative and symmetric   preferences is NP-complete. We motivate our study of the   computational complexity by linking Nash stable partitions in   Additively Separable Hedonic Games to community structures in   networks. Our results formally justify that computing community   structures in general is hard....

Novel stability criteria for uncertain delayed Cohen-Grossberg neural networks using discretized Lyapunov functional

International Nuclear Information System (INIS)

Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.

2009-01-01

This paper deals with the stability analysis of delayed uncertain Cohen-Grossberg neural networks (CGNN). The proposed methodology consists in obtaining new robust stability criteria formulated as linear matrix inequalities (LMIs) via the Lyapunov-Krasovskii theory. Particularly one stability criterion is derived from the selection of a parameter-dependent Lyapunov-Krasovskii functional, which allied with the Gu's discretization technique and a simple strategy that decouples the system matrices from the functional matrices, assures a less conservative stability condition. Two computer simulations are presented to support the improved theoretical results.

Global robust asymptotical stability of multi-delayed interval neural networks: an LMI approach

International Nuclear Information System (INIS)

Li Chuandong; Liao Xiaofeng; Zhang Rong

2004-01-01

Based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria for interval neural networks (IDNN) with multiple time-varying delays are derived to guarantee global robust asymptotic stability. The main results are generalizations of some recent results reported in the literature. Numerical example is also given to show the effectiveness of our results

On exponential stability of bidirectional associative memory neural networks with time-varying delays

International Nuclear Information System (INIS)

Park, Ju H.; Lee, S.M.; Kwon, O.M.

2009-01-01

For bidirectional associate memory neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov functional method and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived. A numerical example is given to demonstrate the effectiveness of the obtained results.

Stabilization of Proteins and Noncovalent Protein Complexes during Electrospray Ionization by Amino Acid Additives.

Science.gov (United States)

Zhang, Hua; Lu, Haiyan; Chingin, Konstantin; Chen, Huanwen

2015-07-21

Ionization of proteins and noncovalent protein complexes with minimal disturbance to their native structure presents a great challenge for biological mass spectrometry (MS). In living organisms, the native structure of intracellular proteins is commonly stabilized by solute amino acids (AAs) accumulated in cells at very high concentrations. Inspired by nature, we hypothesized that AAs could also pose a stabilizing effect on the native structure of proteins and noncovalent protein complexes during ionization. To test this hypothesis, here we explored MS response for various protein complexes upon the addition of free AAs at mM concentrations into the electrospray ionization (ESI) solution. Thermal activation of ESI droplets in the MS inlet capillary was employed as a model destabilizing factor during ionization. Our results indicate that certain AAs, in particular proline (Pro), pose considerable positive effect on the stability of noncovalent protein complexes in ESI-MS without affecting the signal intensity of protein ions and original protein-ligand equilibrium, even when added at the 20 mM concentration. The data suggest that the degree of protein stabilization is primarily determined by the osmolytic and ampholytic characteristics of AA solutes. The highest stability and visibility of noncovalent protein complexes in ESI-MS are achieved using AA additives with neutral isoelectric point, moderate proton affinity, and unfavorable interaction with the native protein state. Overall, our results indicate that the simple addition of free amino acids into the working solution can notably improve the stability and accuracy of protein analysis by native ESI-MS.

Exponential stability of Cohen-Grossberg neural networks with a general class of activation functions

International Nuclear Information System (INIS)

Wan Anhua; Wang Miansen; Peng Jigen; Qiao Hong

2006-01-01

In this Letter, the dynamics of Cohen-Grossberg neural networks model are investigated. The activation functions are only assumed to be Lipschitz continuous, which provide a much wider application domain for neural networks than the previous results. By means of the extended nonlinear measure approach, new and relaxed sufficient conditions for the existence, uniqueness and global exponential stability of equilibrium of the neural networks are obtained. Moreover, an estimate for the exponential convergence rate of the neural networks is precisely characterized. Our results improve those existing ones

Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

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Hongli Liu

2009-01-01

Full Text Available We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.

A 500-600 MHz GaN power amplifier with RC-LC stability network

Science.gov (United States)

Ma, Xinyu; Duan, Baoxing; Yang, Yintang

2017-08-01

A 500-600 MHz high-efficiency, high-power GaN power amplifier is designed and realized on the basis of the push-pull structure. The RC-LC stability network is proposed and applied to the power amplifier circuit for the first time. The RC-LC stability network can significantly reduce the high gain out the band, which eliminates the instability of the power amplifier circuit. The developed power amplifier exhibits 58.5 dBm (700 W) output power with a 17 dB gain and 85% PAE at 500-600 MHz, 300 μs, 20% duty cycle. It has the highest PAE in P-band among the products at home and abroad. Project supported by the National Key Basic Research Program of China (No. 2014CB339901).

Robust stability analysis of uncertain stochastic neural networks with interval time-varying delay

International Nuclear Information System (INIS)

Feng Wei; Yang, Simon X.; Fu Wei; Wu Haixia

2009-01-01

This paper addresses the stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. The parameter uncertainties are assumed to be norm bounded, and the delay factor is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is robustly, globally, asymptotically stable in the mean square. Some stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Finally, numerical examples are provided to demonstrate the usefulness of the proposed criteria.

Sensitivity of directed networks to the addition and pruning of edges and vertices

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Goltsev, A. V.; Timár, G.; Mendes, J. F. F.

2017-08-01

Directed networks have various topologically different extensive components, in contrast to a single giant component in undirected networks. We study the sensitivity (response) of the sizes of these extensive components in directed complex networks to the addition and pruning of edges and vertices. We introduce the susceptibility, which quantifies this sensitivity. We show that topologically different parts of a directed network have different sensitivity to the addition and pruning of edges and vertices and, therefore, they are characterized by different susceptibilities. These susceptibilities diverge at the critical point of the directed percolation transition, signaling the appearance (or disappearance) of the giant strongly connected component in the infinite size limit. We demonstrate this behavior in randomly damaged real and synthetic directed complex networks, such as the World Wide Web, Twitter, the Caenorhabditis elegans neural network, directed Erdős-Rényi graphs, and others. We reveal a nonmonotonic dependence of the sensitivity to random pruning of edges or vertices in the case of C. elegans and Twitter that manifests specific structural peculiarities of these networks. We propose the measurements of the susceptibilities during the addition or pruning of edges and vertices as a new method for studying structural peculiarities of directed networks.

Influence of additive L-phenylalanine on stabilization of metastable α-form of L-glutamic acid in cooling crystallization

Science.gov (United States)

Quang, Khuu Chau; Nhan, Le Thi Hong; Huyen, Trinh Thi Thanh; Tuan, Nguyen Anh

2017-09-01

The influence of additive amino acid L-phenylalanine on stabilization of metastable α-form of L-glutamic acid was investigated in cooling crystallization. The present study found that the additive L-phenylalanine could be used to stabilize the pure metastable α-form in L-glutamic acid crystallization, where the additive concentration of 0.05-0.1 (g/L) was sufficient to stabilize the 100% wt metastable α-form in solid product at L-glutamic acid concentration of 30-45 (g/L). Additionally, the present results indicated that the adsorption of additive L-phenylalanine on the (001) surface of α-form was more favorable than that of the β-form molecular, so the nucleation sites of stable β-form was occupied by additive molecular, which resulted in inhibition of nucleation and growth of β-form, allowing stabilization of metastable α-form.

Exponential p-stability of delayed Cohen-Grossberg-type BAM neural networks with impulses

International Nuclear Information System (INIS)

Xia Yonghui; Huang Zhenkun; Han Maoan

2008-01-01

An impulsive Cohen-Grossberg-type bidirectional associative memory (BAM) neural networks with distributed delays is studied. Some new sufficient conditions are established for the existence and global exponential stability of a unique equilibrium without strict conditions imposed on self regulation functions. The approaches are based on Laypunov-Kravsovskii functional and homeomorphism theory. When our results are applied to the BAM neural networks, our results generalize some previously known results. It is believed that these results are significant and useful for the design and applications of Cohen-Grossberg-type bidirectional associative memory networks

Characterizing short-term stability for Boolean networks over any distribution of transfer functions

International Nuclear Information System (INIS)

Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.

2016-01-01

Here we present a characterization of short-term stability of random Boolean networks under arbitrary distributions of transfer functions. Given any distribution of transfer functions for a random Boolean network, we present a formula that decides whether short-term chaos (damage spreading) will happen. We provide a formal proof for this formula, and empirically show that its predictions are accurate. Previous work only works for special cases of balanced families. Finally, it has been observed that these characterizations fail for unbalanced families, yet such families are widespread in real biological networks.

Effect of dispersing and stabilizing additives on rheological characteristics of the upgraded brown coal water mixture

International Nuclear Information System (INIS)

Umar, Datin Fatia; Muta'alim; Usui, Hiromoto; Komoda, Yoshiyuki

2009-01-01

Upgraded brown coal water mixture (UBCWM) preparation by using an Indonesian upgraded coal produced by upgraded brown coal (UBC) process, was carried out to study the effect of dispersing and stabilizing additives on rheological behavior of the UBCWM. Three kinds of anionic dispersing additives, naphthalene sulfonic formaldehyde condensate (NSF), poly (meth) acrylate (PMA) and poly styrene sulfonic acid (PSS) and three kinds of stabilizing additives, carboxyl methyl cellulose (CMC), rhansam gum (S-194) and gellan gum (S-60) were used in this study. Results indicate that the addition of NSF 0.3 wt.% together with S-194 0.01 wt.% is effective in preparing UBCWM with good slurryability and stability, based on its rheological characteristics with the apparent viscosity at shear rate of 100 s - 1 and yield stress at zero point of shear rate. The rheological behavior of all of the UBCWM that prepared, exhibits non-Newtonian Bingham plastic. From the economical point of view, the price of S-194 is expensive. On the other hand, CMC is cheap and abundant. Therefore, the addition of CMC 0.01 wt.% together with NSF 0.3 wt.% is also effective in preparing UBCWM with good fluidity and stability. (author)

Networks of dissipative systems compositional certification of stability, performance, and safety

CERN Document Server

Arcak, Murat; Packard, Andrew

2016-01-01

This book addresses a major problem for today’s large-scale networked systems: certification of the required stability and performance properties using analytical and computational models. On the basis of illustrative case studies, it demonstrates the applicability of theoretical methods to biological networks, vehicle fleets, and Internet congestion control. Rather than tackle the network as a whole —an approach that severely limits the ability of existing methods to cope with large numbers of physical components— the book develops a compositional approach that derives network-level guarantees from key structural properties of the components and their interactions. The foundational tool in this approach is the established dissipativity theory, which is reviewed in the first chapter and supplemented with modern computational techniques. The book blends this theory with the authors’ recent research efforts at a level that is accessible to graduate students and practising engineers familiar with only th...

Stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Wang Linshan; Zhang Zhe; Wang Yangfan

2008-01-01

Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities

The effect of leachate recirculation with enzyme cellulase addition on waste stability in landfill bioreactor

Science.gov (United States)

Saffira, N.; Kristanto, G. A.

2018-01-01

Landfill bioreactor with leachate recirculation is known to enhance waste stabilization. However, the composition of waste in Indonesia is comprised by organic waste which is lignocellulosic materials that considered take a long time to degrade under anaerobic condition. To accelerate the degradation process, enzyme addition is ought to do. Cellulase is an enzyme that can catalyse cellulose and other polysaccharide decomposition processes. Therefore, operation of waste degradation using leachate recirculation with a cellulase addition to enhance waste stabilization was investigated using anaerobic bioreactor landfill. The experiment was performed on 2 conditions; leachate recirculation with cellulase addition and recirculation only as a control. The addition of cellulase is reported to be significant in decreasing organic content, represented by volatile solid parameter. The volatile solid reduction in the cellulase augmented reactor and control reactor was 17.86% and 7.90%, respectively. Cellulase addition also resulted in the highest cellulose reduction. Settlement of the landfill in a bioreactor with enzyme addition (32.67%) was reported to be higher than the control (19.33%). Stabilization of landfill review by the decreasing rate constant of the cellulose and lignin ratio parameter was more rapidly achieved by the enzyme addition (0.014 day-1) compared to control (0.002 day-1).

Novel global robust stability criteria for interval neural networks with multiple time-varying delays

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.

2005-01-01

This Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

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Guo Hao

2011-05-01

Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

Prediction of Shanghai Index based on Additive Legendre Neural Network

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Yang Bin

2017-01-01

Full Text Available In this paper, a novel Legendre neural network model is proposed, namely additive Legendre neural network (ALNN. A new hybrid evolutionary method besed on binary particle swarm optimization (BPSO algorithm and firefly algorithm is proposed to optimize the structure and parameters of ALNN model. Shanghai stock exchange composite index is used to evaluate the performance of ALNN. Results reveal that ALNN performs better than LNN model.

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

Science.gov (United States)

de Vos, Marjon G. J.; Bollenbach, Tobias

2017-01-01

Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. PMID:28923953

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Xiaohu; Xu Daoyi

2009-01-01

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.

New results for global robust stability of bidirectional associative memory neural networks with multiple time delays

International Nuclear Information System (INIS)

Senan, Sibel; Arik, Sabri

2009-01-01

This paper presents some new sufficient conditions for the global robust asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with multiple time delays. The results we obtain impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. We also give some numerical examples to demonstrate the applicability and effectiveness of our results, and compare the results with the previous robust stability results derived in the literature.

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

International Nuclear Information System (INIS)

Arik, Sabri

2006-01-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

Science.gov (United States)

Arik, Sabri

2006-02-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature.

Global exponential stability analysis on impulsive BAM neural networks with distributed delays

Science.gov (United States)

Li, Yao-Tang; Yang, Chang-Bo

2006-12-01

Using M-matrix and topological degree tool, sufficient conditions are obtained for the existence, uniqueness and global exponential stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with distributed delays and subjected to impulsive state displacements at fixed instants of time by constructing a suitable Lyapunov functional. The results remove the usual assumptions that the boundedness, monotonicity, and differentiability of the activation functions. It is shown that in some cases, the stability criteria can be easily checked. Finally, an illustrative example is given to show the effectiveness of the presented criteria.

The Strength Behaviour of Lime Stabilized Organic Clay Soil Modified by Catalyst Additeives

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Khitam Abdulhussein Saeed

2016-12-01

Full Text Available The organic clay soil can be found in many large size reclaimed lands. These soils present enormously high settlement potential and low strength that needs to be improved by means of effective ground improvement techniques. One of the low cost techniques is to modify the soil with lime in-situ to make it suitable for construction and allow it to increase in strength by pozzolanic reactions between lime and clay minerals. Lime is known to be an effective stabilization material for clayey soil. Nevertheless, its effectiveness may be less with organic clay due to low effective strength properties. Thus, this study concerns the addition of catalyst i.e. zeolite which may improve the performance of lime stabilization to accelerate lime-organic clay reactions. The unconfined compressive test (UCT is conducted on remoulded samples (38mm x 80mm for 0, 7, 14 , 28, and 90 days of curing period. The addition of synthetic zeolite in lime-organic stabilized soil has increased the soil strength by 185% at 90 days curing period at the design mix of organic clay + 10% lime +10% zeolite. The higher value of UCS indicates that zeolite is an effective catalyst to enhance lime stabilization.

Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks

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Ramanpreet Kaur

2017-02-01

Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.

An analysis of global robust stability of uncertain cellular neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Park, Ju H.

2007-01-01

This paper considers the robust stability analysis of cellular neural networks with discrete and distributed delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, a novel stability criterion guaranteeing the global robust convergence of the equilibrium point is derived. The criterion can be solved easily by various convex optimization algorithms. An example is given to illustrate the usefulness of our results

Delay-Dependent Stability Criterion for Bidirectional Associative Memory Neural Networks with Interval Time-Varying Delays

Science.gov (United States)

Park, Ju H.; Kwon, O. M.

In the letter, the global asymptotic stability of bidirectional associative memory (BAM) neural networks with delays is investigated. The delay is assumed to be time-varying and belongs to a given interval. A novel stability criterion for the stability is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. Two numerical examples are illustrated to show the effectiveness of our new result.

Delay-Dependent Stability Criteria of Uncertain Periodic Switched Recurrent Neural Networks with Time-Varying Delays

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Xing Yin

2011-01-01

uncertain periodic switched recurrent neural networks with time-varying delays. When uncertain discrete-time recurrent neural network is a periodic system, it is expressed as switched neural network for the finite switching state. Based on the switched quadratic Lyapunov functional approach (SQLF and free-weighting matrix approach (FWM, some linear matrix inequality criteria are found to guarantee the delay-dependent asymptotical stability of these systems. Two examples illustrate the exactness of the proposed criteria.

Existence and stability of periodic solution in impulsive Hopfield neural networks with finite distributed delays

International Nuclear Information System (INIS)

Yang Xiaofan; Liao Xiaofeng; Evans, David J.; Tang Yuanyan

2005-01-01

In this Letter, we introduce a class of Hopfield neural networks with periodic impulses and finite distributed delays. We then derive a sufficient condition for the existence and global exponential stability of a unique periodic solution of the networks, which assumes neither the differentiability nor the monotonicity of the activation functions. Our condition extends and generalizes a known condition for the global exponential periodicity of continuous Hopfield neural networks with finite distributed delays

Exponential stability of fuzzy cellular neural networks with constant and time-varying delays

International Nuclear Information System (INIS)

Liu Yanqing; Tang Wansheng

2004-01-01

In this Letter, the global stability of delayed fuzzy cellular neural networks (FCNN) with either constant delays or time varying delays is proposed. Firstly, we give the existence and uniqueness of the equilibrium point by using the theory of topological degree and the properties of nonsingular M-matrix and the sufficient conditions for ascertaining the global exponential stability by constructing a suitable Lyapunov functional. Secondly, the criteria for guaranteeing the global exponential stability of FCNN with time varying delays are given and the estimation of exponential convergence rate with regard to speed of vary of delays is presented by constructing a suitable Lyapunov functional

Global robust stability of neural networks with multiple discrete delays and distributed delays

International Nuclear Information System (INIS)

Gao Ming; Cui Baotong

2009-01-01

The problem of global robust stability is investigated for a class of uncertain neural networks with both multiple discrete time-varying delays and distributed time-varying delays. The uncertainties are assumed to be of norm-bounded form and the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov stability theory and linear matrix inequality technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. The proposed LMI-based criteria are computationally efficient as they can be easily checked by using recently developed algorithms in solving LMIs. Two examples are given to show the effectiveness of the proposed results.

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

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Alexandru D. Iordan

2018-01-01

Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

Experimental Study of Hydrogen Addition Effects on a Swirl-Stabilized Methane-Air Flame

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Mao Li

2017-11-01

Full Text Available The effects of H2 addition on a premixed methane-air flame was studied experimentally with a swirl-stabilized gas turbine model combustor. Experiments with 0%, 25%, and 50% H2 molar fraction in the fuel mixture were conducted under atmospheric pressure. The primary objectives are to study the impacts of H2 addition on flame lean blowout (LBO limits, flame shapes and anchored locations, flow field characteristics, precessing vortex core (PVC instability, as well as the CO emission performance. The flame LBO limits were identified by gradually reducing the equivalence ratio until the condition where the flame physically disappeared. The time-averaged CH chemiluminescence was used to reveal the characteristics of flame stabilization, e.g., flame structure and stabilized locations. In addition, the inverse Abel transform was applied to the time-averaged CH results so that the distribution of CH signal on the symmetric plane of the flame was obtained. The particle image velocimetry (PIV was used to detect the characteristics of the flow field with a frequency of 2 kHz. The snapshot method of POD (proper orthogonal decomposition and fast Fourier transform (FFT were adopted to capture the most prominent coherent structures in the turbulent flow field. CO emission was monitored with an exhaust probe that was installed close to the combustor exit. The experimental results indicated that the H2 addition extended the flame LBO limits and the operation range of low CO emission. The influence of H2 addition on the flame shape, location, and flow field was observed. With the assistance of POD and FFT, the combustion suppression impacts on PVC was found.

Existence and exponential stability of almost periodic solution for Hopfield-type neural networks with impulse

International Nuclear Information System (INIS)

Zhang Huiying; Xia Yonghui

2008-01-01

In this paper, some sufficient conditions are obtained for checking the existence and exponential stability of almost periodic solution for bidirectional associative memory Hopfield-type neural networks with impulse. The approaches are based on contraction principle and Gronwall-Bellman's inequality. This paper is considering the almost periodic solution for impulsive Hopfield-type neural networks

Thermal stability and filterability of jet fuels containing PDR additives in small-scale tests and realistic rig simulations

Energy Technology Data Exchange (ETDEWEB)

Bauldreay, J.M.; Clark, R.H.; Heins, R.J. [Shell Research, Ltd., Chester (United Kingdom)

1995-05-01

Specification, small-scale and realistic fuel simulation tests have addressed concerns about the impact of pipeline drag reducer (PDR) flow modifying additives on jet fuel handling and performance. A typical PDR additive tended to block filters which were similar to those used in the specification Jet Fuel Thermal Oxidation Tester (JFTOT) and other thermal stability test apparatus. Blockages reduced flow rates and PDR concentrations downstream of the filters. Consequently two PDR additives (A&B) were tested in JFTOT apparatus without the usual in-line pre-filters as part of a Ministry of Defense (MoD) co-ordinated Round Robin exercise. Some fuel/PDR additive combinations caused decreases in JFTOT breakpoints. Effects were additive- (type, concentration and degree of shear) and fuel-dependent; most failures were caused by filter blockages and not by a failing lacquer rating. In further work at Thornton, the thermal stability characteristics of similar fuel/additive combinations have been examined in non-specification tests. In Flask Oxidation Tests, PDR additives caused no significant increase in the liquid phase oxidation rates of the fuels. Additives were tested in the Single Tube Heat Transfer Rig (STHTR) which duplicates many of the conditions of a heat exchanger element in an engine`s fuel supply system. B produced an average two-fold decrease in thermal stability in a Merox fuel; A had no significant effect. In hydrotreated fuel, B reduced the thermal stability up to five-fold. A had little effect below 205{degrees}C, while at higher temperatures there may have been a marginal improvement in thermal stability. Again, certain jet fuel/PDR combinations were seen to reduce thermal stability.

Impulsive effects on global asymptotic stability of delay BAM neural networks

International Nuclear Information System (INIS)

Chen Jun; Cui Baotong

2008-01-01

Based on the proper Lyapunov functions and the Jacobsthal liner inequality, some sufficient conditions are presented in this paper for global asymptotic stability of delay bidirectional associative memory neural networks with impulses. The obtained results are independently of the delay parameters and can be easily verified. Also, some remarks and an illustrative example are given to demonstrate the effectiveness of the obtained results

Stability and Hopf Bifurcation in a Delayed SEIRS Worm Model in Computer Network

Directory of Open Access Journals (Sweden)

Zizhen Zhang

2013-01-01

Full Text Available A delayed SEIRS epidemic model with vertical transmission in computer network is considered. Sufficient conditions for local stability of the positive equilibrium and existence of local Hopf bifurcation are obtained by analyzing distribution of the roots of the associated characteristic equation. Furthermore, the direction of the local Hopf bifurcation and the stability of the bifurcating periodic solutions are determined by using the normal form theory and center manifold theorem. Finally, a numerical example is presented to verify the theoretical analysis.

Robust stability of uncertain Markovian jumping Cohen-Grossberg neural networks with mixed time-varying delays

International Nuclear Information System (INIS)

Sheng Li; Yang Huizhong

2009-01-01

This paper considers the robust stability of a class of uncertain Markovian jumping Cohen-Grossberg neural networks (UMJCGNNs) with mixed time-varying delays. The parameter uncertainties are norm-bounded and the mixed time-varying delays comprise discrete and distributed time delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. An example is given to show the effectiveness of the proposed results.

Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

Directory of Open Access Journals (Sweden)

Fei Chen

2013-01-01

Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

Global exponential stability of cellular neural networks with mixed delays and impulses

International Nuclear Information System (INIS)

Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua

2007-01-01

In this paper cellular neural networks with mixed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality technique. The results of this paper are new and they complement previously known results

New results on global exponential stability of recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Xu Shengyuan; Chu Yuming; Lu Junwei

2006-01-01

This Letter provides new sufficient conditions for the existence, uniqueness and global exponential stability of the equilibrium point of recurrent neural networks with time-varying delays by employing Lyapunov functions and using the Halanay inequality. The time-varying delays are not necessarily differentiable. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. The derived stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be checked easily by resorting to recently developed algorithms solving LMIs. Furthermore, the proposed stability results are less conservative than some previous ones in the literature, which is demonstrated via some numerical examples

New results on global exponential stability of recurrent neural networks with time-varying delays

Energy Technology Data Exchange (ETDEWEB)

Xu Shengyuan [Department of Automation, Nanjing University of Science and Technology, Nanjing 210094 (China)]. E-mail: syxu02@yahoo.com.cn; Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou, Zhejiang 313000 (China); Lu Junwei [School of Electrical and Automation Engineering, Nanjing Normal University, 78 Bancang Street, Nanjing, 210042 (China)

2006-04-03

This Letter provides new sufficient conditions for the existence, uniqueness and global exponential stability of the equilibrium point of recurrent neural networks with time-varying delays by employing Lyapunov functions and using the Halanay inequality. The time-varying delays are not necessarily differentiable. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. The derived stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be checked easily by resorting to recently developed algorithms solving LMIs. Furthermore, the proposed stability results are less conservative than some previous ones in the literature, which is demonstrated via some numerical examples.

Stabilized zirconia with cerium and neodymium addition

International Nuclear Information System (INIS)

Andrade, I.M. de; Pessoa, R.C.; Nasar, M.C.; Nasar, R.S.; Rodriques, M.K.C.; Oliveira, J.F.

2006-01-01

Zr 0,9 Ce 0,05 Nd 0,05 O 1,975 system was synthesized with the use of the Pechini method. The polymeric resin was calcined at 350 deg C/3 h and analysed by FTIR that show bands relative to organic. Radicals esther type. The TGA curve indicated the polymeric decomposition occurring from 30 deg C to 740 deg C. DTA analysis show a exothermic peak in 100 deg C due to loss of water of material. From 500 deg C to 800 deg C was observed a intense peak due to polymer decomposition and the zirconia crystallization. The calcined powder from 350 deg C/3 h e 30 min to 900 deg/3 h were analysed by XRD that show the crystalline phase formation with the increase of temperature. The X-ray diffraction pattern show the presence of two phases, such as tetragonal and cubic of zirconia demonstrating that neodymium and cerium additions led to zirconia stabilization. (author)

Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Yonggang Chen

2008-01-01

Full Text Available This paper considers the delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays. By constructing the new Lyapunov functional, the improved delay-dependent exponential stability criterion is derived in terms of linear matrix inequality (LMI. Moreover, in order to reduce the conservativeness, some slack matrices are introduced in this paper. Two numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.

High-temperature grain size stabilization of nanocrystalline Fe–Cr alloys with Hf additions

Energy Technology Data Exchange (ETDEWEB)

Li, Lulu, E-mail: lli18@ncsu.edu; Saber, Mostafa; Xu, Weizong; Zhu, Yuntian; Koch, Carl C.; Scattergood, Ronald O.

2014-09-08

The influence of 1–4 at% Hf additions on the thermal stability of mechanically alloyed nanocrystalline Fe–14Cr alloys was studied in this work. XRD-calculated grain size and microhardness results were reported versus isochronal annealing treatments up to 1100 °C. Microstructural evolution was investigated using channeling contrast FIB imaging and TEM. Grain size of samples with 4 at% Hf was found to be maintained in the nanoscale range at temperatures up to 1000 °C. Zener pinning was considered as a major source of high temperature grain size stabilization. By comparing the Orowan strengthening contribution to the total hardness, the deviation of grain size predictions from the actual grain size in Fe–14Cr–4Hf suggests the presence of thermodynamic stabilization by the solute segregation to grain boundaries (GBs). A predictive thermodynamic model indicates that the thermodynamic stabilization can be expected.

Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

OpenAIRE

Wei Feng; Simon X. Yang; Haixia Wu

2014-01-01

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported ...

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

Science.gov (United States)

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Stability and attractive basins of multiple equilibria in delayed two-neuron networks

International Nuclear Information System (INIS)

Huang Yu-Jiao; Zhang Hua-Guang; Wang Zhan-Shan

2012-01-01

Multiple stability for two-dimensional delayed recurrent neural networks with piecewise linear activation functions of 2r (r ≥ 1) corner points is studied. Sufficient conditions are established for checking the existence of (2r + 1) 2 equilibria in delayed recurrent neural networks. Under these conditions, (r + 1) 2 equilibria are locally exponentially stable, and (2r + 1) 2 — (r + 1) 2 — r 2 equilibria are unstable. Attractive basins of stable equilibria are estimated, which are larger than invariant sets derived by decomposing state space. One example is provided to illustrate the effectiveness of our results. (general)

Lagrange α-exponential stability and α-exponential convergence for fractional-order complex-valued neural networks.

Science.gov (United States)

Jian, Jigui; Wan, Peng

2017-07-01

This paper deals with the problem on Lagrange α-exponential stability and α-exponential convergence for a class of fractional-order complex-valued neural networks. To this end, some new fractional-order differential inequalities are established, which improve and generalize previously known criteria. By using the new inequalities and coupling with the Lyapunov method, some effective criteria are derived to guarantee Lagrange α-exponential stability and α-exponential convergence of the addressed network. Moreover, the framework of the α-exponential convergence ball is also given, where the convergence rate is related to the parameters and the order of differential of the system. These results here, which the existence and uniqueness of the equilibrium points need not to be considered, generalize and improve the earlier publications and can be applied to monostable and multistable fractional-order complex-valued neural networks. Finally, one example with numerical simulations is given to show the effectiveness of the obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks.

Science.gov (United States)

Hu, Cheng; Yu, Juan; Chen, Zhanheng; Jiang, Haijun; Huang, Tingwen

2017-05-01

In this paper, the fixed-time stability of dynamical systems and the fixed-time synchronization of coupled discontinuous neural networks are investigated under the framework of Filippov solution. Firstly, by means of reduction to absurdity, a theorem of fixed-time stability is established and a high-precision estimation of the settling-time is given. It is shown by theoretic proof that the estimation bound of the settling time given in this paper is less conservative and more accurate compared with the classical results. Besides, as an important application, the fixed-time synchronization of coupled neural networks with discontinuous activation functions is proposed. By designing a discontinuous control law and using the theory of differential inclusions, some new criteria are derived to ensure the fixed-time synchronization of the addressed coupled networks. Finally, two numerical examples are provided to show the effectiveness and validity of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

International Nuclear Information System (INIS)

Xu Chang-Jin; Li Pei-Luan; Pang Yi-Cheng

2017-01-01

This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. (paper)

A new criterion for global robust stability of interval neural networks with discrete time delays

International Nuclear Information System (INIS)

Li Chuandong; Chen Jinyu; Huang Tingwen

2007-01-01

This paper further studies global robust stability of a class of interval neural networks with discrete time delays. By introducing an equivalent transformation of interval matrices, a new criterion on global robust stability is established. In comparison with the results reported in the literature, the proposed approach leads to results with less restrictive conditions. Numerical examples are also worked through to illustrate our results

Estimation of exponential convergence rate and exponential stability for neural networks with time-varying delay

International Nuclear Information System (INIS)

Tu Fenghua; Liao Xiaofeng

2005-01-01

We study the problem of estimating the exponential convergence rate and exponential stability for neural networks with time-varying delay. Some criteria for exponential stability are derived by using the linear matrix inequality (LMI) approach. They are less conservative than the existing ones. Some analytical methods are employed to investigate the bounds on the interconnection matrix and activation functions so that the systems are exponentially stable

Robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays

International Nuclear Information System (INIS)

Balasubramaniam, P.; Lakshmanan, S.; Manivannan, A.

2012-01-01

Highlights: ► Robust stability analysis for Markovian jumping interval neural networks is considered. ► Both linear fractional and interval uncertainties are considered. ► A new LKF is constructed with triple integral terms. ► MATLAB LMI control toolbox is used to validate theoretical results. ► Numerical examples are given to illustrate the effectiveness of the proposed method. - Abstract: This paper investigates robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. The delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional (LKF), some inequality techniques and stochastic stability theory, new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results.

INFLUENCE OF APPLYING ADDITIONAL FORCING FANS FOR THE AIR DISTRIBUTION IN VENTILATION NETWORK

Directory of Open Access Journals (Sweden)

Nikodem SZLĄZAK

2016-07-01

Full Text Available Mining progress in underground mines cause the ongoing movement of working areas. Consequently, it becomes neces-sary to adapt the ventilation network of a mine to direct airflow into newly-opened districts. For economic reasons, opening new fields is often achieved via underground workings. Length of primary intake and return routes increases and also increases the total resistance of a complex ventilation network. The development of a subsurface structure can make it necessary to change the air distribution in a ventilation network. Increasing airflow into newly-opened districts is necessary. In mines where extraction does not entail gas-related hazards, there is possibility of implementing a push-pull ventilation system in order to supplement airflows to newly developed mining fields. This is achieved by installing sub-surface fan stations with forcing fans at the bottom of downcast shaft. In push-pull systems with multiple main fans, it is vital to select forcing fans with characteristic curves matching those of the existing exhaust fans to prevent undesirable mutual interaction. In complex ventilation networks it is necessary to calculate distribution of airflow (especially in net-works with a large number of installed fans. In the article the influence of applying additional forcing fans for the air distribution in ventilation network for underground mine were considered. There are also analysed the extent of over-pressure caused by the additional forcing fan in branches of the ventilation network (the operating range of additional forcing fan. Possibilities of increasing airflow rate in working areas were conducted.

Stability and bifurcation analysis for a discrete-time bidirectional ring neural network model with delay

Directory of Open Access Journals (Sweden)

Yan-Ke Du

2013-09-01

Full Text Available We study a class of discrete-time bidirectional ring neural network model with delay. We discuss the asymptotic stability of the origin and the existence of Neimark-Sacker bifurcations, by analyzing the corresponding characteristic equation. Employing M-matrix theory and the Lyapunov functional method, global asymptotic stability of the origin is derived. Applying the normal form theory and the center manifold theorem, the direction of the Neimark-Sacker bifurcation and the stability of bifurcating periodic solutions are obtained. Numerical simulations are given to illustrate the main results.

Global asymptotic stability analysis of bidirectional associative memory neural networks with distributed delays and impulse

International Nuclear Information System (INIS)

Huang Zaitang; Luo Xiaoshu; Yang Qigui

2007-01-01

Many systems existing in physics, chemistry, biology, engineering and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be model by impulsive differential system or impulsive neural networks. This paper formulates and studies a new model of impulsive bidirectional associative memory (BAM) networks with finite distributed delays. Several fundamental issues, such as global asymptotic stability and existence and uniqueness of such BAM neural networks with impulse and distributed delays, are established

Global asymptotic stability of Cohen-Grossberg neural network with continuously distributed delays

International Nuclear Information System (INIS)

Wan Li; Sun Jianhua

2005-01-01

The convergence dynamical behaviors of Cohen-Grossberg neural network with continuously distributed delays are discussed. By using Brouwer's fixed point theorem, matrix theory and analysis techniques such as Gronwall inequality, some new sufficient conditions guaranteeing the existence, uniqueness of an equilibrium point and its global asymptotic stability are obtained. An example is given to illustrate the theoretical results

The conformational stability and flexibility of insulin with an additional intramolecular cross-link

International Nuclear Information System (INIS)

Brems, D.N.; Brown, P.L.; Nakagawa, S.H.; Tager, H.S.

1991-01-01

The conformational stability and flexibility of insulin containing a cross-link between the alpha-amino group of the A-chain to the epsilon-amino group of Lys29 of the B-chain was examined. The cross-link varied in length from 2 to 12 carbon atoms. The conformational stability was determined by guanidine hydrochloride-induced equilibrium denaturation and flexibility was assessed by H2O/D2O amide exchange. The cross-link has substantial effects on both conformational stability and flexibility which depend on its length. In general, the addition of a cross-link enhances conformational stability and decreases flexibility. The optimal length for enhanced stability and decreased flexibility was the 6-carbon link. For the 6-carbon link the Gibbs free energy of unfolding was 8.0 kcal/mol compared to 4.5 kcal/mol for insulin, and the amide exchange rate decreased by at least 3-fold. A very short cross-link (i.e. the 2-carbon link) caused conformational strain that was detectable by a lack of stabilization in the Gibbs free energy of unfolding and enhancement in the amide exchange rate compared to insulin. The effect of the cross-link length on insulin hydrodynamic properties is discussed relative to previously obtained receptor binding results

Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of U.S. military veterans.

Science.gov (United States)

von Stockert, Sophia H H; Fried, Eiko I; Armour, Cherie; Pietrzak, Robert H

2018-03-15

Previous studies have used network models to investigate how PTSD symptoms associate with each other. However, analyses examining the degree to which these networks are stable over time, which are critical to identifying symptoms that may contribute to the chronicity of this disorder, are scarce. In the current study, we evaluated the temporal stability of DSM-5 PTSD symptom networks over a three-year period in a nationally representative sample of trauma-exposed U.S. military veterans. Data were analyzed from 611 trauma-exposed U.S. military veterans who participated in the National Health and Resilience in Veterans Study (NHRVS). We estimated regularized partial correlation networks of DSM-5 PTSD symptoms at baseline (Time 1) and at three-year follow-up (Time 2), and examined their temporal stability. Evaluation of the network structure of PTSD symptoms at Time 1 and Time 2 using a formal network comparison indicated that the Time 1 network did not differ significantly from the Time 2 network with regard to network structure (p = 0.12) or global strength (sum of all absolute associations, i.e. connectivity; p = 0.25). Centrality estimates of both networks (r = 0.86) and adjacency matrices (r = 0.69) were highly correlated. In both networks, avoidance, intrusive, and negative cognition and mood symptoms were among the more central nodes. This study is limited by the use of a self-report instrument to assess PTSD symptoms and recruitment of a relatively homogeneous sample of predominantly older, Caucasian veterans. Results of this study demonstrate the three-year stability of DSM-5 PTSD symptom network structure in a nationally representative sample of trauma-exposed U.S. military veterans. They further suggest that trauma-related avoidance, intrusive, and dysphoric symptoms may contribute to the chronicity of PTSD symptoms in this population. Published by Elsevier B.V.

Stability of the spreading in small-world network with predictive controller

International Nuclear Information System (INIS)

Bao, Z.J.; Jiang, Q.Y.; Yan, W.J.; Cao, Y.J.

2010-01-01

In this Letter, we apply the predictive control strategy to suppress the propagation of diseases or viruses in small-world network. The stability of small-world spreading model with predictive controller is investigated. The sufficient and necessary stability condition is given, which is closely related to the controller parameters and small-world rewiring probability p. Our simulations discover a phenomenon that, with the fixed predictive controller parameters, the spreading dynamics become more and more stable when p decreases from a larger value to a smaller one, and the suitable controller parameters can effectively suppress the spreading behaviors even when p varies within the whole spectrum, and the unsuitable controller parameters can lead to oscillation when p lies within a certain range.

Fine-tuning and the stability of recurrent neural networks.

Directory of Open Access Journals (Sweden)

David MacNeil

Full Text Available A central criticism of standard theoretical approaches to constructing stable, recurrent model networks is that the synaptic connection weights need to be finely-tuned. This criticism is severe because proposed rules for learning these weights have been shown to have various limitations to their biological plausibility. Hence it is unlikely that such rules are used to continuously fine-tune the network in vivo. We describe a learning rule that is able to tune synaptic weights in a biologically plausible manner. We demonstrate and test this rule in the context of the oculomotor integrator, showing that only known neural signals are needed to tune the weights. We demonstrate that the rule appropriately accounts for a wide variety of experimental results, and is robust under several kinds of perturbation. Furthermore, we show that the rule is able to achieve stability as good as or better than that provided by the linearly optimal weights often used in recurrent models of the integrator. Finally, we discuss how this rule can be generalized to tune a wide variety of recurrent attractor networks, such as those found in head direction and path integration systems, suggesting that it may be used to tune a wide variety of stable neural systems.

Delay-dependent exponential stability for neural networks with discrete and distributed time-varying delays

International Nuclear Information System (INIS)

Zhu Xunlin; Wang Youyi

2009-01-01

This Letter studies the exponential stability for a class of neural networks (NNs) with both discrete and distributed time-varying delays. Under weaker assumptions on the activation functions, by defining a more general type of Lyapunov functionals and developing a new convex combination technique, new less conservative and less complex stability criteria are established to guarantee the global exponential stability of the discussed NNs. The obtained conditions are dependent on both discrete and distributed delays, are expressed in terms of linear matrix inequalities (LMIs), and contain fewer decision variables. Numerical examples are given to illustrate the effectiveness and the less conservatism of the proposed conditions.

Robust stability analysis of switched Hopfield neural networks with time-varying delay under uncertainty

International Nuclear Information System (INIS)

Huang He; Qu Yuzhong; Li Hanxiong

2005-01-01

With the development of intelligent control, switched systems have been widely studied. Here we try to introduce some ideas of the switched systems into the field of neural networks. In this Letter, a class of switched Hopfield neural networks with time-varying delay is investigated. The parametric uncertainty is considered and assumed to be norm bounded. Firstly, the mathematical model of the switched Hopfield neural networks is established in which a set of Hopfield neural networks are used as the individual subsystems and an arbitrary switching rule is assumed; Secondly, robust stability analysis for such switched Hopfield neural networks is addressed based on the Lyapunov-Krasovskii approach. Some criteria are given to guarantee the switched Hopfield neural networks to be globally exponentially stable for all admissible parametric uncertainties. These conditions are expressed in terms of some strict linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate our results

Polymer stabilization of the smectic C-alpha* liquid crystal phase—Over tenfold thermal stabilization by confining networks of photo-polymerized reactive mesogens

International Nuclear Information System (INIS)

Labeeb, A.; Gleeson, H. F.; Hegmann, T.

2015-01-01

The smectic C*-alpha (SmC α *) phase is one of the sub-phases of ferroelectric liquid crystals that has drawn much interest due to its electro-optical properties and ultrafast switching. Generally observed above the ferroelectric SmC* phase in temperature, the SmC α * commonly shows only very narrow phase temperature range of a few degree Celsius. To broaden the SmC α * phase, polymer stabilization was investigated for thermal phase stabilization. Two different reactive monomers were tested in three mixtures, and all three broadened the temperature range of the SmC α * phase from 3 °C to 39 °C. The current reversal method was used to determine the phase existence versus temperature. Moreover, the texture and network structure was studied by polarized optical microscopy and scanning electron microscopy, with the latter revealing the confinement of the smectic layer structure within the porous polymer network

Improving the phase stability of the SLAC rf driveline network for SLC operation

International Nuclear Information System (INIS)

Weaver, J.N.; Hogg, H.A.

1983-01-01

Successful operation of the Stanford Linear Collider (SLC) will require greater phase stability from the two-mile long rf drive network than previous linac operation did. This paper discusses four proposed modifications of the present system that should help achieve the general objective to reduce all long term temperature and atmospheric pressure induced phase variations to less than 20 0 at 2856 MHz, so that the phase/amplitude detector subsystems, which will control the network output phases relative to a beam reference, will operate within their most accurate ranges

Stability analysis of delayed Cohen-Grossberg BAM neural networks with impulses via nonsmooth analysis

International Nuclear Information System (INIS)

Wen Zhen; Sun Jitao

2009-01-01

In this paper, we investigate the existence and uniqueness of equilibrium point for delayed Cohen-Grossberg bidirectional associative memory (BAM) neural networks with impulses, based on nonsmooth analysis method. And we give the criteria of global exponential stability of the unique equilibrium point for the delayed BAM neural networks with impulses using Lyapunov method. The new sufficient condition generalizes and improves the previously known results. Finally, we present examples to illustrate that our results are effective.

Stability of Control Networks in Autonomous Homeostatic Regulation of Stem Cell Lineages.

Science.gov (United States)

Komarova, Natalia L; van den Driessche, P

2018-05-01

Design principles of biological networks have been studied extensively in the context of protein-protein interaction networks, metabolic networks, and regulatory (transcriptional) networks. Here we consider regulation networks that occur on larger scales, namely the cell-to-cell signaling networks that connect groups of cells in multicellular organisms. These are the feedback loops that orchestrate the complex dynamics of cell fate decisions and are necessary for the maintenance of homeostasis in stem cell lineages. We focus on "minimal" networks that are those that have the smallest possible numbers of controls. For such minimal networks, the number of controls must be equal to the number of compartments, and the reducibility/irreducibility of the network (whether or not it can be split into smaller independent sub-networks) is defined by a matrix comprised of the cell number increments induced by each of the controlled processes in each of the compartments. Using the formalism of digraphs, we show that in two-compartment lineages, reducible systems must contain two 1-cycles, and irreducible systems one 1-cycle and one 2-cycle; stability follows from the signs of the controls and does not require magnitude restrictions. In three-compartment systems, irreducible digraphs have a tree structure or have one 3-cycle and at least two more shorter cycles, at least one of which is a 1-cycle. With further work and proper biological validation, our results may serve as a first step toward an understanding of ways in which these networks become dysregulated in cancer.

Influence of additives on the stability of the phases of alumina; Influencia de aditivos na estabilidade das fases da alumina

Energy Technology Data Exchange (ETDEWEB)

Rosario, D.C.C.; Gouvea, D., E-mail: deisedorosario@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Departamento de Engenharia Metalurgica e de Materiais. Laboratorio de Processos Ceramicos

2011-07-01

Problems with the stability of gamma alumina in catalytic reactions have been solved with the inclusion of additives during the synthesis of alumina. These additives stabilize the temperature of phase transition allowing the use of metastable alumina at high temperatures, but the mechanisms of action of additives are not well defined. It is known that each family of additive or additives behaves in different ways for this stabilization. This work aimed to study the performance of MgO and ZrO{sub 2}, respectively at different concentrations in alumina synthesized via Pechini. The samples were analyzed by DSC, X-ray diffraction, measurement of specific surface area by BET analysis, and infrared analysis. The results showed an increase in transition temperature for both additives, and a different changes for specific surface area, showing that MgO and ZrO{sub 2} work on improving the stability but with distinct mechanisms. (author)

Analysis of Stiffened Penstock External Pressure Stability Based on Immune Algorithm and Neural Network

Directory of Open Access Journals (Sweden)

Wensheng Dong

2014-01-01

Full Text Available The critical external pressure stability calculation of stiffened penstock in the hydroelectric power station is very important work for penstock design. At present, different assumptions and boundary simplification are adopted by different calculation methods which sometimes cause huge differences too. In this paper, we present an immune based artificial neural network model via the model and stability theory of elastic ring, we study effects of some factors (such as pipe diameter, pipe wall thickness, sectional size of stiffening ring, and spacing between stiffening rings on penstock critical external pressure during huge thin-wall procedure of penstock. The results reveal that the variation of diameter and wall thickness can lead to sharp variation of penstock external pressure bearing capacity and then give the change interval of it. This paper presents an optimizing design method to optimize sectional size and spacing of stiffening rings and to determine penstock bearing capacity coordinate with the bearing capacity of stiffening rings and penstock external pressure stability coordinate with its strength safety. As a practical example, the simulation results illustrate that the method presented in this paper is available and can efficiently overcome inherent defects of BP neural network.

Robust stability analysis of Takagi—Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

International Nuclear Information System (INIS)

Ali, M. Syed

2011-01-01

In this paper, the global stability of Takagi—Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature. (general)

Vitrification of F006 plating waste sludge by Reactive Additive Stabilization Process (RASP)

International Nuclear Information System (INIS)

Martin, H.L.; Jantzen, C.M.; Pickett, J.B.

1994-01-01

Solidification into glass of nickel-on-uranium plating wastewater treatment plant sludge (F006 Mixed Waste) has been demonstrated at the Savannah River She (SRS). Vitrification using high surface area additives, the Reactive Additive Stabilization Process (RASP), greatly enhanced the solubility and retention of heavy metals In glass. The bench-scale tests using RASP achieved 76 wt% waste loading In both soda-lime-silica and borosilicate glasses. The RASP has been Independently verified by a commercial waste management company, and a contract awarded to vitrify the approximately 500,000 gallons of stored waste sludge. The waste volume reduction of 89% will greatly reduce the disposal costs, and delisting of the glass waste is anticipated. This will be the world's first commercial-scale vitrification system used for environmental cleanup of Mixed Waste. Its stabilization and volume reduction abilities are expected to set standards for the future of the waste management Industry

Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays.

Science.gov (United States)

Huang, Haiying; Du, Qiaosheng; Kang, Xibing

2013-11-01

In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results. © 2013 ISA. Published by ISA. All rights reserved.

Delay-dependent exponential stability analysis of bi-directional associative memory neural networks with time delay: an LMI approach

International Nuclear Information System (INIS)

Li Chuandong; Liao Xiaofeng; Zhang Rong

2005-01-01

For bi-directional associative memory (BAM) neural networks (NNs) with different constant or time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated in this paper. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problems, which provide bounds on the interconnection matrix and the activation functions, so as to guarantee the system's exponential stability. Some criteria for the exponential stability, which give information on the delay-dependent property, are derived. The results obtained in this paper provide one more set of easily verified guidelines for determining the exponential stability of delayed BAM (DBAM) neural networks, which are less conservative and less restrictive than the ones reported so far in the literature. Some typical examples are presented to show the application of the criteria obtained in this paper

Novel criteria for global exponential periodicity and stability of recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Song Qiankun

2008-01-01

In this paper, the global exponential periodicity and stability of recurrent neural networks with time-varying delays are investigated by applying the idea of vector Lyapunov function, M-matrix theory and inequality technique. We assume neither the global Lipschitz conditions on these activation functions nor the differentiability on these time-varying delays, which were needed in other papers. Several novel criteria are found to ascertain the existence, uniqueness and global exponential stability of periodic solution for recurrent neural network with time-varying delays. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. Some previous results are improved and generalized, and an example is given to show the effectiveness of our method

Evolutionary dynamics on networks of selectively neutral genotypes: Effects of topology and sequence stability

Science.gov (United States)

Aguirre, Jacobo; Buldú, Javier M.; Manrubia, Susanna C.

2009-12-01

Networks of selectively neutral genotypes underlie the evolution of populations of replicators in constant environments. Previous theoretical analysis predicted that such populations will evolve toward highly connected regions of the genome space. We first study the evolution of populations of replicators on simple networks and quantify how the transient time to equilibrium depends on the initial distribution of sequences on the neutral network, on the topological properties of the latter, and on the mutation rate. Second, network neutrality is broken through the introduction of an energy for each sequence. This allows to study the competition between two features (neutrality and energetic stability) relevant for survival and subjected to different selective pressures. In cases where the two features are negatively correlated, the population experiences sudden migrations in the genome space for values of the relevant parameters that we calculate. The numerical study of larger networks indicates that the qualitative behavior to be expected in more realistic cases is already seen in representative examples of small networks.

Evolutionary dynamics on networks of selectively neutral genotypes: effects of topology and sequence stability.

Science.gov (United States)

Aguirre, Jacobo; Buldú, Javier M; Manrubia, Susanna C

2009-12-01

Networks of selectively neutral genotypes underlie the evolution of populations of replicators in constant environments. Previous theoretical analysis predicted that such populations will evolve toward highly connected regions of the genome space. We first study the evolution of populations of replicators on simple networks and quantify how the transient time to equilibrium depends on the initial distribution of sequences on the neutral network, on the topological properties of the latter, and on the mutation rate. Second, network neutrality is broken through the introduction of an energy for each sequence. This allows to study the competition between two features (neutrality and energetic stability) relevant for survival and subjected to different selective pressures. In cases where the two features are negatively correlated, the population experiences sudden migrations in the genome space for values of the relevant parameters that we calculate. The numerical study of larger networks indicates that the qualitative behavior to be expected in more realistic cases is already seen in representative examples of small networks.

Modeling and Stability Analysis of Worm Propagation in Wireless Sensor Network

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Liping Feng

2015-01-01

Full Text Available An improved SIRS model considering communication radius and distributed density of nodes is proposed. The proposed model captures both the spatial and temporal dynamics of worms spread process. Using differential dynamical theories, we investigate dynamics of worm propagation to time in wireless sensor networks (WSNs. Reproductive number which determines global dynamics of worm propagation in WSNs is obtained. Equilibriums and their stabilities are also found. If reproductive number is less than one, the infected fraction of the sensor nodes disappears and if the reproduction number is greater than one, the infected fraction asymptotically stabilizes at the endemic equilibrium. Based on the reproduction number, we discuss the threshold of worm propagation about communication radius and distributed density of nodes in WSNs. Finally, numerical simulations verify the correctness of theoretical analysis.

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

Science.gov (United States)

Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

2017-03-01

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Polymer stabilization of the smectic C-alpha* liquid crystal phase—Over tenfold thermal stabilization by confining networks of photo-polymerized reactive mesogens

Energy Technology Data Exchange (ETDEWEB)

Labeeb, A. [Liquid Crystal Institute, Chemical Physics Interdisciplinary Program, Kent State University, Kent, Ohio 44242 (United States); Microwave Physics and Dielectrics, National Research Center, Dokki 12622 (Egypt); Gleeson, H. F. [School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT (United Kingdom); Hegmann, T., E-mail: thegmann@kent.edu [Liquid Crystal Institute, Chemical Physics Interdisciplinary Program, Kent State University, Kent, Ohio 44242 (United States)

2015-12-07

The smectic C*-alpha (SmC{sub α}*) phase is one of the sub-phases of ferroelectric liquid crystals that has drawn much interest due to its electro-optical properties and ultrafast switching. Generally observed above the ferroelectric SmC* phase in temperature, the SmC{sub α}* commonly shows only very narrow phase temperature range of a few degree Celsius. To broaden the SmC{sub α}* phase, polymer stabilization was investigated for thermal phase stabilization. Two different reactive monomers were tested in three mixtures, and all three broadened the temperature range of the SmC{sub α}* phase from 3 °C to 39 °C. The current reversal method was used to determine the phase existence versus temperature. Moreover, the texture and network structure was studied by polarized optical microscopy and scanning electron microscopy, with the latter revealing the confinement of the smectic layer structure within the porous polymer network.

Global exponential stability of cellular neural networks with continuously distributed delays and impulses

International Nuclear Information System (INIS)

Wang Yixuan; Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua

2006-01-01

In this Letter cellular neural networks with continuously distributed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality techniques. The results of this Letter are new and they complement previously known results

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

Science.gov (United States)

Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

2016-10-01

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

Stability of whole brain and regional network topology within and between resting and cognitive states.

Science.gov (United States)

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Toward a Stakeholder Perspective on Social Stability Risk of Large Hydraulic Engineering Projects in China: A Social Network Analysis

Directory of Open Access Journals (Sweden)

Zhengqi He

2018-04-01

Full Text Available In China, large hydraulic engineering projects have made a great contribution to social economic development; at the same time, they also lead to social risks that affect social stability. The pluralism of stakeholders in large hydraulic engineering projects and the complex interrelationship among stakeholders are the important factors affecting social stability risk. Previous studies of social stability risk have mainly focused on risk identification and risk assessment, without considering the relationships among stakeholders and their linkages of risks. For large hydraulic engineering projects, this paper investigated the relevant risk factors and their interrelationships through a literature review and interviews that represented stakeholder perspectives. The key social stability risk factors were identified based on social network analysis. A multi-channel project financial system, a perfect interest compensation mechanism, an efficient prevention mechanism of group events, and a complete project schedule control system were proposed to mitigate the social stability risks. This study combined stakeholder management with risk management by using social network analysis, providing reference for the social stability risk management of large engineering projects in China.

Exponential stability for stochastic delayed recurrent neural networks with mixed time-varying delays and impulses: the continuous-time case

International Nuclear Information System (INIS)

Karthik Raja, U; Leelamani, A; Raja, R; Samidurai, R

2013-01-01

In this paper, the exponential stability for a class of stochastic neural networks with time-varying delays and impulsive effects is considered. By constructing suitable Lyapunov functionals and by using the linear matrix inequality optimization approach, we obtain sufficient delay-dependent criteria to ensure the exponential stability of stochastic neural networks with time-varying delays and impulses. Two numerical examples with simulation results are provided to illustrate the effectiveness of the obtained results over those already existing in the literature. (paper)

A network dynamics approach to chemical reaction networks

Science.gov (United States)

van der Schaft, A. J.; Rao, S.; Jayawardhana, B.

2016-04-01

A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.

Artificial neural networks versus conventional methods for boiling water reactor stability monitoring

International Nuclear Information System (INIS)

Hagen, T.H.J.J. van der

1995-01-01

The application of an artificial neural network (ANN) for boiling water reactor (BWR) stability monitoring was studied. A three-layer perceptron was trained on synthetic autocorrelation functions to estimate the decay ratio and the resonance frequency from measured neutron noise. Training of the ANN was improved by adding noise to the training patterns and by applying nonconventional error definitions in the generalized delta rule. The performance of the developed ANN was compared with those of conventional stability monitoring techniques. Explicit care was taken for generating unbiased test data. It is found that the trained ANN is capable of monitoring the stability of the Dodewaard BWR for four specific cases. By comparing properties such as the false alarm ratio, the alarm failure ratio, and the average time to alarm, it is shown that it performs worse than model-based methods in stability monitoring of exact second-order systems but that it is more robust (better resistant to corruptions of the input data and to deviations of the system at issue from an exact second-order system) than other methods. The latter explains its good performance on the Dodewaard BWR and is promising for the application of an ANN for stability monitoring of other reactors and for other operating conditions

The effects of Cu addition on the microstructure and thermal stability of an Al-Mg-Si alloy

International Nuclear Information System (INIS)

Man, Jin; Jing, Li; Jie, Shao Guang

2007-01-01

The effects of Cu addition on the microstructure and thermal stability of 6082 Al-Mg-Si alloys were investigated. The results show the Q' precipitates are formed when aged at 170 o C for 4 h in 6082 alloy with 0.6% Cu addition. The hardness value of the alloy with 0.6% Cu is always distinctly higher than that of the alloy without Cu during isothermal treatment at 250 o C. Based on the TEM and three-dimensional atom probe (3DAP) results, the thermal stability of the 6082 alloys with Cu addition is discussed with respect to the distribution of Cu

Global stability and existence of periodic solutions of discrete delayed cellular neural networks

International Nuclear Information System (INIS)

Li Yongkun

2004-01-01

We use the continuation theorem of coincidence degree theory and Lyapunov functions to study the existence and stability of periodic solutions for the discrete cellular neural networks (CNNs) with delays xi(n+1)=xi(n)e-bi(n)h+θi(h)-bar j=1maij(n)fj(xj(n))+θi(h)-bar j=1mbij(n)fj(xj(n- τij(n)))+θi(h)Ii(n),i=1,2,...,m. We obtain some sufficient conditions to ensure that for the networks there exists a unique periodic solution, and all its solutions converge to such a periodic solution

Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Wei Feng

2014-01-01

Full Text Available The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

Stability and Hopf bifurcation in a simplified BAM neural network with two time delays.

Science.gov (United States)

Cao, Jinde; Xiao, Min

2007-03-01

Various local periodic solutions may represent different classes of storage patterns or memory patterns, and arise from the different equilibrium points of neural networks (NNs) by applying Hopf bifurcation technique. In this paper, a bidirectional associative memory NN with four neurons and multiple delays is considered. By applying the normal form theory and the center manifold theorem, analysis of its linear stability and Hopf bifurcation is performed. An algorithm is worked out for determining the direction and stability of the bifurcated periodic solutions. Numerical simulation results supporting the theoretical analysis are also given.

Characterization of cementitiously stabilized subgrades for mechanistic-empirical pavement design

Science.gov (United States)

Solanki, Pranshoo

ettringite, responsible for sulfate-induced heaving, is also discussed. For Level 2 design of pavements, a total of four stress-based statistical models and two feed-forward-type artificial neural network (ANN) models, are evaluated for predicting resilient modulus of 28-day cured stabilized specimens. Specifically, one semi-log stress-based, three log-log stress-based, one Multi-Layer Perceptrons Network (MLPN), and one Radial Basis Function Network (RBFN) are developed. Overall, semi-log stress-based and MLPN neural network are found to show best acceptable performance for the present evaluation and validation datasets. Further, correlations are presented for stress-based models to correlate Mr with compacted specimen characteristics and soil/additive properties. Additionally, the effect of type of additive on indirect tensile and fatigue characteristics of selected stabilized P- and V-soil is evaluated. This study is based on the fact that stabilized layer is subjected to tensile stresses under wheel loading. Thus, the resilient modulus in tension (M rt), fatigue life and strength in tension (sigmat) or flexure (represented by modulus of rupture, MOR) becomes another important design parameter within the mechanistic framework. Cylindrical specimens are prepared, cured for 28 days and subjected to different stress sequences in indirect tension to study the Mrt. On the other hand, stabilized beam specimens are compacted using a Linear Kneading Compactor and subjected to repeated cycles of reloading-unloading after 28 days of curing in a four-point beam fatigue apparatus for evaluating fatigue life and flexural stiffness. It is found that all three additives improved the Mrt, sigmat and MOR values; however, degree of improvement varied with the type of additive and soil. This study encompasses the differences in the design of semi-rigid pavements developed using AASHTO 1993 and AASHTO 2002 MEPDG methodologies. Further, the design curves for fatigue performance prediction of

Stabilization of a Wireless Networked Control System with Packet Loss and Time Delay: An ADS Approach

Directory of Open Access Journals (Sweden)

Jing Wang

2012-01-01

Full Text Available The stabilization problem of a wireless networked control system is considered in this paper. Both time delay and packet loss exist simultaneously in the wireless network. The system is modeled as an asynchronous dynamic system (ADS with unstable subsystems. A sufficient condition for the system to be stable is presented. A numerical example is given to demonstrate the effectiveness of the proposed approach.

Synchronization stability and pattern selection in a memristive neuronal network

Science.gov (United States)

Wang, Chunni; Lv, Mi; Alsaedi, Ahmed; Ma, Jun

2017-11-01

Spatial pattern formation and selection depend on the intrinsic self-organization and cooperation between nodes in spatiotemporal systems. Based on a memory neuron model, a regular network with electromagnetic induction is proposed to investigate the synchronization and pattern selection. In our model, the memristor is used to bridge the coupling between the magnetic flux and the membrane potential, and the induction current results from the time-varying electromagnetic field contributed by the exchange of ion currents and the distribution of charged ions. The statistical factor of synchronization predicts the transition of synchronization and pattern stability. The bifurcation analysis of the sampled time series for the membrane potential reveals the mode transition in electrical activity and pattern selection. A formation mechanism is outlined to account for the emergence of target waves. Although an external stimulus is imposed on each neuron uniformly, the diversity in the magnetic flux and the induction current leads to emergence of target waves in the studied network.

Synchronization stability and pattern selection in a memristive neuronal network.

Science.gov (United States)

Wang, Chunni; Lv, Mi; Alsaedi, Ahmed; Ma, Jun

2017-11-01

Spatial pattern formation and selection depend on the intrinsic self-organization and cooperation between nodes in spatiotemporal systems. Based on a memory neuron model, a regular network with electromagnetic induction is proposed to investigate the synchronization and pattern selection. In our model, the memristor is used to bridge the coupling between the magnetic flux and the membrane potential, and the induction current results from the time-varying electromagnetic field contributed by the exchange of ion currents and the distribution of charged ions. The statistical factor of synchronization predicts the transition of synchronization and pattern stability. The bifurcation analysis of the sampled time series for the membrane potential reveals the mode transition in electrical activity and pattern selection. A formation mechanism is outlined to account for the emergence of target waves. Although an external stimulus is imposed on each neuron uniformly, the diversity in the magnetic flux and the induction current leads to emergence of target waves in the studied network.

Stability of whole brain and regional network topology within and between resting and cognitive states.

Directory of Open Access Journals (Sweden)

Justyna K Rzucidlo

Full Text Available BACKGROUND: Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. METHODOLOGY/PRINCIPAL FINDINGS: fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. CONCLUSIONS/SIGNIFICANCE: These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling

OpenAIRE

Veltz, Romain; Sejnowski, Terrence J.

2015-01-01

International audience; Inhibition stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from in-hibitory interneurons, a circuit element found in the hippocampus and the primary vi-sual cortex. In their working regime, ISNs produce damped oscillations in the γ-range in response to inputs to the inhibitory population. In order to understand the proper-ties of interconnected ISNs, we investigated periodic ...

Stability and bifurcation of a discrete BAM neural network model with delays

International Nuclear Information System (INIS)

Zheng Baodong; Zhang Yang; Zhang Chunrui

2008-01-01

A map modelling a discrete bidirectional associative memory neural network with delays is investigated. Its dynamics is studied in terms of local analysis and Hopf bifurcation analysis. By analyzing the associated characteristic equation, its linear stability is investigated and Hopf bifurcations are demonstrated. It is found that there exist Hopf bifurcations when the delay passes a sequence of critical values. Numerical simulation is performed to verify the analytical results

Global exponential stability of fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Qianhong; Luo Wei

2009-01-01

In this paper, a class of fuzzy bidirectional associated memory (BAM) neural networks with time-varying delays are studied. Employing fixed point theorem, matrix theory and inequality analysis, some sufficient conditions are established for the existence, uniqueness and global exponential stability of equilibrium point. The sufficient conditions are easy to verify at pattern recognition and automatic control. Finally, an example is given to show feasibility and effectiveness of our results.

Lanthanum Nitrate As Electrolyte Additive To Stabilize the Surface Morphology of Lithium Anode for Lithium-Sulfur Battery.

Science.gov (United States)

Liu, Sheng; Li, Guo-Ran; Gao, Xue-Ping

2016-03-01

Lithium-sulfur (Li-S) battery is regarded as one of the most promising candidates beyond conventional lithium ion batteries. However, the instability of the metallic lithium anode during lithium electrochemical dissolution/deposition is still a major barrier for the practical application of Li-S battery. In this work, lanthanum nitrate, as electrolyte additive, is introduced into Li-S battery to stabilize the surface of lithium anode. By introducing lanthanum nitrate into electrolyte, a composite passivation film of lanthanum/lithium sulfides can be formed on metallic lithium anode, which is beneficial to decrease the reducibility of metallic lithium and slow down the electrochemical dissolution/deposition reaction on lithium anode for stabilizing the surface morphology of metallic Li anode in lithium-sulfur battery. Meanwhile, the cycle stability of the fabricated Li-S cell is improved by introducing lanthanum nitrate into electrolyte. Apparently, lanthanum nitrate is an effective additive for the protection of lithium anode and the cycling stability of Li-S battery.

Microstructure and high temperature stability of age hardenable AA2219 aluminium alloy modified by Sc, Mg and Zr additions

Energy Technology Data Exchange (ETDEWEB)

Naga Raju, P. [Metallurgical and Materials Engineering Department, IIT-Madras, Chennai 600036 (India)], E-mail: puvvala_nagaraju@yahoo.com; Srinivasa Rao, K. [Metallurgical Engineering Department, Andhra University, Visakapatnam 530003 (India); Reddy, G.M. [Defence Metallurgical Research Laboratory, Hyderabad 500258 (India); Kamaraj, M.; Prasad Rao, K. [Metallurgical and Materials Engineering Department, IIT-Madras, Chennai 600036 (India)

2007-08-25

The present work pertains to the improvement of high temperature stability of age hardenable AA2219 aluminium-copper (6.3%) alloy. Addition of scandium, magnesium and zirconium to the base metal AA2219 was adopted to improve this high temperature stability. These additions were systematically varied by preparing alloys of different composition using gas tungsten arc melting. Long time ageing studies and impression creep technique were used to study the high temperature stability of the alloys. These modified compositions of the alloy resulted in fine equiaxed grains, refined eutectics, large number of high temperature stable and finer precipitates. Among all the compositions, 0.8% Sc + 0.45% Mg + 0.2% Zr addition was found to be significant in improving the high temperature stability of AA2219 alloy. This may be attributed to the possible microstructural changes, solute enrichment of the matrix and pinning of the grain boundaries by the finer precipitates.

The conductivity and stability of polymer composite solid electrolyte upon addition of graphene

Science.gov (United States)

Hamid, Farzana Abd.; Salleh, Fauzani Md.; Mohamed, Nor Sabirin

2017-12-01

The effect of graphene composition on the conductivity and stability of polymer composite solid electrolyte was studied. These polymer composite solid electrolytes were synthesized by sol gel method and prepared via the solution-casting technique. The compositions of graphene were varied between 10 wt% to 70 wt%. The changes in the functional group of polymer composite after the addition of graphene were characterized by Fourier Transform InfraRed spectroscopy. Electrochemical impedance spectroscopy was conducted at ambient temperature in the frequency range of 10 Hz to 1 MHz to study the conductivity of the polymer composite. The highest conductivity was obtained at 60 wt% graphene with the value of 2.85×10-4 Scm-1. Sample without the addition of graphene showed the lowest conductivity value of 1.77×10-7 Scm-1 and acts as an insulator. The high conductivity at 60 wt% graphene loading is related to dehydration of cellulose. This is supported by the FTIR spectrum where the absorption peaks of C-O stretching vibrations of polymer composite is weakened and the hydroxyl group is slightly shifted compared to the FTIR spectrum without the addition of graphene. Linear sweep voltammetry results demonstrated that the polymer composite solid electrolyte exhibited electrochemical stability up to 3.2 V.

Optimizing Glassy Polymer Network Morphology for Nano-particle Dispersion, Stabilization and Performance

Science.gov (United States)

2016-10-03

viscosity and stabilization of MWCNTs within rheological regimes which inhibit re-agglomeration to aid in post processing stabilization of dispersion state...polypropylene- clay nanocomposites subjected to laser pulse heating Bartolucci, Stephen, Supan, Karen, Wiggins, Jeffrey, LaBeaud, Lawrence, Warrender...addition, concurrent chain extension reactions advance prepolymer molecular weights to desired viscosities in less than 2 minutes of mean residence

Global exponential stability and periodicity of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo; Lu Linji

2009-01-01

In this paper, global exponential stability and periodicity of a class of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.

Existence and globally exponential stability of equilibrium for BAM neural networks with impulses

International Nuclear Information System (INIS)

Xia Yonghui; Huang Zhenkun; Han Maoan

2008-01-01

In this paper, a class of two-layer heteroassociative networks called bidirectional associative memory (BAM) networks with impulses is studied. Some new sufficient conditions are established for the existence and globally exponential stability of a unique equilibrium, which generalize and improve the previously known results. The sufficient conditions are easy to verify and when the impulsive jumps are absent the results reduce to those of the non-impulsive systems. The approaches are based on employing Banach's fixed point theorem, matrix theory and its spectral theory. Our results generalize and significantly improve the previous known results due to this method. Examples are given to show the feasibility and effectiveness of our results

Storage stability of milk-cans with the addition of maltodextrin

Directory of Open Access Journals (Sweden)

L. V. Golubeva

2016-01-01

Full Text Available The paper presents data on the study of the safety milk-containing canned food. The examine sample has multicomponent non-standard composition. The part of components is presented by food additives of a plant origin. The composition of product was varied for improvement of its properties and increase of storage stability. Starch treacly additives were widely expansion in the dairy industry. In milk-containing tinned was decided to replace partially a sweet component – sugar with maltodextrin. As maltodextrin possesses less sweet taste, the basic component was replaced partially. The manufacturing process of the product involves mixing the dry components in order to obtain the desired properties. The technology excludes process of evaporation and use of the expensive volume equipment. For comparison of properties of development investigated a reference template. The reference template s a product of a classical formulation which includes the origin of the components of milk and sugar. Organoleptic properties are studied of two samples. Defined that taste, a smell, appearance and a consistence of development and a control sample are similar on the first periods of storage. However, the consistence of a control sample is aggravating after 6 months of storage. In process of investigation is determined such parameters as the water activity, the dynamic viscosity and the active acidity. Values of these indicators for a new product are in rated limits, but they are much lower than the reference. Carried out the analysis of the received results. We concluded that milkcontaining tinned with maltodextrin have a high storage stability and better performance compared to the control sample.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

Science.gov (United States)

Vitela, Javier E.; Martinell, Julio J.

1998-02-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

International Nuclear Information System (INIS)

Vitela, J.E.; Martinell, J.J.

1998-01-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN. (author)

The design of multi-lead-compensators for stabilization and pole placement in double-integrator networks

NARCIS (Netherlands)

Wan, Yan; Roy, Sandip; Saberi, Ali; Stoorvogel, Antonie Arij

2010-01-01

We study decentralized controller design for stabilization and pole-placement, in a network of autonomous agents with double-integrator internal dynamics and arbitrary observation topology. We show that a simple multi-lead-compensator architecture, in particular one in which each agent uses a

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

Science.gov (United States)

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Globally exponential stability and periodic solutions of CNNS with variable coefficients and variable delays

International Nuclear Information System (INIS)

Liu Haifei; Wang Li

2006-01-01

In this Letter, by using the inequality method and the Lyapunov functional method, we analyze the globally exponential stability and the existence of periodic solutions of a class of cellular neutral networks with delays and variable coefficients. Some simple and new sufficient conditions ensuring the existence and uniqueness of globally exponential stability of periodic solutions for cellular neutral networks with variable coefficients and delays are obtained. In addition, one example is also worked out to illustrate our theory

Globally exponential stability and periodic solutions of CNNS with variable coefficients and variable delays

Energy Technology Data Exchange (ETDEWEB)

Liu Haifei [School of Management and Engineering, Nanjing University, Nanjing 210093 (China)]. E-mail: hfliu80@126.com; Wang Li [School of Management and Engineering, Nanjing University, Nanjing 210093 (China)

2006-09-15

In this Letter, by using the inequality method and the Lyapunov functional method, we analyze the globally exponential stability and the existence of periodic solutions of a class of cellular neutral networks with delays and variable coefficients. Some simple and new sufficient conditions ensuring the existence and uniqueness of globally exponential stability of periodic solutions for cellular neutral networks with variable coefficients and delays are obtained. In addition, one example is also worked out to illustrate our theory.

Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks

Science.gov (United States)

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-06-01

This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.

Topological and Graph-Coloring Conditions on the Parameter-Independent Stability of Second-Order Networked Systems

NARCIS (Netherlands)

Koerts, Filip; Bürger, Mathias; van der Schaft, Abraham; De Persis, Claudio

2017-01-01

In this paper, we study parameter-independent stability in qualitatively heterogeneous passive networked systems containing damped and undamped nodes. Given the graph topology and a set of damped nodes, we ask if output consensus is achieved for all system parameter values. For given parameter

Learning State Space Dynamics in Recurrent Networks

Science.gov (United States)

Simard, Patrice Yvon

Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

International Nuclear Information System (INIS)

Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

2012-01-01

The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

Directory of Open Access Journals (Sweden)

Yongkun Li

2009-01-01

Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.

Machine-Learning Based Channel Quality and Stability Estimation for Stream-Based Multichannel Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Waqas Rehan

2016-09-01

Full Text Available Wireless sensor networks (WSNs have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM, that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI and the average of the link quality indicator (LQI of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC algorithm in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC algorithm, that can perform channel quality estimation on the basis of both current and past values of channel rank estimation

Machine-Learning Based Channel Quality and Stability Estimation for Stream-Based Multichannel Wireless Sensor Networks.

Science.gov (United States)

Rehan, Waqas; Fischer, Stefan; Rehan, Maaz

2016-09-12

Wireless sensor networks (WSNs) have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM), that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI) and the average of the link quality indicator (LQI) of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC) algorithm) in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC) algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC) algorithm), that can perform channel quality estimation on the basis of both current and past values of channel rank estimation. In the end

Energy Optimization for Distributed Energy Resources Scheduling with Enhancements in Voltage Stability Margin

DEFF Research Database (Denmark)

Morais, Hugo; Sousa, Tiago; Perez, Angel

2016-01-01

to evaluate the resulting multiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programming methodology. With this new methodology, the system operators can consider both the costs and voltage stability. Priority can be assigned to one objective function according...... to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks. One detailed case study considering a distribution network with high penetration of distributed energy...

Adjusting neural additional stabilizers for damping interarea oscillations; Ajuste de estabilizadores suplementares neurais para amortecimento de oscilacoes interareas

Energy Technology Data Exchange (ETDEWEB)

Furini, M.A.; Araujo, P.B. de; Pereira, A.L.S. [Universidade Estadual Paulista (FEIS/UNESP), Ilha Solteira, SP (Brazil). Fac. de Engenharia. Dept. Engenharia Eletrica], Emails: mafurini@aluno.feis.unesp.br, percival@dee.feis.unesp.br, andspa@gmail.com

2009-07-01

This paper aims at analyzing the main operation and design of operationally robust controllers in order to damp the electromechanics oscillations type inter area. For this we used an intelligent control technique based on artificial neural networks, where a multilayer perceptron it was implemented. We used a symmetrical test system of four generators, ten bars and nine transmission lines to verify the performance of the power system stabilizers and power oscillation damping (POD) for the FACTS devices, unified power flow controller (UPFC), designed for neural networks. The results show the superiority in the operation and control of oscillations in power systems using UPFC equipped with the POD.

The Influence of Palm Oil Addition on Sunflower Halva Stability and Texture

Directory of Open Access Journals (Sweden)

Vlad Muresan

2014-05-01

Full Text Available Halva is one of the most popular food products of Middle Eastern and North African countries. Worldwide, the most known halva contains roasted sesame seeds, while usually in Eastern European countries the most appreciated is sunflower halva. However, even if sunflower halva is an appreciated product, it has currently a quality below the expectations of the new generation of consumers. Sunflower halva main issue is caused by the oil which separates at the surface during storage, determining a low commercial aspect of the product. Thus, the aim of this work was to assess the influence of palm oil addition on sunflower halva stability and texture. Five samples containing different oil percentages [w/w] (1%, 2%, 3%, 4%, 5% were prepared, as well as a control sample prepared using the standard recipe (no palm oil added. The texture of all samples was analyzed by an instrumental method, while the colloidal stability was determined by a gravimetric technique during 40 days of storage at two different temperatures (1-2ºC and 15-20ºC. After the storage test at 1-2oC, there were not significant differences between the samples, for all palm oil containing samples as well as for control the percentages of separated oil being below 0.6%. With respect to the control sample, the halva samples containing 1%, 4% and 5% of palm oil showed a decrease in their stability, while samples containing 2% and 3% showed an improved stability (3.44% and 1.78% of separated oil. During this study it was established that the sample containing 3% palm oil was the most favorable, regarding its textural properties, as well as its colloidal stability. 

Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays.

Science.gov (United States)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Huang, Tingwen; Alsaadi, Fuad E

2018-06-01

This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effects of gliadin addition on the rheological, microscopic and thermal characteristics of wheat gluten.

Science.gov (United States)

Khatkar, B S; Barak, Sheweta; Mudgil, Deepak

2013-02-01

In the present study, micro-structural, thermal and rheological changes in the gluten network upon addition of gliadins at 5% and 10% levels were investigated using scanning electron microscopy (SEM), thermo gravimetric analysis (TGA), differential scanning calorimetry (DSC) and dynamic rheometry. The addition of gliadins decreased the peak dough height inferring decrease in dough strength. The dough stability also decreased from 3.20 to 1.40 min upon addition of 10% gliadin to the base flour. The TGA profile and the glass transition behavior of the control gluten and gluten obtained from dough with gliadin added at 5% and 10% levels showed decrease in thermal stability. The SEM micrograph of the control gluten showed foam like protein matrix. As the gliadin percentage in the gluten was increased, the compactness of the gluten structure reduced considerably leading to the formation of a more open weak gluten network. Copyright © 2012 Elsevier B.V. All rights reserved.

Microstructure and thermal stability of nickel layers electrodeposited from an additive-free sulphamate-based electrolyte

DEFF Research Database (Denmark)

Rasmussen, Anette Alsted; Møller, Per; Somers, Marcel A. J.

2006-01-01

and scanning electron microscopy and X-ray diffraction; the Vickers hardness was measured in cross sections. The present is meant as a reference for forthcoming articles on the investigation of various strengthening mechanisms on the microstructure, hardness and thermal stability of Ni (alloys) electrodeposits.......The influences of the current density and the temperature on the microstructure and hardness of Ni layers electrodeposited from an additive-free sulphamate bath were investigated. The microstructure and thermal stability of the electrodeposits was investigated with a combination of transmission...

Influence of scandium addition on the high-temperature grain size stabilization of oxide-dispersion-strengthened (ODS) ferritic alloy

Energy Technology Data Exchange (ETDEWEB)

Li, Lulu, E-mail: lli18@ncsu.edu; Xu, Weizong; Saber, Mostafa; Zhu, Yuntian; Koch, Carl C.; Scattergood, Ronald O.

2015-06-11

The influence of 1–4 at% Sc addition on the thermal stability of mechanically alloyed ODS ferritic alloy was studied in this work. Sc addition was found to significantly stabilize grain size and microhardness at high temperatures. Grain sizes of samples with 1 and 4 at% Sc was found maintained in the nanoscale range at temperatures up to 1000 °C with hardness maintained at 5.6 and 6.7 GPa, respectively. The detailed microstructure was also investigated from EDS elemental mapping, where nanofeatures [ScTiO] were observed, while nanosized [YTiO] particles were rarely seen. This is probably due to the concentration difference between Sc and Y, leading to the formation of [ScTiO] favoring that of [YTiO]. Precipitation was considered as the major source for the observed high temperature stabilization. In addition, 14YT–Sc alloys without large second phases such as Ti-oxide can exhibit better performance compared to conventional ODS materials.

Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Li Zuoan; Li Kelin

2009-01-01

In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Qiankun Song

2007-06-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Cao Jinde

2007-01-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling

Science.gov (United States)

Veltz, Romain; Sejnowski, Terrence J.

2016-01-01

Inhibition-stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from inhibitory interneurons, a circuit element found in the hippocampus and the primary visual cortex. In their working regime, ISNs produce damped oscillations in the γ-range in response to inputs to the inhibitory population. In order to understand the properties of interconnected ISNs, we investigated periodic forcing of ISNs. We show that ISNs can be excited over a range of frequencies and derive properties of the resonance peaks. In particular, we studied the phase-locked solutions, the torus solutions, and the resonance peaks. Periodically forced ISNs respond with (possibly multistable) phase-locked activity, whereas networks with sustained intrinsic oscillations respond more dynamically to periodic inputs with tori. Hence, the dynamics are surprisingly rich, and phase effects alone do not adequately describe the network response. This strengthens the importance of phaseamplitude coupling as opposed to phase-phase coupling in providing multiple frequencies for multiplexing and routing information. PMID:26496044

Automatic approach to stabilization and control for multi robot teams by multilayer network operator

Directory of Open Access Journals (Sweden)

Diveev Askhat

2016-01-01

Full Text Available The paper describes a novel methodology for synthesis a high-level control of autonomous multi robot teams. The approach is based on multilayer network operator method that belongs to a symbolic regression class. Synthesis is accomplished in three steps: stabilizing robots about some given position in a state space, finding optimal trajectories of robots’ motion as sets of stabilizing points and then approximating all the points of optimal trajectories by some multi-dimensional function of state variables. The feasibility and effectiveness of the proposed approach is verified on simulations of the task of control synthesis for three mobile robots parking in the constrained space.

Stability in Cohen Grossberg-type bidirectional associative memory neural networks with time-varying delays

Science.gov (United States)

Cao, Jinde; Song, Qiankun

2006-07-01

In this paper, the exponential stability problem is investigated for a class of Cohen-Grossberg-type bidirectional associative memory neural networks with time-varying delays. By using the analysis method, inequality technique and the properties of an M-matrix, several novel sufficient conditions ensuring the existence, uniqueness and global exponential stability of the equilibrium point are derived. Moreover, the exponential convergence rate is estimated. The obtained results are less restrictive than those given in the earlier literature, and the boundedness and differentiability of the activation functions and differentiability of the time-varying delays are removed. Two examples with their simulations are given to show the effectiveness of the obtained results.

Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stability.

Science.gov (United States)

Barroso, L M A; Teodoro, P E; Nascimento, M; Torres, F E; Nascimento, A C C; Azevedo, C F; Teixeira, F R F

2016-11-03

Cowpea (Vigna unguiculata) is grown in three Brazilian regions: the Midwest, North, and Northeast, and is consumed by people on low incomes. It is important to investigate the genotype x environment (GE) interaction to provide accurate recommendations for farmers. The aim of this study was to identify cowpea genotypes with high adaptability and phenotypic stability for growing in the Brazilian Cerrado, and to compare the use of artificial neural networks with the Eberhart and Russell (1966) method. Six trials with upright cowpea genotypes were conducted in 2005 and 2006 in the States of Mato Grosso do Sul and Mato Grosso. The data were subjected to adaptability and stability analysis by the Eberhart and Russell (1966) method and artificial neural networks. The genotypes MNC99-537F-4 and EVX91-2E-2 provided grain yields above the overall environment means, and exhibited high stability according to both methods. Genotype IT93K-93-10 was the most suitable for unfavorable environments. There was a high correlation between the results of both methods in terms of classifying the genotypes by their adaptability and stability. Therefore, this new approach would be effective in quantifying the GE interaction in upright cowpea breeding programs.

Pinning Control Strategy of Multicommunity Structure Networks

Directory of Open Access Journals (Sweden)

Chao Ding

2017-01-01

Full Text Available In order to investigate the effects of community structure on synchronization, a pinning control strategy is researched in a class of complex networks with community structure in this paper. A feedback control law is designed based on the network community structure information. The stability condition is given and proved by using Lyapunov stability theory. Our research shows that as to community structure networks, there being a threshold hT≈5, when coupling strength bellows this threshold, the stronger coupling strength corresponds to higher synchronizability; vice versa, the stronger coupling strength brings lower synchronizability. In addition the synchronizability of overlapping and nonoverlapping community structure networks was simulated and analyzed; while the nodes were controlled randomly and intensively, the results show that intensive control strategy is better than the random one. The network will reach synchronization easily when the node with largest betweenness was controlled. Furthermore, four difference networks’ synchronizability, such as Barabási-Albert network, Watts-Strogatz network, Erdös-Rényi network, and community structure network, are simulated; the research shows that the community structure network is more easily synchronized under the same control strength.

Influence of dispersing additives and blend composition on stability of marine high-viscosity fuels

Directory of Open Access Journals (Sweden)

Т. Н. Митусова

2017-12-01

Full Text Available The article offers a definition of the stability of marine high-viscosity fuel from the point of view of the colloid-chemical concept of oil dispersed systems. The necessity and importance of the inclusion in the current regulatory requirements of this quality parameter of high-viscosity marine fuel is indicated. The objects of the research are high-viscosity marine fuels, the basic components of which are heavy oil residues: fuel oil that is the atmospheric residue of oil refining and viscosity breaking residue that is the product of light thermal cracking of fuel oil. As a thinning agent or distillate component, a light gas oil was taken from the catalytic cracking unit. The stability of the obtained samples was determined through the xylene equivalent index, which characterizes the stability of marine high-viscosity fuel to lamination during storage, transportation and operation processes. To improve performance, the resulting base compositions of high-viscosity marine fuels were modified by introducing small concentrations (0.05 % by weight of stabilizing additives based on oxyethylated amines of domestic origin and alkyl naphthalenes of foreign origin.

Dual Function Additives: A Small Molecule Crosslinker for Enhanced Efficiency and Stability in Organic Solar Cells

KAUST Repository

Rumer, Joseph W.; Ashraf, Raja S.; Eisenmenger, Nancy D.; Huang, Zhenggang; Meager, Iain; Nielsen, Christian B.; Schroeder, Bob C.; Chabinyc, Michael L.; McCulloch, Iain

2015-01-01

A bis-azide-based small molecule crosslinker is synthesized and evaluated as both a stabilizing and efficiency-boosting additive in bulk heterojunction organic photovoltaic cells. Activated by a noninvasive and scalable solution processing technique, polymer:fullerene blends exhibit improved thermal stability with suppressed polymer skin formation at the cathode and frustrated fullerene aggregation on ageing, with initial efficiency increased from 6% to 7%. © 2015 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dual Function Additives: A Small Molecule Crosslinker for Enhanced Efficiency and Stability in Organic Solar Cells

KAUST Repository

Rumer, Joseph W.

2015-02-01

A bis-azide-based small molecule crosslinker is synthesized and evaluated as both a stabilizing and efficiency-boosting additive in bulk heterojunction organic photovoltaic cells. Activated by a noninvasive and scalable solution processing technique, polymer:fullerene blends exhibit improved thermal stability with suppressed polymer skin formation at the cathode and frustrated fullerene aggregation on ageing, with initial efficiency increased from 6% to 7%. © 2015 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Existence and Globally Asymptotic Stability of Equilibrium Solution for Fractional-Order Hybrid BAM Neural Networks with Distributed Delays and Impulses

Directory of Open Access Journals (Sweden)

Hai Zhang

2017-01-01

Full Text Available This paper investigates the existence and globally asymptotic stability of equilibrium solution for Riemann-Liouville fractional-order hybrid BAM neural networks with distributed delays and impulses. The factors of such network systems including the distributed delays, impulsive effects, and two different fractional-order derivatives between the U-layer and V-layer are taken into account synchronously. Based on the contraction mapping principle, the sufficient conditions are derived to ensure the existence and uniqueness of the equilibrium solution for such network systems. By constructing a novel Lyapunov functional composed of fractional integral and definite integral terms, the globally asymptotic stability criteria of the equilibrium solution are obtained, which are dependent on the order of fractional derivative and network parameters. The advantage of our constructed method is that one may directly calculate integer-order derivative of the Lyapunov functional. A numerical example is also presented to show the validity and feasibility of the theoretical results.

[Influence of autoclave sterilization on dimensional stability and detail reproduction of 5 additional silicone impression materials].

Science.gov (United States)

Xu, Tong-kai; Sun, Zhi-hui; Jiang, Yong

2012-03-01

To evaluate the dimensional stability and detail reproduction of five additional silicone impression materials after autoclave sterilization. Impressions were made on the ISO 4823 standard mold containing several marking lines, in five kinds of additional silicone. All the impressions were sterilized by high temperature and pressure (135 °C, 212.8 kPa) for 25 min. Linear measurements of pre-sterilization and post-sterilization were made with a measuring microscope. Statistical analysis utilized single-factor analysis with pair-wise comparison of mean values when appropriate. Hypothesis testing was conducted at alpha = 0.05. No significant difference was found between the pre-sterilization and post-sterilization conditions for all locations, and all the absolute valuse of linear rate of change less than 8%. All the sterilization by the autoclave did not affect the surfuce detail reproduction of the 5 impression materials. The dimensional stability and detail reproduction of the five additional silicone impression materials in the study was unaffected by autoclave sterilization.

Strong-stability-preserving additive linear multistep methods

KAUST Repository

Hadjimichael, Yiannis; Ketcheson, David I.

2018-01-01

The analysis of strong-stability-preserving (SSP) linear multistep methods is extended to semi-discretized problems for which different terms on the right-hand side satisfy different forward Euler (or circle) conditions. Optimal perturbed

Non-additive dissipation in open quantum networks out of equilibrium

Science.gov (United States)

Mitchison, Mark T.; Plenio, Martin B.

2018-03-01

We theoretically study a simple non-equilibrium quantum network whose dynamics can be expressed and exactly solved in terms of a time-local master equation. Specifically, we consider a pair of coupled fermionic modes, each one locally exchanging energy and particles with an independent, macroscopic thermal reservoir. We show that the generator of the asymptotic master equation is not additive, i.e. it cannot be expressed as a sum of contributions describing the action of each reservoir alone. Instead, we identify an additional interference term that generates coherences in the energy eigenbasis, associated with the current of conserved particles flowing in the steady state. Notably, non-additivity arises even for wide-band reservoirs coupled arbitrarily weakly to the system. Our results shed light on the non-trivial interplay between multiple thermal noise sources in modular open quantum systems.

The Critical Choice of PEDOT: PSS Additives for Long Term Stability of Roll‐to‐Roll Processed OPVs

DEFF Research Database (Denmark)

Roth, Bérenger; Benatto, Gisele Alves dos Reis; Corazza, Michael

2015-01-01

The impact of additives mixed with poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) on the stability of organic photovoltaic modules is investigated for fully ambient roll-to-roll (R2R) processed indium tin oxide free modules. Four different PEDOT:PSS inks from two different...... on organic photovoltaic stability (ISOS). For the harsh indoor test (ISOS-L-3) only a slight difference in stability is observed between the different modules. During both ISOS-L-3 and ISOS-D-3 one new failure mode is observed as a result of tiny air inclusions in the barrier foil and a R2R method...

Precessing rotating flows with additional shear: stability analysis.

Science.gov (United States)

Salhi, A; Cambon, C

2009-03-01

We consider unbounded precessing rotating flows in which vertical or horizontal shear is induced by the interaction between the solid-body rotation (with angular velocity Omega(0)) and the additional "precessing" Coriolis force (with angular velocity -epsilonOmega(0)), normal to it. A "weak" shear flow, with rate 2epsilon of the same order of the Poincaré "small" ratio epsilon , is needed for balancing the gyroscopic torque, so that the whole flow satisfies Euler's equations in the precessing frame (the so-called admissibility conditions). The base flow case with vertical shear (its cross-gradient direction is aligned with the main angular velocity) corresponds to Mahalov's [Phys. Fluids A 5, 891 (1993)] precessing infinite cylinder base flow (ignoring boundary conditions), while the base flow case with horizontal shear (its cross-gradient direction is normal to both main and precessing angular velocities) corresponds to the unbounded precessing rotating shear flow considered by Kerswell [Geophys. Astrophys. Fluid Dyn. 72, 107 (1993)]. We show that both these base flows satisfy the admissibility conditions and can support disturbances in terms of advected Fourier modes. Because the admissibility conditions cannot select one case with respect to the other, a more physical derivation is sought: Both flows are deduced from Poincaré's [Bull. Astron. 27, 321 (1910)] basic state of a precessing spheroidal container, in the limit of small epsilon . A Rapid distortion theory (RDT) type of stability analysis is then performed for the previously mentioned disturbances, for both base flows. The stability analysis of the Kerswell base flow, using Floquet's theory, is recovered, and its counterpart for the Mahalov base flow is presented. Typical growth rates are found to be the same for both flows at very small epsilon , but significant differences are obtained regarding growth rates and widths of instability bands, if larger epsilon values, up to 0.2, are considered. Finally

Global stability of almost periodic solution of shunting inhibitory cellular neural networks with variable coefficients

International Nuclear Information System (INIS)

Chen Ling; Zhao Hongyong

2008-01-01

The paper investigates the almost periodicity of shunting inhibitory cellular neural networks with delays and variable coefficients. Several sufficient conditions are established for the existence and globally exponential stability of almost periodic solutions by employing fixed point theorem and differential inequality technique. The results of this paper are new and they complement previously known results

Controlling Voltage Levels of Distribution Network-Radial Feeder after Connecting Wind Turbines to the Network

Directory of Open Access Journals (Sweden)

Muhammad Al Badri

2017-11-01

Full Text Available Several factors in power generation and supply need to be taken into account such as shortages of energy supply, system stability, and energy quality and system disruption due to network losses, industrial development and population expansion. The addition of wind turbines to the distribution network is of great benefit in providing additional power and solving these problems, but this addition is accompanied by the problem of low voltage network. This research found optimal solutions to the problem of low voltage distribution network after connecting wind turbines. The main idea of this paper is to optimize the low-voltage problem as a result of connecting the wind turbines to the "far end" of the radial feeder for a distribution network and to obtain a voltage level within an acceptable and stable range. The problem of low voltage solved by using the load-drop compensation, capacitor-bank and “doubly-fed” induction generators. The results of this study were based on the operation of the entire design of the simulation system which would be compatible with the reality of the energy flow of all network components by using the PSCAD program. The present analysis program revealed an optimum solution for the low voltage profile of the distribution network after connecting the wind turbine.

Delay-dependent stability of neural networks of neutral type with time delay in the leakage term

International Nuclear Information System (INIS)

Li, Xiaodi; Cao, Jinde

2010-01-01

This paper studies the global asymptotic stability of neural networks of neutral type with mixed delays. The mixed delays include constant delay in the leakage term (i.e. 'leakage delay'), time-varying delays and continuously distributed delays. Based on the topological degree theory, Lyapunov method and linear matrix inequality (LMI) approach, some sufficient conditions are derived ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point, which are dependent on both the discrete and distributed time delays. These conditions are expressed in terms of LMI and can be easily checked by the MATLAB LMI toolbox. Even if there is no leakage delay, the obtained results are less restrictive than some recent works. It can be applied to neural networks of neutral type with activation functions without assuming their boundedness, monotonicity or differentiability. Moreover, the differentiability of the time-varying delay in the non-neutral term is removed. Finally, two numerical examples are given to show the effectiveness of the proposed method

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Science.gov (United States)

Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian

2018-06-01

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.

Poly(vinylbenzylchloride) Based Anion-Exchange Blend Membranes (AEBMs): Influence of PEG Additive on Conductivity and Stability.

Science.gov (United States)

Kerres, Jochen A; Krieg, Henning M

2017-06-16

In view of the many possible applications such as fuel cells and electrolysers, recent interest in novel anion exchange membranes (AEMs) has increased significantly. However, their low conductivity and chemical stability limits their current suitability. In this study, the synthesis and characterization of several three- and four-component anion exchange blend membranes (AEBMs) is described, where the compositions have been systematically varied to study the influence of the AEBM's composition on the anion conductivities as well as chemical and thermal stabilities under strongly alkaline conditions. It was shown that the epoxide-functionalized poly(ethylene glycol)s that were introduced into the four-component AEBMs resulted in increased conductivity as well as a marked improvement in the stability of the AEBMs in an alkaline environment. In addition, the thermal stability of the novel AEBMs was excellent showing the suitability of these membranes for several electrochemical applications.

A polarization stabilizer up to 12.6 krad/s with an additional function of stable state of polarization transformation

International Nuclear Information System (INIS)

Xiao-Guang, Zhang; Guang-Qing, Fang; Xin-Yuan, Zhao; Wen-Bo, Zhang; Li-Xia, Xi; Qian-Jin, Xiong; Xi-Xiang, Li; Guang-Yong, Zhang

2010-01-01

This paper reports on an experiment about a novel method of polarization stabilization. The polarization stabilizer proposed here has an additional function of polarization transformation from any state of polarization into any others. The particle swarm optimization is introduced as a control algorithm in the process of either searching or endless tracking. The tracking speed of the stabilizer is obtained up to 12.6 krad/s by using hardware we have in the laboratory, which means that we can achieve a higher speed practical polarization stabilizer if we have faster hardware. (classical areas of phenomenology)

Small-world networks exhibit pronounced intermittent synchronization

Science.gov (United States)

Choudhary, Anshul; Mitra, Chiranjit; Kohar, Vivek; Sinha, Sudeshna; Kurths, Jürgen

2017-11-01

We report the phenomenon of temporally intermittently synchronized and desynchronized dynamics in Watts-Strogatz networks of chaotic Rössler oscillators. We consider topologies for which the master stability function (MSF) predicts stable synchronized behaviour, as the rewiring probability (p) is tuned from 0 to 1. MSF essentially utilizes the largest non-zero Lyapunov exponent transversal to the synchronization manifold in making stability considerations, thereby ignoring the other Lyapunov exponents. However, for an N-node networked dynamical system, we observe that the difference in its Lyapunov spectra (corresponding to the N - 1 directions transversal to the synchronization manifold) is crucial and serves as an indicator of the presence of intermittently synchronized behaviour. In addition to the linear stability-based (MSF) analysis, we further provide global stability estimate in terms of the fraction of state-space volume shared by the intermittently synchronized state, as p is varied from 0 to 1. This fraction becomes appreciably large in the small-world regime, which is surprising, since this limit has been otherwise considered optimal for synchronized dynamics. Finally, we characterize the nature of the observed intermittency and its dominance in state-space as network rewiring probability (p) is varied.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network.

Science.gov (United States)

Jadi, Monika P; Sejnowski, Terrence J

2014-04-21

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Effect of small addition of Cr on stability of retained austenite in high carbon steel

Energy Technology Data Exchange (ETDEWEB)

Hossain, Rumana; Pahlevani, Farshid, E-mail: f.pahlevani@unsw.edu.au; Sahajwalla, Veena

2017-03-15

High carbon steels with dual phase structures of martensite and austenite have considerable potential for industrial application in high abrasion environments due to their hardness, strength and relatively low cost. To design cost effective high carbon steels with superior properties, it is crucial to identify the effect of Chromium (Cr) on the stability of retained austenite (RA) and to fully understand its effect on solid-state phase transition. This study addresses this important knowledge gap. Using standard compression tests on bulk material, quantitative X-ray diffraction analysis, nano-indentation on individual austenitic grains, transmission electron microscopy and electron backscatter diffraction–based orientation microscopy techniques, the authors investigated the effect of Cr on the microstructure, transformation behaviour and mechanical stability of retained austenite in high carbon steel, with varying Cr contents. The results revealed that increasing the Cr %, altered the morphology of the RA and increased its stability, consequently, increasing the critical pressure for martensitic transformation. This study has critically addressed the elastoplastic behaviour of retained austenite – and provides a deep understanding of the effect of small additions of Cr on the metastable austenite of high carbon steel from the macro- to nano-level. Consequently, it paves the way for new applications for high carbon low alloy steels. - Highlights: • Effect of small addition of Cr on metastable austenite of high carbon steel from the macro- to nano-level • A multi-scale study of elastoplastic behaviour of retained austenite in high carbon steel • The mechanical stability of retained austenite during plastic deformation increased with increasing Cr content • Effect of grain boundary misorientation angle on hardness of individual retained austenite grains in high carbon steel.

Industrial Wastes as Auxiliary Additives to Cement/Lime Stabilization of Soils

OpenAIRE

James, Jijo; Pandian, P. Kasinatha

2016-01-01

Chemical stabilization involves the use of chemical agents for initiating reactions within the soil for modification of its geotechnical properties. Cement and lime stabilization have been the most common stabilization methods adopted for soil treatment. Cement stabilization results in good compressive strengths and is preferred for cohesionless to moderately cohesive soil but loses effectiveness when the soil is highly plastic. Lime stabilization is the most preferred method for plastic clay...

The design of multi-lead-compensators for stabilization and pole placement in double-integrator networks under saturation

NARCIS (Netherlands)

Wan, Yan; Roy, Sandip; Saberi, Ali; Stoorvogel, Antonie Arij

2009-01-01

We study decentralized controller design for stabilization and pole-placement, in a network of autonomous agents with double-integrator internal dynamics and arbitrary observation topology. We show that a simple multi-lead-compensator architecture, in particular one in which each agent uses a

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

International Nuclear Information System (INIS)

Liu, Yurong; Wang, Zidong; Serrano, Alan; Liu, Xiaohui

2007-01-01

This Letter is concerned with the analysis problem of exponential stability for a class of discrete-time recurrent neural networks (DRNNs) with time delays. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strict monotonic. Furthermore, the description of the activation functions is more general than the recently commonly used Lipschitz conditions. Under such mild conditions, we first prove the existence of the equilibrium point. Then, by employing a Lyapunov-Krasovskii functional, a unified linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the DRNNs to be globally exponentially stable. It is shown that the delayed DRNNs are globally exponentially stable if a certain LMI is solvable, where the feasibility of such an LMI can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition

Impact of silage additives on aerobic stability and characteristics of high-moisture maize during exposure to air, and on fermented liquid feed

DEFF Research Database (Denmark)

Canibe, Nuria; Kristensen, Niels Bastian; Jensen, Bent Borg

2014-01-01

during aeration- and impact of additives on the aerobic stability of HMM depended on the characteristics of the samples. No blooming of Enterobacteriaceae was observed in FLF containing c. 20 g HMM 100 g−1. Significance and Impact of the Study The impact of silage additives on aerobic stability of HMM...

Effect of SiO2 addition in the zirconia stabilization

International Nuclear Information System (INIS)

Pessoa, R.C.; Lima, U.R.; Nasar, M.C.; Nasar, R.S.; Yoshida, I.V.P.; Acchar, W.

2006-01-01

The aims of this work was investigated the zirconia stabilization with a mixture of resins based on silico nas promoting the Zr O 2 /Si O 2 formation. The powder was heated and characterized by TGA, DTA, FTIR, XRD and SEM/EDS. The results indicated the tetragonal and cubic phase formation stabilized at 1000 deg C/2 h. The increase of calcing temperature promoted decrease of stabilization. The amorphous silica calcined at 1000 deg C induced defects into the zirconia structure and favour the formation of more stable phases. The decrease of stabilization at high temperatures are related to growth of crystallite above of critical value. (author)

Stability Analysis of Continuous-Time and Discrete-Time Quaternion-Valued Neural Networks With Linear Threshold Neurons.

Science.gov (United States)

Chen, Xiaofeng; Song, Qiankun; Li, Zhongshan; Zhao, Zhenjiang; Liu, Yurong

2018-07-01

This paper addresses the problem of stability for continuous-time and discrete-time quaternion-valued neural networks (QVNNs) with linear threshold neurons. Applying the semidiscretization technique to the continuous-time QVNNs, the discrete-time analogs are obtained, which preserve the dynamical characteristics of their continuous-time counterparts. Via the plural decomposition method of quaternion, homeomorphic mapping theorem, as well as Lyapunov theorem, some sufficient conditions on the existence, uniqueness, and global asymptotical stability of the equilibrium point are derived for the continuous-time QVNNs and their discrete-time analogs, respectively. Furthermore, a uniform sufficient condition on the existence, uniqueness, and global asymptotical stability of the equilibrium point is obtained for both continuous-time QVNNs and their discrete-time version. Finally, two numerical examples are provided to substantiate the effectiveness of the proposed results.

Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm. (On-Line Harmonics Estimation Application

Directory of Open Access Journals (Sweden)

Eyad K Almaita

2017-03-01

Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application.  International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17

Passivity of memristive BAM neural networks with leakage and additive time-varying delays

Science.gov (United States)

Wang, Weiping; Wang, Meiqi; Luo, Xiong; Li, Lixiang; Zhao, Wenbing; Liu, Linlin; Ping, Yuan

2018-02-01

This paper investigates the passivity of memristive bidirectional associate memory neural networks (MBAMNNs) with leakage and additive time-varying delays. Based on some useful inequalities and appropriate Lyapunov-Krasovskii functionals (LKFs), several delay-dependent conditions for passivity performance are obtained in linear matrix inequalities (LMIs). Moreover, the leakage delays as well as additive delays are considered separately. Finally, numerical simulations are provided to demonstrate the feasibility of the theoretical results.

Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix

International Nuclear Information System (INIS)

Singh, Vimal

2007-01-01

The question of estimating the upper limit of -parallel B -parallel 2 , which is a key step in some recently reported global robust stability criteria for delayed neural networks, is revisited ( B denotes the delayed connection weight matrix). Recently, Cao, Huang, and Qu have given an estimate of the upper limit of -parallel B -parallel 2 . In the present paper, an alternative estimate of the upper limit of -parallel B -parallel 2 is highlighted. It is shown that the alternative estimate may yield some new global robust stability results

Global exponential stability of BAM neural networks with transmission delays and nonlinear impulses

International Nuclear Information System (INIS)

Huang Zhenkun; Xia Yonghui

2008-01-01

In this paper, a class of bidirectional associative memory (BAM) networks with transmission delays and nonlinear impulses are studied. Some new sufficient conditions are established for the existence and global exponential stability of a unique equilibrium, which generalize and improve the previously known results. The sufficient conditions are easy to verify and when the impulsive jumps are linear or absent the results reduce to those of common impulsive or non-impulsive systems. Finally, an example is given to show the feasibility and effectiveness of our results

Positron annihilation study of yttria-stabilized zirconia nanopowders containing Cr2O3 additive

International Nuclear Information System (INIS)

Prochazka, I; Cizek, J; Melikhova, O; Kuriplach, J; Konstantinova, T E; Danilenko, I A

2011-01-01

Yttria-stabilized zirconia compacted nanopowders, doped with trivalent chromium oxide, were studied by means of high-resolution positron lifetime and coincidence Doppler broadening techniques. The observed data suggest that positrons annihilate mainly in vacancylike defects at grain boundaries or in larger open volumes most likely located at triple points. The results also show that an addition of Cr 2 O 3 leads to a decrease in grain size.

Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

Science.gov (United States)

Wang, Dongshu; Huang, Lihong

2014-03-01

In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stability monitoring for BWR based on singular value decomposition method using artificial neural network

International Nuclear Information System (INIS)

Tsuji, Masashi; Shimazu, Yoichiro; Michishita, Hiroshi

2005-01-01

A new method for evaluating the decay ratios in a boiling water reactor (BWR) using the singular value decomposition (SVD) method had been proposed. In this method, a signal component closely related to the BWR stability can be extracted from independent components of the neutron noise signal decomposed by the SVD method. However, real-time stability monitoring by the SVD method requires an efficient procedure for screening such components. For efficient screening, an artificial neural network (ANN) with three layers was adopted. The trained ANN was actually applied to decomposed components of local power range monitor (LPRM) signals that were measured in stability experiments conducted in the Ringhals-1 BWR. In each LPRM signal, multiple candidates were screened from the decomposed components. However, decay ratios could be estimated by introducing appropriate criterions for selecting the most suitable component among the candidates. The estimated decay ratios are almost identical to those evaluated by visual screening in a previous study. The selected components commonly have the largest singular value, the largest decay ratio and the least squared fitting error among the candidates. By virtue of excellent screening performance of the trained ANN, the real-time stability monitoring by the SVD method can be applied in practice. (author)

Additional Tension Screws Improve Stability in Elastic Stable Intramedullary Nailing: Biomechanical Analysis of a Femur Spiral Fracture Model.

Science.gov (United States)

Zachert, Gregor; Rapp, Marion; Eggert, Rebecca; Schulze-Hessing, Maaike; Gros, Nina; Stratmann, Christina; Wendlandt, Robert; Kaiser, Martin M

2015-08-01

For pediatric femoral shaft fractures, elastic stable intramedullary nailing (ESIN) is an accepted method of treatment. But problems regarding stability with shortening or axial deviation are well known in complex fracture types and heavier children. Biomechanical in vitro testing was performed to determine whether two modified osteosyntheses with an additional tension screw fixation or screw fixation alone without nails could significantly improve the stability in comparison to classical ESIN. A total of 24 synthetic adolescent-sized femoral bone models (Sawbones, 4th generation; Vashon, Washington, United States) with an identical spiral fracture (length 100 mm) were used. All grafts underwent retrograde fixation with two C-shaped steel nails (2C). Of the 24, 8 osteosyntheses were supported by one additional tension screw (2C1S) and another 8 by two screws (2S) in which the intramedullary nails were removed before testing. Each configuration underwent biomechanical testing in 4-point bending, external rotation (ER) and internal rotation (IR). Furthermore, the modifications were tested in axial physiological 9 degrees position for shifting and dynamic compression as well as dynamic load. Both screw configurations (2C1S and 2S) demonstrated a significantly higher stability in comparison to the 2C configuration in 4-point bending (anterior-posterior, 0.95 Nm/mm [2C] spiral fracture model, the stability of ESIN could be significantly improved by two modifications with additional tension screws. If transferred in clinical practice, these modifications might offer earlier weight bearing and less problems of shortening or axial deviation. Georg Thieme Verlag KG Stuttgart · New York.

Development of real-time voltage stability monitoring tool for power system transmission network using Synchrophasor data

Science.gov (United States)

Pulok, Md Kamrul Hasan

Intelligent and effective monitoring of power system stability in control centers is one of the key issues in smart grid technology to prevent unwanted power system blackouts. Voltage stability analysis is one of the most important requirements for control center operation in smart grid era. With the advent of Phasor Measurement Unit (PMU) or Synchrophasor technology, real time monitoring of voltage stability of power system is now a reality. This work utilizes real-time PMU data to derive a voltage stability index to monitor the voltage stability related contingency situation in power systems. The developed tool uses PMU data to calculate voltage stability index that indicates relative closeness of the instability by producing numerical indices. The IEEE 39 bus, New England power system was modeled and run on a Real-time Digital Simulator that stream PMU data over the Internet using IEEE C37.118 protocol. A Phasor data concentrator (PDC) is setup that receives streaming PMU data and stores them in Microsoft SQL database server. Then the developed voltage stability monitoring (VSM) tool retrieves phasor measurement data from SQL server, performs real-time state estimation of the whole network, calculate voltage stability index, perform real-time ranking of most vulnerable transmission lines, and finally shows all the results in a graphical user interface. All these actions are done in near real-time. Control centers can easily monitor the systems condition by using this tool and can take precautionary actions if needed.

Effect of highly dispersed yttria addition on thermal stability of hydroxyapatite

International Nuclear Information System (INIS)

Parente, P.; Savoini, B.; Ferrari, B.; Monge, M.A.; Pareja, R.; Sanchez-Herencia, A.J.

2013-01-01

The capability of the colloidal method to produce yttria (Y 2 O 3 ) dispersed hydroxyapatite (HA) has been investigated as an alternative method to the conventional method of mechanical mixing and sintering for developing HA-based materials that could exhibit controllable and enhanced functional properties. A water based colloidal route to produce HA materials with highly dispersed Y 2 O 3 has been applied, and the effect of 10 wt.% Y 2 O 3 addition to HA investigated by thermal analysis, X-ray diffraction and Fourier transform infrared spectroscopy. These measurements evidence a remarkable effect of this Y 2 O 3 addition on decomposition mechanisms of synthetic HA. Results show that incorporation of Y 2 O 3 as dispersed second phase is beneficial because it hinders the decomposition mechanisms of HA into calcium phosphates. This retardation will allow the control of the sintering conditions for developing HA implants with improved properties. Besides, substitution of Ca 2+ with Y 3+ ions appears to promote the formation of OH − vacancies, which could improve the conductive properties of HA favorable to osseointegration. - Highlights: ► We reveal the influence of Y 2 O 3 on thermal stability of hydroxyapatite. ► Incorporation of Y 2 O 3 delays decomposition of hydroxyapatite to calcium phosphates. ► Addition of Y 2 O 3 enables sintering conditions more favorable to the densification.

Self-stabilizing Synchronization in Mobile Sensor Networks with Covering

Science.gov (United States)

Beauquier, Joffroy; Burman, Janna

Synchronization is widely considered as an important service in distributed systems which may simplify protocol design. Phase clock is a general synchronization tool that provides a form of a logical time. This paper presents a self-stabilizing (a tolerating state-corrupting transient faults) phase clock algorithm suited to the model of population protocols with covering. This model has been proposed recently for sensor networks with a very large, possibly unknown number of anonymous mobile agents having small memory. Agents interact in pairs in an asynchronous way subject to the constraints expressed in terms of the cover times of agents. The cover time expresses the "frequency" of an agent to communicate with all the others and abstracts agent's communication characteristics (e.g. moving speed/patterns, transmitting/receiving capabilities). We show that a phase clock is impossible in the model with only constant-state agents. Hence, we assume an existence of resource-unlimited agent - the base station.

The influence of additions of Al and Si on the lattice stability of fcc and hcp Fe-Mn random alloys

Energy Technology Data Exchange (ETDEWEB)

Gebhardt, T; Music, D; Schneider, J M [Materials Chemistry, RWTH Aachen University, D-52056 Aachen (Germany); Ekholm, M; Abrikosov, I A [Department of Physics, Chemistry and Biology (IFM), Linkoeping University, SE-58183 Linkoeping (Sweden); Vitos, L [Department of Materials and Engineering, Applied Materials Physics, Royal Institute of Technology (KTH), SE-10044 Stockholm (Sweden); Dick, A; Hickel, T; Neugebauer, J, E-mail: gebhardt@mch.rwth-aachen.de [Department of Computational Materials Design, Max-Planck-Institut fuer Eisenforschung GmbH, D-40237 Duesseldorf (Germany)

2011-06-22

We have studied the influence of additions of Al and Si on the lattice stability of face-centred-cubic (fcc) versus hexagonal-closed-packed (hcp) Fe-Mn random alloys, considering the influence of magnetism below and above the fcc Neel temperature. Employing two different ab initio approaches with respect to basis sets and treatment of magnetic and chemical disorder, we are able to quantify the predictive power of the ab initio methods. We find that the addition of Al strongly stabilizes the fcc lattice independent of the regarded magnetic states. For Si a much stronger dependence on magnetism is observed. Compared to Al, almost no volume change is observed as Si is added to Fe-Mn, indicating that the electronic contributions are responsible for stabilization/destabilization of the fcc phase.

The influence of additions of Al and Si on the lattice stability of fcc and hcp Fe-Mn random alloys

International Nuclear Information System (INIS)

Gebhardt, T; Music, D; Schneider, J M; Ekholm, M; Abrikosov, I A; Vitos, L; Dick, A; Hickel, T; Neugebauer, J

2011-01-01

We have studied the influence of additions of Al and Si on the lattice stability of face-centred-cubic (fcc) versus hexagonal-closed-packed (hcp) Fe-Mn random alloys, considering the influence of magnetism below and above the fcc Neel temperature. Employing two different ab initio approaches with respect to basis sets and treatment of magnetic and chemical disorder, we are able to quantify the predictive power of the ab initio methods. We find that the addition of Al strongly stabilizes the fcc lattice independent of the regarded magnetic states. For Si a much stronger dependence on magnetism is observed. Compared to Al, almost no volume change is observed as Si is added to Fe-Mn, indicating that the electronic contributions are responsible for stabilization/destabilization of the fcc phase.

The stability of the extended model of hypothalamic-pituitary-adrenal axis examined by stoichiometric network analysis

Science.gov (United States)

Marković, V. M.; Čupić, Ž.; Ivanović, A.; Kolar-Anić, Lj.

2011-12-01

Stoichiometric network analysis (SNA) represents a powerful mathematical tool for stability analysis of complex stoichiometric networks. Recently, the important improvement of the method has been made, according to which instability relations can be entirely expressed via reaction rates, instead of thus far used, in general case undefined, current rates. Such an improved SNA methodology was applied to the determination of exact instability conditions of the extended model of the hypothalamic-pituitary-adrenal (HPA) axis, a neuroendocrinological system, whose hormone concentrations exert complex oscillatory evolution. For emergence of oscillations, the Hopf bifurcation condition was utilized. Instability relations predicted by SNA showed good correlation with numerical simulation data of the HPA axis model.

Maximizing synchronizability of duplex networks

Science.gov (United States)

Wei, Xiang; Emenheiser, Jeffrey; Wu, Xiaoqun; Lu, Jun-an; D'Souza, Raissa M.

2018-01-01

We study the synchronizability of duplex networks formed by two randomly generated network layers with different patterns of interlayer node connections. According to the master stability function, we use the smallest nonzero eigenvalue and the eigenratio between the largest and the second smallest eigenvalues of supra-Laplacian matrices to characterize synchronizability on various duplexes. We find that the interlayer linking weight and linking fraction have a profound impact on synchronizability of duplex networks. The increasingly large inter-layer coupling weight is found to cause either decreasing or constant synchronizability for different classes of network dynamics. In addition, negative node degree correlation across interlayer links outperforms positive degree correlation when most interlayer links are present. The reverse is true when a few interlayer links are present. The numerical results and understanding based on these representative duplex networks are illustrative and instructive for building insights into maximizing synchronizability of more realistic multiplex networks.

Weak Links The Universal Key to the Stability of Networks and Complex Systems

CERN Document Server

Csermely, Peter

2009-01-01

How can our societies be stabilized in a crisis? Why can we enjoy and understand Shakespeare? Why are fruitflies uniform? How do omnivorous eating habits aid our survival? What makes the Mona Lisa’s smile beautiful? How do women keep our social structures intact? – Could there possibly be a single answer to all these questions? This book shows that the statement: "weak links stabilize complex systems" provides the key to understanding each of these intriguing puzzles, and many others too. The author (recipient of several distinguished science communication prizes) uses weak (low affinity, low probability) interactions as a thread to introduce a vast variety of networks from proteins to economics and ecosystems. Many people, from Nobel Laureates to high-school students have helped to make the book understandable to all interested readers. This unique book and the ideas it develops will have a significant impact on many, seemingly diverse, fields of study. A very personal, engaging, and unique book that wil...

Low silicon U(Al,Si)3 stabilization by Zr addition

International Nuclear Information System (INIS)

Pizarro, L.M.; Alonso, P.R.; Rubiolo, G.H.

2009-01-01

Previous knowledge states that (U,Zr)Al 3 and U(Al,Si) 3 phases with Zr and Si content higher than 6 at.% (7.7 wt%) and 4 at.% (1.4 wt%), respectively, does not partially transform to UAl 4 at 600 o C. In this work, four alloys within the quaternary system U-Al-Si-Zr were made with a fixed nominal 0.18 at.% (0.1 wt%) Si content in order to assess the synergetic effect of both Zr and Si alloying elements to the thermodynamic stability of the (U,Zr)(Al,Si) 3 phase. Heat treatments at 600 deg. C were undertaken and samples were analyzed by means of XRD, EPMA and EDS techniques. A remarkable conclusion is that addition of 0.3 at.% Si in the (U,Zr)(Al,Si) 3 phase reduces in 2.7 at.% the necessary Zr content to inhibit its transformation to U(Al,Si) 4 .

Nanocarbon networks for advanced rechargeable lithium batteries.

Science.gov (United States)

Xin, Sen; Guo, Yu-Guo; Wan, Li-Jun

2012-10-16

Carbon is one of the essential elements in energy storage. In rechargeable lithium batteries, researchers have considered many types of nanostructured carbons, such as carbon nanoparticles, carbon nanotubes, graphene, and nanoporous carbon, as anode materials and, especially, as key components for building advanced composite electrode materials. Nanocarbons can form efficient three-dimensional conducting networks that improve the performance of electrode materials suffering from the limited kinetics of lithium storage. Although the porous structure guarantees a fast migration of Li ions, the nanocarbon network can serve as an effective matrix for dispersing the active materials to prevent them from agglomerating. The nanocarbon network also affords an efficient electron pathway to provide better electrical contacts. Because of their structural stability and flexibility, nanocarbon networks can alleviate the stress and volume changes that occur in active materials during the Li insertion/extraction process. Through the elegant design of hierarchical electrode materials with nanocarbon networks, researchers can improve both the kinetic performance and the structural stability of the electrode material, which leads to optimal battery capacity, cycling stability, and rate capability. This Account summarizes recent progress in the structural design, chemical synthesis, and characterization of the electrochemical properties of nanocarbon networks for Li-ion batteries. In such systems, storage occurs primarily in the non-carbon components, while carbon acts as the conductor and as the structural buffer. We emphasize representative nanocarbon networks including those that use carbon nanotubes and graphene. We discuss the role of carbon in enhancing the performance of various electrode materials in areas such as Li storage, Li ion and electron transport, and structural stability during cycling. We especially highlight the use of graphene to construct the carbon conducting

LONG-TERM STABILITY OF THE LOCAL GROUND CONTROL NETWORK AT THE CO-LOCATION SITE OF MEDICINA

Science.gov (United States)

Abbondanza, C.; Sarti, P.; Legrand, J.

2009-12-01

ITRF combinations rely on the availability of accurate tie vectors linking reference points of space geodetic techniques. Co-located instruments are assumed to move consistently and no local relative motion is taken into account. Instabilities may degrade the quality of the co-location itself and perturb the result of ITRF combinations. This work aims to determine the stability of the local ground control network at Medicina (Italy) with independent surveying methods. The observatory hosts a co-location between a VLBI telescope and two GPS antennas, MEDI and MSEL. It is located in the Po Plain where thick layers of clays are the prevalent soil characteristics. Hence, provision of long term stability of geodetic monuments is a challenge and monitoring their stability is an issue. MEDI and the VLBI station regularly contribute to the determination of ITRF, while MSEL is part of the EUREF network. A set of five tie vectors observations linking the VLBI and MEDI reference points was acquired between 2001 and 2007. It is our main tool for performing local deformation analysis. Additionally, the GPS time series of MEDI and MSEL were used to cross check and confirm the local instability detected by terrestrial methods. To achieve a rigorous and reliable investigation of the local stability, multi-epoch terrestrial observations were homogeneously processed according to common parameterizations in a consistent reference frame. Similarly, continuous GPS observations from MEDI and MSEL were analysed according to the new EPN reprocessing strategy in order to monitor the short baseline between MEDI and MSEL; to spotlight any change in its length. Both approaches confirm differential motions at the site which can be related to monument instabilities originated by the particularly unfavourable local geological setting and the inapt design of the monuments foundation. The monuments move non homogeneously at rates reaching up to 1.6 mm/year, this value being comparable to intra

Studies for the stabilization of coal-oil mixtures. Final report, August 1978-May 1981

Energy Technology Data Exchange (ETDEWEB)

Botsaris, G.D.; Glazman, Y.M.; Adams-Viola, M.

1981-01-01

A fundamental understanding of the stabilization of coal-oil mixtures (COM) was developed. Aggregation of the coal particles was determined to control both the sedimentation and rheological properties of the COM. Sedimentation stability of COM prepared with coal, 80% < 200 mesh, is achieved by particle aggregation, which leads to the formation of a network of particles throughout the oil. The wettability of coal powders was evaluated by the Pickering emulsion test and a spherical agglomeration test to assess its effect on the stability of various COM formulations. Sedimentation stability of hydrophilic coal-oil-water mixtures (COWM) involves the formation of water bridges between the coal particles, while less stabilization of oleophilic COWM is achieved by the formation of an emulsion. Anionic SAA were least sensitive to the coal type and enhanced the aggregation stability of the suspension. The effect of cationic SAA, nonionic SAA and polymer additives depended upon the specific chemical structure of the SAA, the water content of the COM and the type of coal. The sedimentation stability of ultrafine COM was not directly due to the fineness of the powder but due to the formation of a network of flocculated particles.

Voltage Stability Control of Electrical Network Using Intelligent Load Shedding Strategy Based on Fuzzy Logic

Directory of Open Access Journals (Sweden)

Houda Jouini

2010-01-01

Full Text Available As a perspective to ensure the power system stability and to avoid the vulnerability leading to the blackouts, several preventive and curative means are adopted. In order to avoid the voltage collapse, load shedding schemes represent a suitable action to maintain the power system service quality and to control its vulnerability. In this paper, we try to propose an intelligent load shedding strategy as a new approach based on fuzzy controllers. This strategy was founded on the calculation of generated power sensitivity degree related to those injected at different network buses. During the fault phase, fuzzy controller algorithms generate monitor vectors ensuring a precalculated load shedding ratio in the purpose to reestablish the power balance and conduct the network to a new steady state.

Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

Directory of Open Access Journals (Sweden)

Juan Carlos Vasquez

2013-11-01

Full Text Available Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that report quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (‘in-degree’ and efferent (‘out-degree’ connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all presynaptic and postsynaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron ‘ring’ motif (1→2→3→1, can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future.Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics.

Freeze-Thaw Performance and Moisture-Induced Damage Resistance of Base Course Stabilized with Slow Setting Bitumen Emulsion-Portland Cement Additives

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Mojtaba Shojaei Baghini

2015-01-01

Full Text Available Freeze-thaw (FT cycles and moisture susceptibility are important factors influencing the geotechnical characteristics of soil-aggregates. Given the lack of published information on the behavior of cement-bitumen emulsion-treated base (CBETB under environmental conditions, especially freezing and thawing, this study investigated the effects of these additives on the CBETB performance. The primary goal was to evaluate the resistance of CBETB to moisture damage by performing FT, Marshall conditioning, and AASHTO T-283 tests and to evaluate the long-term stripping susceptibility of CBETB while also predicting the liquid antistripping additives to assess the mixture’s durability and workability. Specimens were stabilized with Portland cement (0%–6%, bitumen emulsion (0%–5%, and Portland cement-bitumen emulsion mixtures and cured for 7 days, and their short- and long-term performances were studied. Evaluation results of both the Marshall stability ratio and the tensile strength ratio show that the additions of additives increase the resistance of the mixtures to moisture damage. Results of durability tests performed for determining the resistance of compacted specimens to repeated FT cycles indicate that the specimen with the 4% cement-3% bitumen emulsion mixture significantly improves water absorption, volume changes, and weight losses. This indicates the effectiveness of this additive as a road base stabilizer with excellent engineering properties for cold regions.

Formation of contractile networks and fibers in the medial cell cortex through myosin-II turnover, contraction, and stress-stabilization.

Science.gov (United States)

Nie, Wei; Wei, Ming-Tzo; Ou-Yang, H Daniel; Jedlicka, Sabrina S; Vavylonis, Dimitrios

2015-01-01

The morphology of adhered cells depends crucially on the formation of a contractile meshwork of parallel and cross-linked fibers along the contacting surface. The motor activity and minifilament assembly of non-muscle myosin-II is an important component of cortical cytoskeletal remodeling during mechanosensing. We used experiments and computational modeling to study cortical myosin-II dynamics in adhered cells. Confocal microscopy was used to image the medial cell cortex of HeLa cells stably expressing myosin regulatory light chain tagged with GFP (MRLC-GFP). The distribution of MRLC-GFP fibers and focal adhesions was classified into three types of network morphologies. Time-lapse movies show: myosin foci appearance and disappearance; aligning and contraction; stabilization upon alignment. Addition of blebbistatin, which perturbs myosin motor activity, leads to a reorganization of the cortical networks and to a reduction of contractile motions. We quantified the kinetics of contraction, disassembly and reassembly of myosin networks using spatio-temporal image correlation spectroscopy (STICS). Coarse-grained numerical simulations include bipolar minifilaments that contract and align through specified interactions as basic elements. After assuming that minifilament turnover decreases with increasing contractile stress, the simulations reproduce stress-dependent fiber formation in between focal adhesions above a threshold myosin concentration. The STICS correlation function in simulations matches the function measured in experiments. This study provides a framework to help interpret how different cortical myosin remodeling kinetics may contribute to different cell shape and rigidity depending on substrate stiffness. © 2015 Wiley Periodicals, Inc.

Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human

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Xiaodian Sun

2016-10-01

Full Text Available Background: Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE modelling approach and investigate the properties of these networks. Results: Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. Conclusions: The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

Science.gov (United States)

Xu, Chang-Jin; Li, Pei-Luan; Pang, Yi-Cheng

2017-02-01

This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos.~61673008, 11261010, 11101126, Project of High-Level Innovative Talents of Guizhou Province ([2016]5651), Natural Science and Technology Foundation of Guizhou Province (J[2015]2025 and J[2015]2026), 125 Special Major Science and Technology of Department of Education of Guizhou Province ([2012]011) and Natural Science Foundation of the Education Department of Guizhou Province (KY[2015]482)

Radiolytic stabilization of poly(methyl methacrylate) using commercial additives; Estabilizacao radiolitica do poli(metacrilato de metila) usando aditivos comerciais

Energy Technology Data Exchange (ETDEWEB)

Aquino, Katia Aparecida da Silva

2000-04-01

Poly(methyl methacrylate), PMMA, Acrigel, a Brazilian polymer, is used in the manufacture of medical supplies sterelizable by ionizing radiation. However, when PMMA is gamma-irradiated it undergoes main chain scissions, which promote molecular degradation causing reduction in its mechanical properties. Therefore, radiolytic of PMMA is important for it to become commercially radiosterizable. In this work some commercial additives, originally used in photo-and thermo-oxidate stabilization of polymers, were tested. Only two additives, type HALS (Hindered Amine Light Stabilizer), denoted Scavenger, showed a good protective quality. The investigation of radiation-induced main scissions was carried out by viscosimetric method. The most effective additive, added to the polymer system at 0.3 w/w%, promotes a great molecular radioprotection of 93%. That means a reduction of G-value (scissions/100 eV) from 0.611 to 0.053. In addition, the glassy transition temperature (T{sub g}) of PMMA (no additive) significantly changed by radiation does not change when PMMA (with additive) is irradiated. The spectroscopy analysis, FT-IR and NMR ({sup 1}H), showed that the radioprotector added to the system does not change the PMMA structure. (author)

Stability switches, oscillatory multistability, and spatio-temporal patterns of nonlinear oscillations in recurrently delay coupled neural networks.

Science.gov (United States)

Song, Yongli; Makarov, Valeri A; Velarde, Manuel G

2009-08-01

A model of time-delay recurrently coupled spatially segregated neural assemblies is here proposed. We show that it operates like some of the hierarchical architectures of the brain. Each assembly is a neural network with no delay in the local couplings between the units. The delay appears in the long range feedforward and feedback inter-assemblies communications. Bifurcation analysis of a simple four-units system in the autonomous case shows the richness of the dynamical behaviors in a biophysically plausible parameter region. We find oscillatory multistability, hysteresis, and stability switches of the rest state provoked by the time delay. Then we investigate the spatio-temporal patterns of bifurcating periodic solutions by using the symmetric local Hopf bifurcation theory of delay differential equations and derive the equation describing the flow on the center manifold that enables us determining the direction of Hopf bifurcations and stability of the bifurcating periodic orbits. We also discuss computational properties of the system due to the delay when an external drive of the network mimicks external sensory input.

Influence of Zr and nano-Y{sub 2}O{sub 3} additions on thermal stability and improved hardness in mechanically alloyed Fe base ferritic alloys

Energy Technology Data Exchange (ETDEWEB)

Kotan, Hasan, E-mail: hkotan@konya.edu.tr [Department of Metallurgical Engineering and Materials Science, Necmettin Erbakan University, Dere Aşıklar Mah. Demet Sokak, Meram, Konya 42140 (Turkey); Darling, Kris A. [U.S. Army Research Laboratory, Weapons and Materials Research Directorate, RDRL-WMM-F, Aberdeen Proving Ground, MD 21005-5069 (United States); Scattergood, Ronald O.; Koch, Carl C. [Department of Materials Science and Engineering, NC State University, 911 Partners Way, Room 3078, Raleigh, NC 27695-7907 (United States)

2014-12-05

The motivation of this work was driven to improve the thermal stability in systems where polymorphic transformations can result in an additional driving force, upsetting the expected thermodynamic stability. In this study, Fe{sub 92}Ni{sub 8} alloys with Zr and nano-Y{sub 2}O{sub 3} additions were produced by ball milling and then annealed at high temperatures. Emphasis was placed on understanding the effects of dispersed nano-Y{sub 2}O{sub 3} particle additions and their effect on microstructural stability at and above the bcc-to-fcc transformation occurring at 700 °C in Fe–Ni systems. Results reveal that microstructural stability and hardness can be promoted by a combination of Zr and Y{sub 2}O{sub 3} additions, that being mostly effective for stability before and after phase transition, respectively. The mechanical strength of these alloys is achieved by a unique microstructure comprised a ultra-fine grain Fe base matrix, which contains dispersions of both nano-scale in-situ formed Zr base intermetallics and ex-situ added Y{sub 2}O{sub 3} secondary oxide phases. Both of these were found to be essential for a combination of high thermal stability and high mechanical strength properties. - Highlights: • Polymorphic transformations can limit the processing of nanostructured powders. • It causes a rapid grain growth and impairs the improved mechanical properties. • We aim to improve the hardness and thermal stability above the phase transformation. • Thermal stability is achieved by a combination of Zr and Y{sub 2}O{sub 3} additions. • Hardness is promoted by in-situ formed and ex-situ added secondary nano phases.

Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

Science.gov (United States)

Li, Hongfei; Jiang, Haijun; Hu, Cheng

2016-03-01

In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Amino-Acid Network Clique Analysis of Protein Mutation Non-Additive Effects: A Case Study of Lysozme

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Dengming Ming

2018-05-01

Full Text Available Optimizing amino-acid mutations in enzyme design has been a very challenging task in modern bio-industrial applications. It is well known that many successful designs often hinge on extensive correlations among mutations at different sites within the enzyme, however, the underpinning mechanism for these correlations is far from clear. Here, we present a topology-based model to quantitively characterize non-additive effects between mutations. The method is based on the molecular dynamic simulations and the amino-acid network clique analysis. It examines if the two mutation sites of a double-site mutation fall into to a 3-clique structure, and associates such topological property of mutational site spatial distribution with mutation additivity features. We analyzed 13 dual mutations of T4 phage lysozyme and found that the clique-based model successfully distinguishes highly correlated or non-additive double-site mutations from those additive ones whose component mutations have less correlation. We also applied the model to protein Eglin c whose structural topology is significantly different from that of T4 phage lysozyme, and found that the model can, to some extension, still identify non-additive mutations from additive ones. Our calculations showed that mutation non-additive effects may heavily depend on a structural topology relationship between mutation sites, which can be quantitatively determined using amino-acid network k-cliques. We also showed that double-site mutation correlations can be significantly altered by exerting a third mutation, indicating that more detailed physicochemical interactions should be considered along with the network clique-based model for better understanding of this elusive mutation-correlation principle.

Studies of stability and robustness for artificial neural networks and boosted decision trees

International Nuclear Information System (INIS)

Yang, H.-J.; Roe, Byron P.; Zhu Ji

2007-01-01

In this paper, we compare the performance, stability and robustness of Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using MiniBooNE Monte Carlo samples. These methods attempt to classify events given a number of identification variables. The BDT algorithm has been discussed by us in previous publications. Testing is done in this paper by smearing and shifting the input variables of testing samples. Based on these studies, BDT has better particle identification performance than ANN. The degradation of the classifications obtained by shifting or smearing variables of testing results is smaller for BDT than for ANN

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Global exponential stability of bidirectional associative memory neural networks with distributed delays

Science.gov (United States)

Song, Qiankun; Cao, Jinde

2007-05-01

A bidirectional associative memory neural network model with distributed delays is considered. By constructing a new Lyapunov functional, employing the homeomorphism theory, M-matrix theory and the inequality (a[greater-or-equal, slanted]0,bk[greater-or-equal, slanted]0,qk>0 with , and r>1), a sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the model. Moreover, the exponential converging velocity index is estimated, which depends on the delay kernel functions and the system parameters. The results generalize and improve the earlier publications, and remove the usual assumption that the activation functions are bounded . Two numerical examples are given to show the effectiveness of the obtained results.

Reactive Additive Stabilization Process (RASP) for hazardous and mixed waste vitrification

International Nuclear Information System (INIS)

Jantzen, C.M.; Pickett, J.B.; Ramsey, W.G.

1993-01-01

Solidification of hazardous/mixed wastes into glass is being examined at the Savannah River Site (SRS) for (1) nickel plating line (F006) sludges and (2) incinerator wastes. Vitrification of these wastes using high surface area additives, the Reactive Additive Stabilization Process (RASP), has been determined to greatly enhance the dissolution and retention of hazardous, mixed, and heavy metal species in glass. RASP lowers melt temperatures (typically 1050-- 1150 degrees C), thereby minimizing volatility concerns during vitrification. RASP maximizes waste loading (typically 50--75 wt% on a dry oxide basis) by taking advantage of the glass forming potential of the waste. RASP vitrification thereby minimizes waste disposal volume (typically 86--97 vol. %), and maximizes cost savings. Solidification of the F006 plating line sludges containing depleted uranium has been achieved in both soda-lime-silica (SLS) and borosilicate glasses at 1150 degrees C up to waste loadings of 75 wt%. Solidification of incinerator blowdown and mixtures of incinerator blowdown and bottom kiln ash have been achieved in SLS glass at 1150 degrees C up to waste loadings of 50% using RASP. These waste loadings correspond to volume reductions of 86 and 94 volume %, respectively, with large associated savings in storage costs

Stabilization of Networked Control Systems with Variable Delays and Saturating Inputs

Directory of Open Access Journals (Sweden)

M. Mahmodi Kaleybar

2014-06-01

Full Text Available In this paper, improved conditions for the synthesis of static state-feedback controller are derived to stabilize networked control systems (NCSs subject to actuator saturation. Both of the data packet latency and dropout which deteriorate the performance of the closed-loop system are considered in the NCS model via variable delays. Two different techniques are employed to incorporate actuator saturation in the system description. Utilizing Lyapunov-Krasovskii Theorem, delay-dependent conditions are obtained in terms of linear matrix inequalities (LMIs to determine the static feedback gain. Moreover, an optimization problem is formulated in order to find the less conservative estimate for the region of attraction corresponding to different maximum allowable delays. Numerical examples are introduced to demonstrate the effectiveness and advantages of the proposed schemes.

Network spandrels reflect ecological assembly.

Science.gov (United States)

Maynard, Daniel S; Serván, Carlos A; Allesina, Stefano

2018-03-01

Ecological networks that exhibit stable dynamics should theoretically persist longer than those that fluctuate wildly. Thus, network structures which are over-represented in natural systems are often hypothesised to be either a cause or consequence of ecological stability. Rarely considered, however, is that these network structures can also be by-products of the processes that determine how new species attempt to join the community. Using a simulation approach in tandem with key results from random matrix theory, we illustrate how historical assembly mechanisms alter the structure of ecological networks. We demonstrate that different community assembly scenarios can lead to the emergence of structures that are often interpreted as evidence of 'selection for stability'. However, by controlling for the underlying selection pressures, we show that these assembly artefacts-or spandrels-are completely unrelated to stability or selection, and are instead by-products of how new species are introduced into the system. We propose that these network-assembly spandrels are critically overlooked aspects of network theory and stability analysis, and we illustrate how a failure to adequately account for historical assembly can lead to incorrect inference about the causes and consequences of ecological stability. © 2018 John Wiley & Sons Ltd/CNRS.

Context-Based Topology Control for Wireless Mesh Networks

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Pragasen Mudali

2016-01-01

Full Text Available Topology Control has been shown to provide several benefits to wireless ad hoc and mesh networks. However these benefits have largely been demonstrated using simulation-based evaluations. In this paper, we demonstrate the negative impact that the PlainTC Topology Control prototype has on topology stability. This instability is found to be caused by the large number of transceiver power adjustments undertaken by the prototype. A context-based solution is offered to reduce the number of transceiver power adjustments undertaken without sacrificing the cumulative transceiver power savings and spatial reuse advantages gained from employing Topology Control in an infrastructure wireless mesh network. We propose the context-based PlainTC+ prototype and show that incorporating context information in the transceiver power adjustment process significantly reduces topology instability. In addition, improvements to network performance arising from the improved topology stability are also observed. Future plans to add real-time context-awareness to PlainTC+ will have the scheme being prototyped in a software-defined wireless mesh network test-bed being planned.

Effect of phosphorus on out-of-pile and in-pile behaviour of stabilized austenitic stainless steels

International Nuclear Information System (INIS)

Delalande, C.

1992-02-01

This work deals with the improvement of swelling resistance for austenitic stainless steels used as fuel pin cladding in Fast Breeder Reactor. The effect of phosphorus addition and multistabilization by Ti and Nb or Ti, Nb and V are studied on Fe-15Cr-15/25Ni based alloys. First, different ageings are performed to verify the stability of dislocation network, main condition of swelling absence at high irradiation temperature (T>550 deg C, and to study the precipitation, especially the one being able to form during irradiation and to control swelling at lower temperature. Then, 1 MeV electron irradiations are performed to estimate the swelling resistance of these multistabilized steels. Furthermore, neutron radiation induced microstructure of phosphorus modified steels already irradiated in reactor give us fundamental informations to predict and explain the effect of phosphorus and multistabilization on the behaviour of the multistabilized steels. Our results show that niobium plays the same role as titanium on the stabilization ratio in steels, but it is present in more phases. Vanadium seems to have less effect on stability of dislocation network and chemical composition of precipitates. Phosphorus increases the stability of dislocation network of multistabilized steels and FeNbP phosphides are observed at high temperature for phosphorus level above 600 ppm. 1 MeV electron irradiations show that multistabilized steels present good swelling resistance. Phosphorus addition increases the swelling resistance of neutron irradiated steels. (Author). refs., figs., tabs

Effect of highly dispersed yttria addition on thermal stability of hydroxyapatite

Energy Technology Data Exchange (ETDEWEB)

Parente, P., E-mail: pparente@icv.csic.es [Instituto de Ceramica y Vidrio, CSIC, C/Kelsen 5, Madrid 28049 (Spain); Savoini, B. [Departamento de Fisica, Universidad Carlos III de Madrid, Avda. Universidad 30, Leganes 28911 (Spain); Ferrari, B. [Instituto de Ceramica y Vidrio, CSIC, C/Kelsen 5, Madrid 28049 (Spain); Monge, M.A.; Pareja, R. [Departamento de Fisica, Universidad Carlos III de Madrid, Avda. Universidad 30, Leganes 28911 (Spain); Sanchez-Herencia, A.J. [Instituto de Ceramica y Vidrio, CSIC, C/Kelsen 5, Madrid 28049 (Spain)

2013-03-01

The capability of the colloidal method to produce yttria (Y{sub 2}O{sub 3}) dispersed hydroxyapatite (HA) has been investigated as an alternative method to the conventional method of mechanical mixing and sintering for developing HA-based materials that could exhibit controllable and enhanced functional properties. A water based colloidal route to produce HA materials with highly dispersed Y{sub 2}O{sub 3} has been applied, and the effect of 10 wt.% Y{sub 2}O{sub 3} addition to HA investigated by thermal analysis, X-ray diffraction and Fourier transform infrared spectroscopy. These measurements evidence a remarkable effect of this Y{sub 2}O{sub 3} addition on decomposition mechanisms of synthetic HA. Results show that incorporation of Y{sub 2}O{sub 3} as dispersed second phase is beneficial because it hinders the decomposition mechanisms of HA into calcium phosphates. This retardation will allow the control of the sintering conditions for developing HA implants with improved properties. Besides, substitution of Ca{sup 2+} with Y{sup 3+} ions appears to promote the formation of OH{sup -} vacancies, which could improve the conductive properties of HA favorable to osseointegration. - Highlights: Black-Right-Pointing-Pointer We reveal the influence of Y{sub 2}O{sub 3} on thermal stability of hydroxyapatite. Black-Right-Pointing-Pointer Incorporation of Y{sub 2}O{sub 3} delays decomposition of hydroxyapatite to calcium phosphates. Black-Right-Pointing-Pointer Addition of Y{sub 2}O{sub 3} enables sintering conditions more favorable to the densification.

Stabilizing additives added during cell lysis aid in the solubilization of recombinant proteins.

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David J Leibly

Full Text Available Insoluble recombinant proteins are a major issue for both structural genomics and enzymology research. Greater than 30% of recombinant proteins expressed in Escherichia coli (E. coli appear to be insoluble. The prevailing view is that insolubly expressed proteins cannot be easily solubilized, and are usually sequestered into inclusion bodies. However, we hypothesize that small molecules added during the cell lysis stage can yield soluble protein from insoluble protein previously screened without additives or ligands. We present a novel screening method that utilized 144 additive conditions to increase the solubility of recombinant proteins expressed in E. coli. These selected additives are natural ligands, detergents, salts, buffers, and chemicals that have been shown to increase the stability of proteins in vivo. We present the methods used for this additive solubility screen and detailed results for 41 potential drug target recombinant proteins from infectious organisms. Increased solubility was observed for 80% of the recombinant proteins during the primary and secondary screening of lysis with the additives; that is 33 of 41 target proteins had increased solubility compared with no additive controls. Eleven additives (trehalose, glycine betaine, mannitol, L-Arginine, potassium citrate, CuCl(2, proline, xylitol, NDSB 201, CTAB and K(2PO(4 solubilized more than one of the 41 proteins; these additives can be easily screened to increase protein solubility. Large-scale purifications were attempted for 15 of the proteins using the additives identified and eight (40% were prepared for crystallization trials during the first purification attempt. Thus, this protocol allowed us to recover about a third of seemingly insoluble proteins for crystallography and structure determination. If recombinant proteins are required in smaller quantities or less purity, the final success rate may be even higher.

Landslide Monitoring Network Establishment within Unified Datum and Stability Analysis in the Three Gorges Reservoir Area

Directory of Open Access Journals (Sweden)

Shengxiang Huang

2017-01-01

Full Text Available A landslide monitoring network construction within unified datum which combined fiducial points, working reference points, and monitoring points was intensively studied in the Three Gorges Reservoir area. With special long and narrow geographical location in the area, designing and building monitoring network was vital to the realization of landslide monitoring. To build such a network with high precision, this paper mainly focused on the following four aspects: (1 method of using multiple GPS reference stations to build a unified datum network and subnet adjustment, (2 GPS data processing algorithm with millimeter level, (3 analysis of influence on the adjustment resulting from systematic error of time evolution datum from different GPS observations, and (4 establishment and stability analysis of unified datum. Then, using global test and trial-and-error method to analyze the datum based on the GPS observations (2008~2011 of landslide monitoring network in the area, we concluded that there were moved reference points during the three years of high water impoundment, and the horizontal displacement of moved reference points was more than 4 cm, even up to 79.4 cm. The displacement direction of unstable reference points was inspected with geographical environment at sites, which revealed congruency between them.

Long-term stability of superhydrophilic oxygen plasma-modified single-walled carbon nanotube network surfaces and the influence on ammonia gas detection

Energy Technology Data Exchange (ETDEWEB)

Min, Sungjoon [Department of Biomicrosystem Technology, Korea University, Seoul 136-713 (Korea, Republic of); Kim, Joonhyub [Department of Control and Instrumentation Engineering, Korea University, 2511 Sejong-ro, Sejong City 339-770 (Korea, Republic of); Park, Chanwon [Department of Electrical and Electronic Engineering, Kangwon National University, Chuncheon 200-701 (Korea, Republic of); Jin, Joon-Hyung, E-mail: jj1023@chol.com [Department of Chemical Engineering, Kyonggi University, 154-42 Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16227 (Korea, Republic of); Min, Nam Ki, E-mail: nkmin@korea.ac.kr [Department of Biomicrosystem Technology, Korea University, Seoul 136-713 (Korea, Republic of)

2017-07-15

Graphical abstract: Superhydrophilic single-walled carbon nanotube obtained by O{sub 2} plasma treatment voluntarily and non-reversibly reverts to a metastable state. This aerobic aging is an essential process to develop a stable carbon nanotube-based sensor. - Highlights: • Superhydrophilic single-walled carbon nanotube network can be obtained by O{sub 2} plasma-based surface modification. • The modified carbon nanotube surface invariably reverts to a metastable state in a non-reversible manner. • Aerobic aging is essential to stabilize the modified carbon nanotube and the carbon nanotube-based sensing device due to minimized sensor-to-sensor variation. - Abstract: Single-walled carbon nanotube (SWCNT) networks are subjected to a low-powered oxygen plasma for the surface modification. Changes in the surface chemical composition and the stability of the plasma-treated SWCNT (p-SWCNT) with aging in air for up to five weeks are studied using X-ray photoelectron spectroscopy (XPS) and contact angle analysis. The contact angle decreases from 120° of the untreated hydrophobic SWCNT to 0° for the superhydrophilic p-SWCNT. Similarly, the ratio of oxygen to carbon (O:C) based on the XPS spectra increases from 0.25 to 1.19, indicating an increase in surface energy of the p-SWCNT. The enhanced surface energy is gradually dissipated and the p-SWCNT network loses the superhydrophilic surface property. However, it never revert to the original hydrophobic surface state but to a metastable hydrophilic state. The aging effect on sensitivity of the p-SWCNT network-based ammonia sensor is investigated to show the importance of the aging process for the stabilization of the p-SWCNT. The best sensitivity for monitoring NH{sub 3} gas is observed with the as-prepared p-SWCNT, and the sensitivity decreases as similar as the p-SWCNT loses its hydrophilicity with time goes by. After a large performance degradation during the aging time for about two weeks, the response

Thermal Stabilization of Biologics with Photoresponsive Hydrogels.

Science.gov (United States)

Sridhar, Balaji V; Janczy, John R; Hatlevik, Øyvind; Wolfson, Gabriel; Anseth, Kristi S; Tibbitt, Mark W

2018-03-12

Modern medicine, biological research, and clinical diagnostics depend on the reliable supply and storage of complex biomolecules. However, biomolecules are inherently susceptible to thermal stress and the global distribution of value-added biologics, including vaccines, biotherapeutics, and Research Use Only (RUO) proteins, requires an integrated cold chain from point of manufacture to point of use. To mitigate reliance on the cold chain, formulations have been engineered to protect biologics from thermal stress, including materials-based strategies that impart thermal stability via direct encapsulation of the molecule. While direct encapsulation has demonstrated pronounced stabilization of proteins and complex biological fluids, no solution offers thermal stability while enabling facile and on-demand release from the encapsulating material, a critical feature for broad use. Here we show that direct encapsulation within synthetic, photoresponsive hydrogels protected biologics from thermal stress and afforded user-defined release at the point of use. The poly(ethylene glycol) (PEG)-based hydrogel was formed via a bioorthogonal, click reaction in the presence of biologics without impact on biologic activity. Cleavage of the installed photolabile moiety enabled subsequent dissolution of the network with light and release of the encapsulated biologic. Hydrogel encapsulation improved stability for encapsulated enzymes commonly used in molecular biology (β-galactosidase, alkaline phosphatase, and T4 DNA ligase) following thermal stress. β-galactosidase and alkaline phosphatase were stabilized for 4 weeks at temperatures up to 60 °C, and for 60 min at 85 °C for alkaline phosphatase. T4 DNA ligase, which loses activity rapidly at moderately elevated temperatures, was protected during thermal stress of 40 °C for 24 h and 60 °C for 30 min. These data demonstrate a general method to employ reversible polymer networks as robust excipients for thermal stability of complex

Dynamics of continuous-time bidirectional associative memory neural networks with impulses and their discrete counterparts

International Nuclear Information System (INIS)

Huo Haifeng; Li Wantong

2009-01-01

This paper is concerned with the global stability characteristics of a system of equations modelling the dynamics of continuous-time bidirectional associative memory neural networks with impulses. Sufficient conditions which guarantee the existence of a unique equilibrium and its exponential stability of the networks are obtained. For the goal of computation, discrete-time analogues of the corresponding continuous-time bidirectional associative memory neural networks with impulses are also formulated and studied. Our results show that the above continuous-time and discrete-time systems with impulses preserve the dynamics of the networks without impulses when we make some modifications and impose some additional conditions on the systems, the convergence characteristics dynamics of the networks are preserved by both continuous-time and discrete-time systems with some restriction imposed on the impulse effect.

Stability analysis and synchronization in discrete-time complex networks with delayed coupling

Science.gov (United States)

Cheng, Ranran; Peng, Mingshu; Yu, Weibin; Sun, Bo; Yu, Jinchen

2013-12-01

A new network of coupled maps is proposed in which the connections between units involve no delays but the intra-neural communication does, whereas in the work of Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], the focus is on information processing delayed by the inter-neural communication. We show that the synchronization of the network depends on not only the intrinsic dynamical features and inter-connection topology (characterized by the spectrum of the graph Laplacian) but also the delays and the coupling strength. There are two main findings: (i) the more neighbours, the easier to be synchronized; (ii) odd delays are easier to be synchronized than even ones. In addition, compared with those discussed by Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], our model has a better synchronizability for regular networks and small-world variants.

Stabilization of inorganic mixed waste to pass the TCLP and STLC tests using clay and pH-insensitive additives

Energy Technology Data Exchange (ETDEWEB)

Bowers, J.S.; Anson, J.R.; Painter, S.M. [Lawrence Livermore National Lab., CA (United States)] [and others

1995-12-31

Stabilization is a best demonstrated available technology, or BDAT. This technology traps toxic contaminants in a matrix so that they do not leach into the environment. The stabilization process routinely uses pozzolanic materials. Portland cement, fly ash-lime mixes, gypsum cements, and clays are some of the most common materials. In many instances, materials that can pass the Toxicity Characteristic Leaching Procedure (TCLP the federal leach test) or the Soluble Threshold Leachate Concentration (STLC the California leach test) must have high concentrations of lime or other caustic material because of the low pH of the leaching media. Both leaching media, California`s and EPA`s, have a pH of 5.0. California uses citric acid and sodium citrate while EPA uses acetic acid and sodium acetate. The concentration in the leachate is approximately ten times higher for the STLC procedure than the TCLP. These media can form ligands that provide excellent metal leaching. Because of the aggressive nature of the leaching medium, stabilized wastes in many cases will not pass the leaching tests. At the Lawrence Livermore National Laboratory (LLNL), additives such as dithiocarbamates and thiocarbonates, which are pH-insensitive and provide resistance to ligand formation, are used in the waste stabilization process. Attapulgite, montmorillonite, and sepiolite clays are used because they are forgiving (recipe can be adjusted before the matrix hardens) when formulating a stabilization matrix, and they have a neutral pH. By using these clays and additives, LLNL`s highly concentrated wastewater treatment sludges have passed the TCLP and STLC tests. The most frequently used stabilization process consists of a customized recipe involving waste sludge, clay and dithiocarbamate salt, mixed with a double planetary mixer into a pasty consistency. TCLP and STLC data on this waste matrix have shown that the process matrix meets land disposal requirements.

Global robust stability for shunting inhibitory CNNs with delays.

Science.gov (United States)

Wang, Lingna; Lin, Yiping

2004-08-01

In this paper, the problem of global robust stability for shunting inhibitory cellular neural networks (SICNNs) is studied. A sufficient condition guaranteeing the network's global robust stability is established. The result can easily be used to verify globally robust stable networks. An example is given to illustrate that the conditions of our results are feasible.

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances.

Science.gov (United States)

Fairbank, Michael; Li, Shuhui; Fu, Xingang; Alonso, Eduardo; Wunsch, Donald

2014-01-01

We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stabilization of a locally minimal forest

Science.gov (United States)

Ivanov, A. O.; Mel'nikova, A. E.; Tuzhilin, A. A.

2014-03-01

The method of partial stabilization of locally minimal networks, which was invented by Ivanov and Tuzhilin to construct examples of shortest trees with given topology, is developed. According to this method, boundary vertices of degree 2 are not added to all edges of the original locally minimal tree, but only to some of them. The problem of partial stabilization of locally minimal trees in a finite-dimensional Euclidean space is solved completely in the paper, that is, without any restrictions imposed on the number of edges remaining free of subdivision. A criterion for the realizability of such stabilization is established. In addition, the general problem of searching for the shortest forest connecting a finite family of boundary compact sets in an arbitrary metric space is formalized; it is shown that such forests exist for any family of compact sets if and only if for any finite subset of the ambient space there exists a shortest tree connecting it. The theory developed here allows us to establish further generalizations of the stabilization theorem both for arbitrary metric spaces and for metric spaces with some special properties. Bibliography: 10 titles.

Hybrid discrete-time neural networks.

Science.gov (United States)

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

Impact of Distributed Generation Grid Code Requirements on Islanding Detection in LV Networks

Directory of Open Access Journals (Sweden)

Fabio Bignucolo

2017-01-01

Full Text Available The recent growing diffusion of dispersed generation in low voltage (LV distribution networks is entailing new rules to make local generators participate in network stability. Consequently, national and international grid codes, which define the connection rules for stability and safety of electrical power systems, have been updated requiring distributed generators and electrical storage systems to supply stabilizing contributions. In this scenario, specific attention to the uncontrolled islanding issue has to be addressed since currently required anti-islanding protection systems, based on relays locally measuring voltage and frequency, could no longer be suitable. In this paper, the effects on the interface protection performance of different LV generators’ stabilizing functions are analysed. The study takes into account existing requirements, such as the generators’ active power regulation (according to the measured frequency and reactive power regulation (depending on the local measured voltage. In addition, the paper focuses on other stabilizing features under discussion, derived from the medium voltage (MV distribution network grid codes or proposed in the literature, such as fast voltage support (FVS and inertia emulation. Stabilizing functions have been reproduced in the DIgSILENT PowerFactory 2016 software environment, making use of its native programming language. Later, they are tested both alone and together, aiming to obtain a comprehensive analysis on their impact on the anti-islanding protection effectiveness. Through dynamic simulations in several network scenarios the paper demonstrates the detrimental impact that such stabilizing regulations may have on loss-of-main protection effectiveness, leading to an increased risk of unintentional islanding.

Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

Directory of Open Access Journals (Sweden)

Jose P. Perez

2014-01-01

Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

Stability and generalization in seed dispersal networks: a case study of frugivorous fish in Neotropical wetlands.

Science.gov (United States)

Correa, Sandra Bibiana; Arujo, Joisiane K; Penha, Jerry; Nunes da Cunha, Catia; Bobier, Karen E; Anderson, Jill T

2016-08-31

When species within guilds perform similar ecological roles, functional redundancy can buffer ecosystems against species loss. Using data on the frequency of interactions between fish and fruit, we assessed whether co-occurring frugivores provide redundant seed dispersal services in three species-rich Neotropical wetlands. Our study revealed that frugivorous fishes have generalized diets; however, large-bodied fishes had greater seed dispersal breadth than small species, in some cases, providing seed dispersal services not achieved by smaller fish species. As overfishing disproportionately affects big fishes, the extirpation of these species could cause larger secondary extinctions of plant species than the loss of small specialist frugivores. To evaluate the consequences of frugivore specialization for network stability, we extracted data from 39 published seed dispersal networks of frugivorous birds, mammals and fish (our networks) across ecosystems. Our analysis of interaction frequencies revealed low frugivore specialization and lower nestedness than analyses based on binary data (presence-absence of interactions). In that case, ecosystems may be resilient to loss of any given frugivore. However, robustness to frugivore extinction declines with specialization, such that networks composed primarily of specialist frugivores are highly susceptible to the loss of generalists. In contrast with analyses of binary data, recently developed algorithms capable of modelling interaction strengths provide opportunities to enhance our understanding of complex ecological networks by accounting for heterogeneity of frugivore-fruit interactions. © 2016 The Author(s).

Influence of degree correlations on network structure and stability in protein-protein interaction networks