Nonlinear Dynamical Analysis for the Cable Excited with Parametric and Forced Excitation

Directory of Open Access Journals (Sweden)

C. Z. Qian

2014-01-01

Full Text Available Considering the deck vibration effect on the cable in cable-stayed bridge, using nonlinear structure dynamics theory, the nonlinear dynamical equation for the stayed cable excited with deck vibration is proposed. Research shows that the vertical vibration of the deck has a combined parametric and forced excitation effect on the cable when the angle of the cable is taken into consideration. Using multiscale method, the 1/2 principle parametric resonance is studied and the bifurcation equation is obtained. Despite the parameters analysis, the bifurcation characters of the dynamical system are studied. At last, by means of numerical method and software MATHMATIC, the effect rules of system parameters to the dynamical behavior of the system are studied, and some useful conclusions are obtained.

Nonlinear adaptive inverse control via the unified model neural network

Science.gov (United States)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Simulation of nonlinear random vibrations using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Paez, T.L.; Tucker, S.; O`Gorman, C.

1997-02-01

The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

Nonlinear optical sub-bandgap excitation of ZnO-based photonic resonators

Energy Technology Data Exchange (ETDEWEB)

Bader, Christina A.; Zeuner, Franziska; Bader, Manuel H. W.; Zentgraf, Thomas; Meier, Cedrik [Department of Physics and Center for Optoelectronics and Photonics Paderborn (CeOPP), Universität Paderborn, Warburger Str. 100, 33098 Paderborn (Germany)

2015-12-07

Zinc oxide (ZnO) is a versatile candidate for photonic devices due to its highly efficient optical emission. However, for pumping of ZnO photonic devices UV-sources are required. Here, we investigate the alternative usage of widely available pulsed near-infrared (NIR)-sources and compare the efficiency of linear and nonlinear excitation processes. We found that bulk ZnO, ZnO thin films grown by molecular beam epitaxy, and ZnO/SiO{sub 2} microdisk devices exhibit strong nonlinear response when excited with NIR pulses (λ ≈ 1060 nm). In addition, we show that the ZnO/SiO{sub 2} microdisks exhibit sharp whispering gallery modes over the blue-yellow part of the visible spectrum for both excitation conditions and high Q-factors up to Q = 4700. The results demonstrate that nonlinear excitation is an efficient way to pump ZnO photonic devices.

Nonlinear programming with feedforward neural networks.

Energy Technology Data Exchange (ETDEWEB)

Reifman, J.

1999-06-02

We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

Neural networks for feedback feedforward nonlinear control systems.

Science.gov (United States)

Parisini, T; Zoppoli, R

1994-01-01

This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

A Recurrent Neural Network for Nonlinear Fractional Programming

Directory of Open Access Journals (Sweden)

Quan-Ju Zhang

2012-01-01

Full Text Available This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.

Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

Science.gov (United States)

Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

2016-11-14

In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

Stability analysis of embedded nonlinear predictor neural generalized predictive controller

Directory of Open Access Journals (Sweden)

Hesham F. Abdel Ghaffar

2014-03-01

Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

Nonlinear dynamic analysis of cantilevered piezoelectric energy harvesters under simultaneous parametric and external excitations

Science.gov (United States)

Fang, Fei; Xia, Guanghui; Wang, Jianguo

2018-02-01

The nonlinear dynamics of cantilevered piezoelectric beams is investigated under simultaneous parametric and external excitations. The beam is composed of a substrate and two piezoelectric layers and assumed as an Euler-Bernoulli model with inextensible deformation. A nonlinear distributed parameter model of cantilevered piezoelectric energy harvesters is proposed using the generalized Hamilton's principle. The proposed model includes geometric and inertia nonlinearity, but neglects the material nonlinearity. Using the Galerkin decomposition method and harmonic balance method, analytical expressions of the frequency-response curves are presented when the first bending mode of the beam plays a dominant role. Using these expressions, we investigate the effects of the damping, load resistance, electromechanical coupling, and excitation amplitude on the frequency-response curves. We also study the difference between the nonlinear lumped-parameter and distributed-parameter model for predicting the performance of the energy harvesting system. Only in the case of parametric excitation, we demonstrate that the energy harvesting system has an initiation excitation threshold below which no energy can be harvested. We also illustrate that the damping and load resistance affect the initiation excitation threshold.

Implementation of neural network based non-linear predictive

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1998-01-01

The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

Nonlinear Characteristics of Randomly Excited Transonic Flutter

DEFF Research Database (Denmark)

Christiansen, Lasse Engbo; Lehn-Schiøler, Tue; Mosekilde, Erik

2002-01-01

. When this model is extended by the introduction of nonlinear terms, it can reproduce the subcritical Hopf bifurcation. We hereafter consider the effects of subjecting simplified versions of the model to random external excitations representing the fluctuations present in the airflow. These models can......The paper describes the effects of random external excitations on the onset and dynamical characteristics of transonic flutter (i.e. large-amplitude, self-sustained oscillations) for a high aspect ratio wing. Wind tunnel experiments performed at the National Aerospace Laboratory (NAL) in Japan have...

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Science.gov (United States)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Three types magnetic moment distribution of nonlinear excitations in a Heisenberg helimagnet

Energy Technology Data Exchange (ETDEWEB)

Qi, Jian-Wen [School of Physics, Northwest University, Xi' an 710069 (China); Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi' an 710069 (China); Li, Zai-Dong [Department of Applied Physics, Hebei University of Technology, Tianjin 300401 (China); Yang, Zhan-Ying, E-mail: zyyang@nwu.edu.cn [School of Physics, Northwest University, Xi' an 710069 (China); Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi' an 710069 (China); Yang, Wen-Li [Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi' an 710069 (China); Institute of Modern Physics, Northwest University, Xi' an 710069 (China)

2017-06-15

Highlights: • Three different types of soliton excitations under the spin-wave background are demonstrated in spin chain system. • The magnetic moment distributions corresponding to these solitons are characterized in detail. • The formation mechanisms of those excitations are explained by the magnon density distribution. - Abstract: We study the nonlinear spin dynamics of an anisotropic Heisenberg helimagnet in a fourth-order integrable nonlinear Schrödinger equation. We demonstrate that there are three types of nonlinear spin excitations on a spin-wave background in the Heisenberg helimagnet, notably including anti-dark soliton, W-shaped soliton, and multi-peak soliton. The magnetic moment distribution that corresponds to each of these are characterized in detail. Additionally, the formation mechanism is clarified by the magnon density distribution.

Spatially localized, temporally quasiperiodic, discrete nonlinear excitations

International Nuclear Information System (INIS)

Cai, D.; Bishop, A.R.; Gronbech-Jensen, N.

1995-01-01

In contrast to the commonly discussed discrete breather, which is a spatially localized, time-periodic solution, we present an exact solution of a discrete nonlinear Schroedinger breather which is a spatially localized, temporally quasiperiodic nonlinear coherent excitation. This breather is a multiple-soliton solution in the sense of the inverse scattering transform. A discrete breather of multiple frequencies is conceptually important in studies of nonlinear lattice systems. We point out that, for this breather, the incommensurability of its frequencies is a discrete lattice effect and these frequencies become commensurate in the continuum limit. To understand the dynamical properties of the breather, we also discuss its stability and its behavior in the presence of an external potential. Finally, we indicate how to obtain an exact N-soliton breather as a discrete generalization of the continuum multiple-soliton solution

Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations.

Science.gov (United States)

Xiao, Lin; Liao, Bolin; Li, Shuai; Chen, Ke

2018-02-01

In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Implementation of neural network based non-linear predictive control

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1999-01-01

This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

DEFF Research Database (Denmark)

Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

2011-01-01

It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude....

Nonlinear identification of process dynamics using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.F.; Chong, K.T.

1992-01-01

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Homeostatic scaling of excitability in recurrent neural networks.

NARCIS (Netherlands)

Remme, M.W.H.; Wadman, W.J.

2012-01-01

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which

Nonlinear behavior of photoluminescence from silicon particles under two-photon excitation

International Nuclear Information System (INIS)

Xu Xingsheng; Yokoyama, Shiyoshi

2011-01-01

Two-photon excited fluorescence (TPEF) under continuous-wave excitation from silicon particles produced by a pulsed laser is investigated. Spectra and images of TPEF from silicon particles are studied under different excitation intensities and operation modes (continuous wave or pulse). It is found that the photoluminescence depends superlinearly on the excitation intensity and that the spectral shape and peaks vary with different silicon particles. The above phenomena show the nonlinear behavior of TPEF from silicon particles, and stimulated emission is a possible process.

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

Science.gov (United States)

Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

2016-02-01

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

CERN Document Server

Rigatos, Gerasimos G

2015-01-01

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

Science.gov (United States)

Zhao, Haiquan; Zhang, Jiashu

2009-12-01

To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

Parametric autoresonant excitation of the nonlinear Schrödinger equation.

Science.gov (United States)

Friedland, L; Shagalov, A G

2016-10-01

Parametric excitation of autoresonant solutions of the nonlinear Schrodinger (NLS) equation by a chirped frequency traveling wave is discussed. Fully nonlinear theory of the process is developed based on Whitham's averaged variational principle and its predictions verified in numerical simulations. The weakly nonlinear limit of the theory is used to find the threshold on the amplitude of the driving wave for entering the autoresonant regime. It is shown that above the threshold, a flat (spatially independent) NLS solution can be fully converted into a traveling wave. A simplified, few spatial harmonics expansion approach is also developed for studying this nonlinear mode conversion process, allowing interpretation as autoresonant interaction within triads of spatial harmonics.

Nonlinear phenomena in the highly excited state of C60

International Nuclear Information System (INIS)

Byrne, H.J.; Maser, W.K.; Kaiser, M.; Akselrod, L.; Anders, J.; Ruehle, W.W.; Zhou, X.Q.; Mittelbach, A.; Roth, S.

1993-01-01

Under high intensity illumination, the optical and electronic properties of fullerenes are seen to undergo dramatic, nonlinear changes. The photoluminescence emission is seen to increase with approximately the third power of the input intensity above an apparent threshold intensity. Associated with this nonlinear increase is the emergence of a long lifetime emission component and a redshifting of the emission spectrum. Above the threshold intensity the photoconductive response increases with approximately the cube of the input power. In the highly excited state, the photoconductive response becomes relatively temperature independent compared to the thermally activated behaviour observed at low intensities. The characteristics of the temperature dependence are associated with a metallic-like phase in the highly excited state and therefore an optically driven insulator to metal transition is proposed as a description of the observed phenomena. (orig.)

Nonboson treatment of excitonic nonlinearity in optically excited media

International Nuclear Information System (INIS)

Nguyen Ba An.

1990-11-01

The present article shortly reviews some recent results in the study of excitonic nonlinearity in optically excited media using a nonboson treatment for many-exciton systems. After a brief discussion of the exciton nonbosonity the closed commutation relations are given for exciton operators which hold for any exciton density and type. The nonboson treatment is then applied to the problems of intrinsic optical bistability and nonlinear polariton yielding quite interesting and new effects, e.g. new shapes of hysteresis loops of intrinsic optical bistability or anomalies of polariton dispersion. (author). 71 refs, 4 figs

Nonlinear Time Series Prediction Using Chaotic Neural Networks

Science.gov (United States)

Li, Ke-Ping; Chen, Tian-Lun

2001-06-01

A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. The project supported by National Basic Research Project "Nonlinear Science" and National Natural Science Foundation of China under Grant No. 60074020

Excitation of multiphase waves of the nonlinear Schroedinger equation by capture into resonances

International Nuclear Information System (INIS)

Friedland, L.; Shagalov, A.G.

2005-01-01

A method for adiabatic excitation and control of multiphase (N-band) waves of the periodic nonlinear Schroedinger (NLS) equation is developed. The approach is based on capturing the system into successive resonances with external, small amplitude plane waves having slowly varying frequencies. The excitation proceeds from zero and develops in stages, as an (N+1)-band (N=0,1,2,...), growing amplitude wave is formed in the (N+1)th stage from an N-band solution excited in the preceding stage. The method is illustrated in simulations, where the excited multiphase waves are analyzed via the spectral approach of the inverse scattering transform method. The theory of excitation of 0- and 1-band NLS solutions by capture into resonances is developed on the basis of a weakly nonlinear version of Whitham's averaged variational principle. The phenomenon of thresholds on the driving amplitudes for capture into successive resonances and the stability of driven, phase-locked solutions in these cases are discussed

Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

Directory of Open Access Journals (Sweden)

Pei-Chen Lo

2013-01-01

Full Text Available This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph. Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y, the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording, in Chan meditation (stage M, and the unique Chakra-focusing practice (stage C. Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

Wideband excitation in nonlinear vibro-acoustic modulation for damage detection

Science.gov (United States)

Klepka, A.; Adamczyk, M.; Pieczonka, L.; Staszewski, W. J.

2016-04-01

The paper discusses the use of wideband excitation in nonlinear vibro-acoustic modulation technique (VAM) used for damage detection. In its original form, two mono-harmonic signals (low and high frequency) are used for excitation. The low frequency excitation is typically selected based on a modal analysis test and high frequency excitation is selected arbitrarily in the ultrasonic frequency range. This paper presents a different approach with use of wideband excitation signals. The proposed approach gives the possibility to simplify the testing procedure by omitting the modal test used to determine the value of low frequency excitation. Simultaneous use of wideband excitation for high frequency solves the ambiguity related to the selection of the frequency of acoustic wave. Broadband excitation signals require, however, more elaborate signal processing methods to determine the intensity of modulation for a given bandwidth. The paper discusses the proposed approach and the related signal processing procedure. Experimental validation of the proposed technique is performed on a laminated composite plate with a barely visible impact damage that was generated in an impact test. Piezoceramic actuators are used for vibration excitation and a scanning laser vibrometer is used for noncontact data acquisition.

Efficient excitation of nonlinear phonons via chirped pulses: Induced structural phase transitions

Science.gov (United States)

Itin, A. P.; Katsnelson, M. I.

2018-05-01

Nonlinear phononics play important role in strong laser-solid interactions. We discuss a dynamical protocol for efficient phonon excitation, considering recent inspiring proposals: inducing ferroelectricity in paraelectric perovskites, and inducing structural deformations in cuprates [Subedi et al., Phys. Rev. B 89, 220301(R) (2014), 10.1103/PhysRevB.89.220301; Phys. Rev. B 95, 134113 (2017), 10.1103/PhysRevB.95.134113]. High-frequency phonon modes are driven by midinfrared pulses, and coupled to lower-frequency modes those indirect excitations cause structural deformations. We study in more detail the case of KTaO3 without strain, where it was not possible to excite the needed low-frequency phonon mode by resonant driving of the higher frequency one. Behavior of the system is explained using a reduced model of coupled driven nonlinear oscillators. We find a dynamical mechanism which prevents effective excitation at resonance driving. To induce ferroelectricity, we employ driving with sweeping frequency, realizing so-called capture into resonance. The method can be applied to many other related systems.

Nonlinear dynamic analysis of atomic force microscopy under deterministic and random excitation

International Nuclear Information System (INIS)

Pishkenari, Hossein Nejat; Behzad, Mehdi; Meghdari, Ali

2008-01-01

The atomic force microscope (AFM) system has evolved into a useful tool for direct measurements of intermolecular forces with atomic-resolution characterization that can be employed in a broad spectrum of applications. This paper is devoted to the analysis of nonlinear behavior of amplitude modulation (AM) and frequency modulation (FM) modes of atomic force microscopy. For this, the microcantilever (which forms the basis for the operation of AFM) is modeled as a single mode approximation and the interaction between the sample and cantilever is derived from a van der Waals potential. Using perturbation methods such as averaging, and Fourier transform nonlinear equations of motion are analytically solved and the advantageous results are extracted from this nonlinear analysis. The results of the proposed techniques for AM-AFM, clearly depict the existence of two stable and one unstable (saddle) solutions for some of exciting parameters under deterministic vibration. The basin of attraction of two stable solutions is different and dependent on the exciting frequency. From this analysis the range of the frequency which will result in a unique periodic response can be obtained and used in practical experiments. Furthermore the analytical responses determined by perturbation techniques can be used to detect the parameter region where the chaotic motion is avoided. On the other hand for FM-AFM, the relation between frequency shift and the system parameters can be extracted and used for investigation of the system nonlinear behavior. The nonlinear behavior of the oscillating tip can easily explain the observed shift of frequency as a function of tip sample distance. Also in this paper we have investigated the AM-AFM system response under a random excitation. Using two different methods we have obtained the statistical properties of the tip motion. The results show that we can use the mean square value of tip motion to image the sample when the excitation signal is random

Nonlinear dynamic analysis of atomic force microscopy under deterministic and random excitation

Energy Technology Data Exchange (ETDEWEB)

Pishkenari, Hossein Nejat [Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Behzad, Mehdi [Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)], E-mail: m_behzad@sharif.edu; Meghdari, Ali [Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)

2008-08-15

The atomic force microscope (AFM) system has evolved into a useful tool for direct measurements of intermolecular forces with atomic-resolution characterization that can be employed in a broad spectrum of applications. This paper is devoted to the analysis of nonlinear behavior of amplitude modulation (AM) and frequency modulation (FM) modes of atomic force microscopy. For this, the microcantilever (which forms the basis for the operation of AFM) is modeled as a single mode approximation and the interaction between the sample and cantilever is derived from a van der Waals potential. Using perturbation methods such as averaging, and Fourier transform nonlinear equations of motion are analytically solved and the advantageous results are extracted from this nonlinear analysis. The results of the proposed techniques for AM-AFM, clearly depict the existence of two stable and one unstable (saddle) solutions for some of exciting parameters under deterministic vibration. The basin of attraction of two stable solutions is different and dependent on the exciting frequency. From this analysis the range of the frequency which will result in a unique periodic response can be obtained and used in practical experiments. Furthermore the analytical responses determined by perturbation techniques can be used to detect the parameter region where the chaotic motion is avoided. On the other hand for FM-AFM, the relation between frequency shift and the system parameters can be extracted and used for investigation of the system nonlinear behavior. The nonlinear behavior of the oscillating tip can easily explain the observed shift of frequency as a function of tip sample distance. Also in this paper we have investigated the AM-AFM system response under a random excitation. Using two different methods we have obtained the statistical properties of the tip motion. The results show that we can use the mean square value of tip motion to image the sample when the excitation signal is random.

Lukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models

CERN Document Server

Baianu, I C

2004-01-01

A categorical and Lukasiewicz-Topos framework for Lukasiewicz Algebraic Logic models of nonlinear dynamics in complex functional systems such as neural networks, genomes and cell interactomes is proposed. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Lukasiewicz Topos with an n-valued Lukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

Three-dimensional finite element nonlinear dynamic analysis of pile groups for lateral transient and seismic excitations

International Nuclear Information System (INIS)

Maheshwari, B.K.; Truman, K.Z.; El Naggar, M.H.; Gould, P.L.

2004-01-01

The effects of material nonlinearity of soil and separation at the soil-pile interface on the dynamic behaviour of a single pile and pile groups are investigated. An advanced plasticity-based soil model, hierarchical single surface (HiSS), is incorporated in the finite element formulation. To simulate radiation effects, proper boundary conditions are used. The model and algorithm are verified with analytical results that are available for elastic and elastoplastic soil models. Analyses are performed for seismic excitation and for the load applied on the pile cap. For seismic analysis, both harmonic and transient excitations are considered. For loading on the pile cap, dynamic stiffness of the soil-pile system is derived and the effect of nonlinearity is investigated. The effects of spacing between piles are investigated, and it was found that the effect of soil nonlinearity on the seismic response is very much dependent on the frequency of excitation. For the loading on a pile cap, the nonlinearity increases the response for most of the frequencies of excitation while decreasing the dynamic stiffness of the soil-pile system. (author)

Localized excitations in nonlinear complex systems current state of the art and future perspectives

CERN Document Server

Cuevas-Maraver, Jesús; Frantzeskakis, Dimitri; Karachalios, Nikos; Kevrekidis, Panayotis; Palmero-Acebedo, Faustino

2014-01-01

The study of nonlinear localized excitations is a long-standing challenge for research in basic and applied science, as well as engineering, due to their importance in understanding and predicting phenomena arising in nonlinear and complex systems, but also due to their potential for the development and design of novel applications. This volume is a compilation of chapters representing the current state-of-the-art on the field of localized excitations and their role in the dynamics of complex physical systems.

Non-linear operation of nanomechnical systems combining photothermal excitation and magneto-motive detection

International Nuclear Information System (INIS)

Koenig, Daniel R; Metzger, Constanze; Camerer, Stephan; Kotthaus, Joerg P

2006-01-01

We present a non-linear operation of a nanomechanical beam resonator by photothermal excitation at 4 K. The resonators dimensions are 10 μm in length, 200 nm in width, and 200 nm in height. The actuation mechanism is based on a pulsed diode laser focused onto the centre of the beam resonator. Thermally induced stress caused by the different thermal expansion coefficients of the bi-layer system periodically deflects the resonator. Magnetomotively detected amplitudes up to 150 nm are reached at the fundamental resonance mode at a frequency of 8.9 MHz. Furthermore, the third eigenmode of the resonator at a frequency 36 MHz is also excited. We conclude that the photothermal excitation at 4 K should be applicable up to the GHz regime, the operation in the non-linear regime can be used for performance enhancement of nanomechanical systems, and the combination of photothermal excitation and magneto-motive detection avoids undesired cross talk

Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

Directory of Open Access Journals (Sweden)

Ruliang Wang

2012-01-01

Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

Self-excited nonlinear plasma series resonance oscillations in geometrically symmetric capacitively coupled radio frequency discharges

International Nuclear Information System (INIS)

Donko, Z.; Schulze, J.; Czarnetzki, U.; Luggenhoelscher, D.

2009-01-01

At low pressures, nonlinear self-excited plasma series resonance (PSR) oscillations are known to drastically enhance electron heating in geometrically asymmetric capacitively coupled radio frequency discharges by nonlinear electron resonance heating (NERH). Here we demonstrate via particle-in-cell simulations that high-frequency PSR oscillations can also be excited in geometrically symmetric discharges if the driving voltage waveform makes the discharge electrically asymmetric. This can be achieved by a dual-frequency (f+2f) excitation, when PSR oscillations and NERH are turned on and off depending on the electrical discharge asymmetry, controlled by the phase difference of the driving frequencies

Twistacene contained molecule for optical nonlinearity: Excited-state based negative refraction and optical limiting

Science.gov (United States)

Wu, Xingzhi; Xiao, Jinchong; Sun, Ru; Jia, Jidong; Yang, Junyi; Ao, Guanghong; Shi, Guang; Wang, Yuxiao; Zhang, Xueru; Song, Yinglin

2018-06-01

Spindle-type molecules containing twisted acenes (PyBTA-1 &PyBTA-2) are designed, synthesized characterized. Picosecond Z-scan experiments under 532 nm show reverse saturable absorption and negative nonlinear refraction, indicating large third-order optical nonlinearity in PyBTA-1. The mechanism of the optical nonlinearity is investigated and the results show that the nonlinear absorption and refraction in PyBTA-1 originates from a charge transfer (CT) state. Furthermore, relatively long lifetime and absorptive cross section of the CT state are measured. Based on the excited state absorption in PyBTA-1, strong optical limiting with ∼0.3 J/cm2 thresholds are obtained when excited by picoseconds and nanoseconds pulses. The findings on nonlinear optics suggest PyBTA-1 a promising material of all optical modulation and laser protection, which enrich the potential applications of these spindle-type molecules. Comparing to the previously reported spindle-type molecules with analogous structures, the introduction of ICT in PyBTA-1 &PyBTA-2 dramatically decreases the two-photon absorption while enhances the nonlinear refraction. The results could be used to selectively tailor the optical nonlinearity in such kind of compounds.

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

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Xiao-Li Li

2014-01-01

Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

Localization of nonlinear excitations in curved waveguides

DEFF Research Database (Denmark)

Gaididei, Yu. B.; Christiansen, Peter Leth; Kevrekidis, P. G.

2005-01-01

numerical simulations of the nonlinear problem and in this case localized excitations are found to persist. We found also interesting relaxational dynamics. Analogies of the present problem in context related to atomic physics and particularly to Bose–Einstein condensation are discussed.......Motivated by the examples of a curved waveguide embedded in a photonic crystal and cold atoms moving in a waveguide created by a spatially inhomogeneous electromagnetic field, we examine the effects of geometry in a 'quantum channel' of parabolic form. Starting with the linear case we derive exact...

Memory-induced nonlinear dynamics of excitation in cardiac diseases.

Science.gov (United States)

Landaw, Julian; Qu, Zhilin

2018-04-01

Excitable cells, such as cardiac myocytes, exhibit short-term memory, i.e., the state of the cell depends on its history of excitation. Memory can originate from slow recovery of membrane ion channels or from accumulation of intracellular ion concentrations, such as calcium ion or sodium ion concentration accumulation. Here we examine the effects of memory on excitation dynamics in cardiac myocytes under two diseased conditions, early repolarization and reduced repolarization reserve, each with memory from two different sources: slow recovery of a potassium ion channel and slow accumulation of the intracellular calcium ion concentration. We first carry out computer simulations of action potential models described by differential equations to demonstrate complex excitation dynamics, such as chaos. We then develop iterated map models that incorporate memory, which accurately capture the complex excitation dynamics and bifurcations of the action potential models. Finally, we carry out theoretical analyses of the iterated map models to reveal the underlying mechanisms of memory-induced nonlinear dynamics. Our study demonstrates that the memory effect can be unmasked or greatly exacerbated under certain diseased conditions, which promotes complex excitation dynamics, such as chaos. The iterated map models reveal that memory converts a monotonic iterated map function into a nonmonotonic one to promote the bifurcations leading to high periodicity and chaos.

Nonlinear excitation of electron cyclotron waves by a monochromatic strong microwave: computer simulation analysis of the MINIX results

Energy Technology Data Exchange (ETDEWEB)

Matsumoto, H.; Kimura, T.

1986-01-01

Triggered by the experimental results of the MINIX, a computer simulation study was initiated on the nonlinear excitation of electrostatic electron cyclotron waves by a monochromatic electromagnetic wave such as the transmitted microwave in the MINIX. The model used assumes that both of the excited waves and exciting (pumping) electromagnetic wave as well as the idler electromagnetic wave propagate in the direction perpendicular to the external magnetic field. The simulation code used for this study was the one-and-two-half dimensional electromagnetic particle code named KEMPO. The simulation result shows the high power electromagnetic wave produces both the backscattered electromagnetic wave and electrostatic electron cyclotron waves as a result of nonlinear parametric instability. Detailed nonlinear microphysics related to the wave excitation is discussed in terms of the nonlinear wave-wave couplings and associated ponderomotive force produced by the high power electromagnetic waves. 2 references, 4 figures.

Nonlinear excitation of electron cyclotron waves by a monochromatic strong microwave: computer simulation analysis of the MINIX results

International Nuclear Information System (INIS)

Matsumoto, H.; Kimura, T.

1986-01-01

Triggered by the experimental results of the MINIX, a computer simulation study was initiated on the nonlinear excitation of electrostatic electron cyclotron waves by a monochromatic electromagnetic wave such as the transmitted microwave in the MINIX. The model used assumes that both of the excited waves and exciting (pumping) electromagnetic wave as well as the idler electromagnetic wave propagate in the direction perpendicular to the external magnetic field. The simulation code used for this study was the one-and-two-half dimensional electromagnetic particle code named KEMPO. The simulation result shows the high power electromagnetic wave produces both the backscattered electromagnetic wave and electrostatic electron cyclotron waves as a result of nonlinear parametric instability. Detailed nonlinear microphysics related to the wave excitation is discussed in terms of the nonlinear wave-wave couplings and associated ponderomotive force produced by the high power electromagnetic waves. 2 references, 4 figures

Excitation of plasma waves by nonlinear currents induced by a high-frequency electromagnetic pulse

Energy Technology Data Exchange (ETDEWEB)

Grishkov, V. E.; Uryupin, S. A., E-mail: uryupin@sci.lebedev.ru [Russian Academy of Sciences, Lebedev Physical Institute (Russian Federation)

2017-03-15

Excitation of plasma waves by nonlinear currents induced by a high-frequency electromagnetic pulse is analyzed within the kinetic approach. It is shown that the most efficient source of plasma waves is the nonlinear current arising due to the gradient of the energy density of the high-frequency field. Generation of plasma waves by the drag current is usually less efficient but not negligibly small at relatively high frequencies of electron–ion collisions. The influence of electron collisions on the excitation of plasma waves by pulses of different duration is described quantitatively.

A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

Science.gov (United States)

Xu, Rui; Zhou, Miaolei

2018-04-01

Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

Hybrid neural network bushing model for vehicle dynamics simulation

International Nuclear Information System (INIS)

Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

2008-01-01

Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers

Nonlinear neural network for hemodynamic model state and input estimation using fMRI data

KAUST Repository

Karam, Ayman M.

2014-11-01

Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.

Nonlinear excitation of geodesic acoustic modes by drift waves

International Nuclear Information System (INIS)

Chakrabarti, N.; Singh, R.; Kaw, P. K.; Guzdar, P. N.

2007-01-01

In this paper, two mode-coupling analyses for the nonlinear excitation of the geodesic acoustic modes (GAMs) in tokamak plasmas by drift waves are presented. The first approach is a coherent parametric process, which leads to a three-wave resonant interaction. This investigation allows for the drift waves and the GAMs to have comparable scales. The second approach uses the wave-kinetic equations for the drift waves, which then couples to the GAMs. This requires that the GAM scale length be large compared to the wave packet associated with the drift waves. The resonance conditions for these two cases lead to specific predictions of the radial wave number of the excited GAMs

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

Directory of Open Access Journals (Sweden)

YanBin Liu

2017-01-01

Full Text Available The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.

The implications of non-linearity for excitation transfer in DNA

International Nuclear Information System (INIS)

Baverstock, K.F.; Cundall, R.B.

1990-01-01

Non-linear effects which arise from the coupling of anharmonic interactions can completely change excitation transport through molecular chains. The consequences of this for an understanding of the effect of ionising radiation on DNA are discussed. We consider that these effects should be taken into account in the interpretation of experimental data. (author)

Nonlinear dynamics of trions under strong optical excitation in monolayer MoSe2.

Science.gov (United States)

Ye, Jialiang; Yan, Tengfei; Niu, Binghui; Li, Ying; Zhang, Xinhui

2018-02-05

By employing ultrafast transient reflection measurements based on two-color pump-probe spectroscopy, the population and valley polarization dynamics of trions in monolayer MoSe 2 were investigated at relatively high excitation densities under near-resonant excitation. Both the nonlinear dynamic photobleaching of the trion resonance and the redshift of the exciton resonance were found to be responsible for the excitation-energy- and density-dependent transient reflection change as a result of many-body interactions. Furthermore, from the polarization-resolved measurements, it was revealed that the initial fast population and polarization decay process upon strong photoexcitation observed for trions was determined by trion formation, transient phase-space filling and the short valley lifetime of excitons. The results provide a basic understanding of the nonlinear dynamics of population and valley depolarization of trions, as well as exciton-trion correlation in atomically thin MoSe 2 and other transition metal dichalcogenide materials.

Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

Directory of Open Access Journals (Sweden)

Ibnkahla Mohamed

2003-01-01

Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

Sensitivity of nonlinear photoionization to resonance substructure in collective excitation

Science.gov (United States)

Mazza, T.; Karamatskou, A.; Ilchen, M.; Bakhtiarzadeh, S.; Rafipoor, A. J.; O'Keeffe, P.; Kelly, T. J.; Walsh, N.; Costello, J. T.; Meyer, M.; Santra, R.

2015-01-01

Collective behaviour is a characteristic feature in many-body systems, important for developments in fields such as magnetism, superconductivity, photonics and electronics. Recently, there has been increasing interest in the optically nonlinear response of collective excitations. Here we demonstrate how the nonlinear interaction of a many-body system with intense XUV radiation can be used as an effective probe for characterizing otherwise unresolved features of its collective response. Resonant photoionization of atomic xenon was chosen as a case study. The excellent agreement between experiment and theory strongly supports the prediction that two distinct poles underlie the giant dipole resonance. Our results pave the way towards a deeper understanding of collective behaviour in atoms, molecules and solid-state systems using nonlinear spectroscopic techniques enabled by modern short-wavelength light sources. PMID:25854939

Nonlinear Dynamic Behavior of a Flexible Structure to Combined External Acoustic and Parametric Excitation

Directory of Open Access Journals (Sweden)

Paulo S. Varoto

2006-01-01

Full Text Available Flexible structures are frequently subjected to multiple inputs when in the field environment. The accurate determination of the system dynamic response to multiple inputs depends on how much information is available from the excitation sources that act on the system under study. Detailed information include, but are not restricted to appropriate characterization of the excitation sources in terms of their variation in time and in space for the case of distributed loads. Another important aspect related to the excitation sources is how inputs of different nature contribute to the measured dynamic response. A particular and important driving mechanism that can occur in practical situations is the parametric resonance. Another important input that occurs frequently in practice is related to acoustic pressure distributions that is a distributed type of loading. In this paper, detailed theoretical and experimental investigations on the dynamic response of a flexible cantilever beam carrying a tip mass to simultaneously applied external acoustic and parametric excitation signals have been performed. A mathematical model for transverse nonlinear vibration is obtained by employing Lagrange’s equations where important nonlinear effects such as the beam’s curvature and quadratic viscous damping are accounted for in the equation of motion. The beam is driven by two excitation sources, a sinusoidal motion applied to the beam’s fixed end and parallel to its longitudinal axis and a distributed sinusoidal acoustic load applied orthogonally to the beam’s longitudinal axis. The major goal here is to investigate theoretically as well as experimentally the dynamic behavior of the beam-lumped mass system under the action of these two excitation sources. Results from an extensive experimental work show how these two excitation sources interacts for various testing conditions. These experimental results are validated through numerically simulated results

Effect of Various Excitation Conditions on Vibrational Energy in a Multi-Degree-of-Freedom Torsional System with Piecewise-Type Nonlinearities

Directory of Open Access Journals (Sweden)

Jong-Yun Yoon

2015-09-01

Full Text Available Dynamic behaviors in practical driveline systems for wind turbines or vehicles are inherently affected by multiple nonlinearities such as piecewise-type torsional springs. However, various excitation conditions with different levels of magnitudes also show strong relationships to the dynamic behaviors when system responses are examined in both frequency and time domains. This study investigated the nonlinear responses of torsional systems under various excitations by using the harmonic balance method and numerical analysis. In order to understand the effect of piecewise-type nonlinearities on vibrational energy with different excitations, the nonlinear responses were investigated with various comparisons. First, two different jumping phenomena with frequency up- and down-sweeping conditions were determined under severe excitation levels. Second, practical system analysis using the phase plane and Poincaré map was conducted in various ways. When the system responses were composed of quasi-periodic components, Poincaré map analysis clearly revealed the nonlinear dynamic characteristics and thus it is suggested to investigate complicated nonlinear dynamic responses in practical driveline systems.

On the synchronization of neural networks containing time-varying delays and sector nonlinearity

International Nuclear Information System (INIS)

Yan, J.-J.; Lin, J.-S.; Hung, M.-L.; Liao, T.-L.

2007-01-01

We present a systematic design procedure for synchronization of neural networks subject to time-varying delays and sector nonlinearity in the control input. Based on the drive-response concept and the Lyapunov stability theorem, a memoryless decentralized control law is proposed which guarantees exponential synchronization even when input nonlinearity is present. The supplementary requirement that the time-derivative of time-varying delays must be smaller than one is released for the proposed control scheme. A four-dimensional Hopfield neural network with time-varying delays is presented as the illustrative example to demonstrate the effectiveness of the proposed synchronization scheme

Supercritical Nonlinear Vibration of a Fluid-Conveying Pipe Subjected to a Strong External Excitation

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Yan-Lei Zhang

2016-01-01

Full Text Available Nonlinear vibration of a fluid-conveying pipe subjected to a transverse external harmonic excitation is investigated in the case with two-to-one internal resonance. The excitation amplitude is in the same magnitude of the transverse displacement. The fluid in the pipes flows in the speed larger than the critical speed so that the straight configuration becomes an unstable equilibrium and two curved configurations bifurcate as stable equilibriums. The motion measured from each of curved equilibrium configurations is governed by a nonlinear integro-partial-differential equation with variable coefficients. The Galerkin method is employed to discretize the governing equation into a gyroscopic system consisting of a set of coupled nonlinear ordinary differential equations. The method of multiple scales is applied to analyze approximately the gyroscopic system. A set of first-order ordinary differential equations governing the modulations of the amplitude and the phase are derived via the method. In the supercritical regime, the subharmonic, superharmonic, and combination resonances are examined in the presence of the 2 : 1 internal resonance. The steady-state responses and their stabilities are determined. The various jump phenomena in the amplitude-frequency response curves are demonstrated. The effects of the viscosity, the excitation amplitude, the nonlinearity, and the flow speed are observed. The analytical results are supported by the numerical integration.

Nonlinear Resonance Analysis of Slender Portal Frames under Base Excitation

Directory of Open Access Journals (Sweden)

Luis Fernando Paullo Muñoz

2017-01-01

Full Text Available The dynamic nonlinear response and stability of slender structures in the main resonance regions are a topic of importance in structural analysis. In complex problems, the determination of the response in the frequency domain indirectly obtained through analyses in time domain can lead to huge computational effort in large systems. In nonlinear cases, the response in the frequency domain becomes even more cumbersome because of the possibility of multiple solutions for certain forcing frequencies. Those solutions can be stable and unstable, in particular saddle-node bifurcation at the turning points along the resonance curves. In this work, an incremental technique for direct calculation of the nonlinear response in frequency domain of plane frames subjected to base excitation is proposed. The transformation of equations of motion to the frequency domain is made through the harmonic balance method in conjunction with the Galerkin method. The resulting system of nonlinear equations in terms of the modal amplitudes and forcing frequency is solved by the Newton-Raphson method together with an arc-length procedure to obtain the nonlinear resonance curves. Suitable examples are presented, and the influence of the frame geometric parameters and base motion on the nonlinear resonance curves is investigated.

A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

Science.gov (United States)

Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

2011-01-01

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

Non-linear feedback neural networks VLSI implementations and applications

CERN Document Server

Ansari, Mohd Samar

2014-01-01

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

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Emmanuel García

2014-01-01

Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.

Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks

Science.gov (United States)

2014-03-27

series. Chakraborty et al. (1992) modeled flour prices over an eight year period for the cities of Buffalo, Minneapolis and Kansas City via a neural...on stock and commodity market prices (Kaastra & Boyd, 1996) with a goal of discovering non-linear relationships via ANNs which might provide an...Time Series A vector of past observations from a specific time interval is an example of a time series. For example, monthly stock prices from 2000

Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

Directory of Open Access Journals (Sweden)

Bahita Mohamed

2011-01-01

Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

Science.gov (United States)

Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

2017-12-01

We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.

Science.gov (United States)

Hutt, Axel; Buhry, Laure

2014-12-01

Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.

Multidimensional Plasma Wake Excitation in the Non-linear Blowout Regime

CERN Document Server

Vieira, J.; Silva, L.O.

2016-01-01

Plasma accelerators can sustain very high acceleration gradients. They are promising candidates for future generations of particle accelerators for sev- eral scientific, medical and technological applications. Current plasma based acceleration experiments operate in the relativistic regime, where the plasma response is strongly non-linear. We outline some of the key properties of wake- field excitation in these regimes. We outline a multidimensional theory for the excitation of plasma wakefields in connection with current experiments. We then use these results and provide design guidelines for the choice of laser and plasma parameters ensuring a stable laser wakefield accelerator that maximizes the quality of the accelerated electrons. We also mention some of the future challenges associated with this technology.

Enhancement of nonlinear optical response of weakly confined excitons in GaAs thin films by spectrally rectangle-shape-pulse-excitation

International Nuclear Information System (INIS)

Kojima, O; Isu, T; Ishi-Hayase, J; Sasaki, M; Tsuchiya, M

2007-01-01

We report the enhancement of the nonlinear optical response of the weakly confined excitons with use of spectrally rectangular pulse. The nonlinear optical response was investigated as a function of excitation energy by a degenerate four-wave-mixing (DFWM) technique. In the case that the laser pulse with the controlled spectral shape excites the plural exciton states simultaneously, the DFWM signal intensity is enhanced by a factor of two in comparison with the intensity under the excitation of a single exciton state. This enhancement is caused by the superposition of the nonlinear optical responses from the plural exciton states

FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control.

Science.gov (United States)

Kalkkuhl, J; Hunt, K J; Fritz, H

1999-01-01

An finite-element methods (FEM)-based neural-network approach to Nonlinear AutoRegressive with eXogenous input (NARX) modeling is presented. The method uses multilinear interpolation functions on C0 rectangular elements. The local and global structure of the resulting model is analyzed. It is shown that the model can be interpreted both as a local model network and a single layer feedforward neural network. The main aim is to use the model for nonlinear control design. The proposed FEM NARX description is easily accessible to feedback linearizing control techniques. Its use with a two-degrees of freedom nonlinear internal model controller is discussed. The approach is applied to modeling of the nonlinear longitudinal dynamics of an experimental lorry, using measured data. The modeling results are compared with local model network and multilayer perceptron approaches. A nonlinear speed controller was designed based on the identified FEM model. The controller was implemented in a test vehicle, and several experimental results are presented.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

Energy Technology Data Exchange (ETDEWEB)

Olyaee, Saeed; Hamedi, Samaneh, E-mail: s_olyaee@srttu.edu [Nano-photonics and Optoelectronics Research Laboratory (NORLab), Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 16788, Tehran (Iran, Islamic Republic of)

2011-02-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

International Nuclear Information System (INIS)

Olyaee, Saeed; Hamedi, Samaneh

2011-01-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

Non-Linear State Estimation Using Pre-Trained Neural Networks

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2010-01-01

effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

Local and global nonlinear dynamics of a parametrically excited rectangular symmetric cross-ply laminated composite plate

International Nuclear Information System (INIS)

Ye Min; Lu Jing; Zhang Wei; Ding Qian

2005-01-01

The present investigation deals with nonlinear dynamic behavior of a parametrically excited simply supported rectangular symmetric cross-ply laminated composite thin plate for the first time. The governing equation of motion for rectangular symmetric cross-ply laminated composite thin plate is derived by using von Karman equation. The geometric nonlinearity and nonlinear damping are included in the governing equations of motion. The Galerkin approach is used to obtain a two-degree-of-freedom nonlinear system under parametric excitation. The method of multiple scales is utilized to transform the second-order non-autonomous differential equations to the first-order averaged equations. Using numerical method, the averaged equations are analyzed to obtain the steady state bifurcation responses. The analysis of stability for steady state bifurcation responses in laminated composite thin plate is also given. Under certain conditions laminated composite thin plate may have two or multiple steady state bifurcation solutions. Jumping phenomenon occurs in the steady state bifurcation solutions. The chaotic motions of rectangular symmetric cross-ply laminated composite thin plate are also found by using numerical simulation. The results obtained here demonstrate that the periodic, quasi-periodic and chaotic motions coexist for a parametrically excited fore-edge simply supported rectangular symmetric cross-ply laminated composite thin plate under certain conditions

Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

International Nuclear Information System (INIS)

Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

2007-01-01

In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

Pinning Synchronization of Delayed Neural Networks with Nonlinear Inner-Coupling

Directory of Open Access Journals (Sweden)

Yangling Wang

2011-01-01

Full Text Available Without assuming the symmetry and irreducibility of the outer-coupling weight configuration matrices, we investigate the pinning synchronization of delayed neural networks with nonlinear inner-coupling. Some delay-dependent controlled stability criteria in terms of linear matrix inequality (LMI are obtained. An example is presented to show the application of the criteria obtained in this paper.

Absolute stability of nonlinear systems with time delays and applications to neural networks

Directory of Open Access Journals (Sweden)

Xinzhi Liu

2001-01-01

Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

Nonlinear vibration behaviors of suspended cables under two-frequency excitation with temperature effects

Science.gov (United States)

Zhao, Yaobing; Huang, Chaohui; Chen, Lincong; Peng, Jian

2018-03-01

The aim of this paper is to investigate temperature effects on the nonlinear vibration behaviors of suspended cables under two-frequency excitation. For this purpose, two combination and simultaneous resonances are chosen and studied in detail. First of all, based on the assumptions of the temperature effects, the partial differential equations of the in-plane and out-of-plane motions with thermal effects under multi-frequency excitations are obtained. The Galerkin method is adopted to discretize the nonlinear dynamic equations, and the single-mode planar discretization is considered. Then, in the absence of the primary and internal resonances, the frequency response equations are obtained by using the multiple scales method. The stability analyses are conducted via investigating the nature of the singular points of equations. After that, temperature effects on nonlinear vibration characteristics of the first symmetric mode are studied. Parametric investigations of temperature effects on corresponding non-dimensional factors and coefficients of linear and nonlinear terms are performed. Numerical results are presented to show the temperature effects via the frequency-response curves and detuning-phase curves of four different sag-to-span ratios. It is found out that effects of temperature variations would lead to significant quantitative and/or qualitative changes of the nonlinear vibration properties, and these effects are closely related to the sag-to-span ratio and the degree of the temperature variation. Specifically, the softening/hardening-type spring behaviors, the response amplitude, the range of the resonance, the intersection and number of branches, the number and phase of the steady-state solutions are all affected by the temperature changes.

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

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Luis Gonzaga Baca Ruiz

2016-08-01

Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

Nonlinear dynamics and bifurcation characteristics of shape memory alloy thin films subjected to in-plane stochastic excitation

International Nuclear Information System (INIS)

Zhu, Zhi-Wen; Zhang, Qing-Xin; Xu, Jia

2014-01-01

A kind of shape memory alloy (SMA) hysteretic nonlinear model was developed, and the nonlinear dynamics and bifurcation characteristics of the SMA thin film subjected to in-plane stochastic excitation were investigated. Van der Pol difference item was introduced to describe the hysteretic phenomena of the SMA strain–stress curves, and the nonlinear dynamic model of the SMA thin film subjected to in-plane stochastic excitation was developed. The conditions of global stochastic stability of the system were determined in singular boundary theory, and the probability density function of the system response was obtained. Finally, the conditions of stochastic Hopf bifurcation were analyzed. The results of theoretical analysis and numerical simulation indicate that self-excited vibration is induced by the hysteretic nonlinear characteristics of SMA, and stochastic Hopf bifurcation appears when the bifurcation parameter was changed; there are two limit cycles in the stationary probability density of the dynamic response of the system in some cases, which means that there are two vibration amplitudes whose probabilities are both very high, and jumping phenomena between the two vibration amplitudes appear with the change in conditions. The results obtained in this current paper are helpful for the application of the SMA thin film in stochastic vibration fields. - Highlights: • Hysteretic nonlinear model of shape memory alloy was developed. • Van der Pol item was introduced to interpret hysteretic strain–stress curves. • Nonlinear dynamic characteristics of the shape memory alloy film were analyzed. • Jumping phenomena were observed in the change of the parameters

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

Science.gov (United States)

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Analysis of the power flow in nonlinear oscillators driven by random excitation using the first Wiener kernel

Science.gov (United States)

Hawes, D. H.; Langley, R. S.

2018-01-01

Random excitation of mechanical systems occurs in a wide variety of structures and, in some applications, calculation of the power dissipated by such a system will be of interest. In this paper, using the Wiener series, a general methodology is developed for calculating the power dissipated by a general nonlinear multi-degree-of freedom oscillatory system excited by random Gaussian base motion of any spectrum. The Wiener series method is most commonly applied to systems with white noise inputs, but can be extended to encompass a general non-white input. From the extended series a simple expression for the power dissipated can be derived in terms of the first term, or kernel, of the series and the spectrum of the input. Calculation of the first kernel can be performed either via numerical simulations or from experimental data and a useful property of the kernel, namely that the integral over its frequency domain representation is proportional to the oscillating mass, is derived. The resulting equations offer a simple conceptual analysis of the power flow in nonlinear randomly excited systems and hence assist the design of any system where power dissipation is a consideration. The results are validated both numerically and experimentally using a base-excited cantilever beam with a nonlinear restoring force produced by magnets.

Subharmonic response of a single-degree-of-freedom nonlinear vibro-impact system to a narrow-band random excitation.

Science.gov (United States)

Haiwu, Rong; Wang, Xiangdong; Xu, Wei; Fang, Tong

2009-08-01

The subharmonic response of single-degree-of-freedom nonlinear vibro-impact oscillator with a one-sided barrier to narrow-band random excitation is investigated. The narrow-band random excitation used here is a filtered Gaussian white noise. The analysis is based on a special Zhuravlev transformation, which reduces the system to one without impacts, or velocity jumps, thereby permitting the applications of asymptotic averaging over the "fast" variables. The averaged stochastic equations are solved exactly by the method of moments for the mean-square response amplitude for the case of linear system with zero offset. A perturbation-based moment closure scheme is proposed and the formula of the mean-square amplitude is obtained approximately for the case of linear system with nonzero offset. The perturbation-based moment closure scheme is used once again to obtain the algebra equation of the mean-square amplitude of the response for the case of nonlinear system. The effects of damping, detuning, nonlinear intensity, bandwidth, and magnitudes of random excitations are analyzed. The theoretical analyses are verified by numerical results. Theoretical analyses and numerical simulations show that the peak amplitudes may be strongly reduced at large detunings or large nonlinear intensity.

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

Science.gov (United States)

Bouchard, M

2001-01-01

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

Science.gov (United States)

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Comparison among nonlinear excitation control strategies used for damping power system oscillations

International Nuclear Information System (INIS)

Leon, A.E.; Solsona, J.A.; Valla, M.I.

2012-01-01

Highlights: ► A description and comparison of nonlinear control strategies for synchronous generators are presented. ► Advantages of using nonlinear controllers are emphasized against the use of classical PSSs. ► We find that a particular selection of IDA gains achieve the same performance that FL controllers. - Abstract: This work is focused on the problem of power system stability. A thorough description of nonlinear control strategies for synchronous generator excitation, which are designed for damping oscillations and improving transient stability on power systems, is presented along with a detailed comparison among these modern strategies and current solutions based on power system stabilizers. The performance related to damping injection in each controller, critical time enhancement, robustness against parametric uncertainties, and control signal energy consumption is analyzed. Several tests are presented to validate discussions on various advantages and disadvantages of each control strategy.

Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

Directory of Open Access Journals (Sweden)

Jidong Wang

2017-01-01

Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

An analysis of nonlinear dynamics underlying neural activity related to auditory induction in the rat auditory cortex.

Science.gov (United States)

Noto, M; Nishikawa, J; Tateno, T

2016-03-24

A sound interrupted by silence is perceived as discontinuous. However, when high-intensity noise is inserted during the silence, the missing sound may be perceptually restored and be heard as uninterrupted. This illusory phenomenon is called auditory induction. Recent electrophysiological studies have revealed that auditory induction is associated with the primary auditory cortex (A1). Although experimental evidence has been accumulating, the neural mechanisms underlying auditory induction in A1 neurons are poorly understood. To elucidate this, we used both experimental and computational approaches. First, using an optical imaging method, we characterized population responses across auditory cortical fields to sound and identified five subfields in rats. Next, we examined neural population activity related to auditory induction with high temporal and spatial resolution in the rat auditory cortex (AC), including the A1 and several other AC subfields. Our imaging results showed that tone-burst stimuli interrupted by a silent gap elicited early phasic responses to the first tone and similar or smaller responses to the second tone following the gap. In contrast, tone stimuli interrupted by broadband noise (BN), considered to cause auditory induction, considerably suppressed or eliminated responses to the tone following the noise. Additionally, tone-burst stimuli that were interrupted by notched noise centered at the tone frequency, which is considered to decrease the strength of auditory induction, partially restored the second responses from the suppression caused by BN. To phenomenologically mimic the neural population activity in the A1 and thus investigate the mechanisms underlying auditory induction, we constructed a computational model from the periphery through the AC, including a nonlinear dynamical system. The computational model successively reproduced some of the above-mentioned experimental results. Therefore, our results suggest that a nonlinear, self-exciting

The reliability of nonlinear least-squares algorithm for data analysis of neural response activity during sinusoidal rotational stimulation in semicircular canal neurons.

Science.gov (United States)

Ren, Pengyu; Li, Bowen; Dong, Shiyao; Chen, Lin; Zhang, Yuelin

2018-01-01

Although many mathematical methods were used to analyze the neural activity under sinusoidal stimulation within linear response range in vestibular system, the reliabilities of these methods are still not reported, especially in nonlinear response range. Here we chose nonlinear least-squares algorithm (NLSA) with sinusoidal model to analyze the neural response of semicircular canal neurons (SCNs) during sinusoidal rotational stimulation (SRS) over a nonlinear response range. Our aim was to acquire a reliable mathematical method for data analysis under SRS in vestibular system. Our data indicated that the reliability of this method in an entire SCNs population was quite satisfactory. However, the reliability was strongly negatively depended on the neural discharge regularity. In addition, stimulation parameters were the vital impact factors influencing the reliability. The frequency had a significant negative effect but the amplitude had a conspicuous positive effect on the reliability. Thus, NLSA with sinusoidal model resulted a reliable mathematical tool for data analysis of neural response activity under SRS in vestibular system and more suitable for those under the stimulation with low frequency but high amplitude, suggesting that this method can be used in nonlinear response range. This method broke out of the restriction of neural activity analysis under nonlinear response range and provided a solid foundation for future study in nonlinear response range in vestibular system.

Nonlinear Analysis of Cable Vibration of a Multispan Cable-Stayed Bridge under Transverse Excitation

Directory of Open Access Journals (Sweden)

Kun Lin

2014-01-01

Full Text Available The nonlinear vibrations of cable in a multispan cable-stayed bridge subjected to transverse excitation are investigated. The MECS (multielements cable system model, where multielements per cable stay are used, is built up and used to analyze the model properties of the multispan cable-stayed bridges. Then, a simplified two-degrees-of-freedom (2-DOFs model, where the tower or the deck is reduced to a beam, is proposed to analyze the nonlinear dynamic behaviors of the beam and cable. The results of MECS model analysis show that the main tower in the multispan cable-stayed bridge is prone to the transverse vibration, and the local vibration of cables only has a little impact on the frequency values of the global modes. The results of simplified model analysis show that the energy can be transformed between the modes of the beam and cable when the nature frequencies of them are very close. On the other hand, with the transverse excitation changing, the cable can exhibit richer quasi-periodic or chaotic motions due to the nonlinear terms caused by the coupled mode between the beam and cable.

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.

New evolution equations for the joint response-excitation probability density function of stochastic solutions to first-order nonlinear PDEs

Science.gov (United States)

Venturi, D.; Karniadakis, G. E.

2012-08-01

By using functional integral methods we determine new evolution equations satisfied by the joint response-excitation probability density function (PDF) associated with the stochastic solution to first-order nonlinear partial differential equations (PDEs). The theory is presented for both fully nonlinear and for quasilinear scalar PDEs subject to random boundary conditions, random initial conditions or random forcing terms. Particular applications are discussed for the classical linear and nonlinear advection equations and for the advection-reaction equation. By using a Fourier-Galerkin spectral method we obtain numerical solutions of the proposed response-excitation PDF equations. These numerical solutions are compared against those obtained by using more conventional statistical approaches such as probabilistic collocation and multi-element probabilistic collocation methods. It is found that the response-excitation approach yields accurate predictions of the statistical properties of the system. In addition, it allows to directly ascertain the tails of probabilistic distributions, thus facilitating the assessment of rare events and associated risks. The computational cost of the response-excitation method is order magnitudes smaller than the one of more conventional statistical approaches if the PDE is subject to high-dimensional random boundary or initial conditions. The question of high-dimensionality for evolution equations involving multidimensional joint response-excitation PDFs is also addressed.

Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

Directory of Open Access Journals (Sweden)

Chaojiao Sun

2016-01-01

Full Text Available An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

Passivation and control of partially known SISO nonlinear systems via dynamic neural networks

Directory of Open Access Journals (Sweden)

Reyes-Reyes J.

2000-01-01

Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.

Nonlinear optical measurements of conducting copolymers of aniline under CW laser excitation

Science.gov (United States)

Pramodini, S.; Poornesh, P.

2015-08-01

Synthesis and measurements of third-order optical nonlinearity and optical limiting of conducting copolymers of aniline are presented. Single beam z-scan technique was employed for the nonlinear optical studies. Continuous wave He-Ne laser operating at 633 nm was used as the source of excitation. Copolymer samples exhibited reverse saturable absorption (RSA) process. The nonlinear refraction studies depict that the copolymers exhibit self-defocusing property. The estimated values of βeff, n2 and χ(3) were found to be of the order of 10-2 cm/W, 10-5 esu and 10-7 esu respectively. Self-diffraction rings were observed due to refractive index change when exposed to the laser beam. A good optical limiting and clamping of power of ∼0.9 mW and ∼0.05 mW was observed. Therefore, copolymers of aniline emerge as a potential candidate for photonic device applications.

Approximate Forward Difference Equations for the Lower Order Non-Stationary Statistics of Geometrically Non-Linear Systems subject to Random Excitation

DEFF Research Database (Denmark)

Köylüoglu, H. U.; Nielsen, Søren R. K.; Cakmak, A. S.

Geometrically non-linear multi-degree-of-freedom (MDOF) systems subject to random excitation are considered. New semi-analytical approximate forward difference equations for the lower order non-stationary statistical moments of the response are derived from the stochastic differential equations...... of motion, and, the accuracy of these equations is numerically investigated. For stationary excitations, the proposed method computes the stationary statistical moments of the response from the solution of non-linear algebraic equations....

Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

International Nuclear Information System (INIS)

Benmouiza, Khalil; Cheknane, Ali

2013-01-01

Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results

Nonlinear Dynamic Analysis of Telescopic Mechanism for Truss Structure Bridge Inspection Vehicle Under Pedestrian Excitation

Directory of Open Access Journals (Sweden)

Wenwen Sui

Full Text Available Abstract Nonlinear dynamic analysis of an axially moving telescopic mechanism for truss structure bridge inspection vehicle under pedestrian excitation is carried out. A biomechanically inspired inverted-pendulum model is utilized to simplify the pedestrian. The nonlinear equations of motion for the beam-pedestrian system are derived using the Hamilton's principle. The equations are transformed into two ordinary differential equations by applying the Galerkin's method at the first two orders. The solutions to the equations are acquired by using the Newmark-β method associated with the Newton-Raphson method. The time-dependent feature of the eigenfunctions for the two beams are taken into consideration in the solutions. Accordingly, the equations of motion for a simplified system, in which the pedestrian is regarded as moving cart, are given. In the numerical examples, dynamic responses of the telescopic mechanism in eight conditions of different beam-telescoping and pedestrian-moving directions are simulated. Comparisons between the vibrations of the beams under pedestrian excitation and corresponding moving cart are carried out to investigate the influence of the pedestrian excitation on the telescopic mechanism. The results show that the displacement of the telescopic mechanism under pedestrian excitation is smaller than that under moving cart especially when the pedestrian approaches the beams end. Additionally, compared with moving cart, the pedestrian excitation can effectively strengthen the vibration when the beam extension is small or when the pedestrian is close to the beams end.

Synchronization of chaotic recurrent neural networks with time-varying delays using nonlinear feedback control

International Nuclear Information System (INIS)

Cui Baotong; Lou Xuyang

2009-01-01

In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme

Dynamics of Nonlinear Excitation of the High-Order Mode in a Single-Mode Step-Index Optical Fiber

Science.gov (United States)

Burdin, V.; Bourdine, A.

2018-04-01

This work is concerned with approximate model of higher-order mode nonlinear excitation in a singlemode silica optical fiber. We present some results of simulation for step-index optical fiber under femtosecond optical pulse launching, which confirm ability of relatively stable higher-order mode excitation in such singlemode optical fiber over sufficiently narrow range of launched optical power variation.

Neural network based adaptive control for nonlinear dynamic regimes

Science.gov (United States)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

Science.gov (United States)

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Linear and nonlinear excitations in two stacks of parallel arrays of long Josephson junctions

DEFF Research Database (Denmark)

Carapella, G.; Constabile, Giovanni; Latempa, R.

2000-01-01

We investigate a structure consisting of two parallel arrays of long Josephson junctions sharing a common electrode that allows inductive coupling between the arrays. A model for this structure is derived starting from the description of its continuous limit. The excitation of linear cavity modes...... known from continuous and discrete systems as well as the excitation of a new state exhibiting synchronization in two dimensions are inferred from the mathematical model of the system. The stable nonlinear solution of the coupled sine-Gordon equations describing the system is found to consist...

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Science.gov (United States)

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

Bifurcation and chaos in neural excitable system

International Nuclear Information System (INIS)

Jing Zhujun; Yang Jianping; Feng Wei

2006-01-01

In this paper, we investigate the dynamical behaviors of neural excitable system without periodic external current (proposed by Chialvo [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] and with periodic external current as system's parameters vary. The existence and stability of three fixed points, bifurcation of fixed points, the conditions of existences of fold bifurcation, flip bifurcation and Hopf bifurcation are derived by using bifurcation theory and center manifold theorem. The chaotic existence in the sense of Marotto's definition of chaos is proved. We then give the numerical simulated results (using bifurcation diagrams, computations of Maximum Lyapunov exponent and phase portraits), which not only show the consistence with the analytic results but also display new and interesting dynamical behaviors, including the complete period-doubling and inverse period-doubling bifurcation, symmetry period-doubling bifurcations of period-3 orbit, simultaneous occurrence of two different routes (invariant cycle and period-doubling bifurcations) to chaos for a given bifurcation parameter, sudden disappearance of chaos at one critical point, a great abundance of period windows (period 2 to 10, 12, 19, 20 orbits, and so on) in transient chaotic regions with interior crises, strange chaotic attractors and strange non-chaotic attractor. In particular, the parameter k plays a important role in the system, which can leave the chaotic behavior or the quasi-periodic behavior to period-1 orbit as k varies, and it can be considered as an control strategy of chaos by adjusting the parameter k. Combining the existing results in [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] with the new results reported in this paper, a more complete description of the system is now obtained

Extreme nonlinear energy exchanges in a geometrically nonlinear lattice oscillating in the plane

Science.gov (United States)

Zhang, Zhen; Manevitch, Leonid I.; Smirnov, Valeri; Bergman, Lawrence A.; Vakakis, Alexander F.

2018-01-01

We study the in-plane damped oscillations of a finite lattice of particles coupled by linear springs under distributed harmonic excitation. Strong nonlinearity in this system is generated by geometric effects due to the in-plane stretching of the coupling spring elements. The lattice has a finite number of nonlinear transverse standing waves (termed nonlinear normal modes - NNMs), and an equal number of axial linear modes which are nonlinearly coupled to the transverse ones. Nonlinear interactions between the transverse and axial modes under harmonic excitation give rise to unexpected and extreme nonlinear energy exchanges in the lattice. In particular, we directly excite a transverse NNM by harmonic forcing (causing simulataneous indirect excitation of a corresponding axial linear mode due to nonlinear coupling), and identify three energy transfer mechanisms in the lattice. First, we detect the stable response of the directly excited transverse NNM (despite its instability in the absence of forcing), with simultaneous stability of the indirectly excited axial linear mode. Second, by changing the system and forcing parameters we report extreme nonlinear "energy explosions," whereby, after an initial regime of stability, the directly excited transverse NNM loses stability, leading to abrupt excitation of all transverse and axial modes of the lattice, at all possible wave numbers. This strong instability is triggered by the parametric instability of an indirectly excited axial mode which builds energy until the explosion. This is proved through theoretical analysis. Finally, in other parameter ranges we report intermittent, intense energy transfers from the directly excited transverse NNM to a small set of transverse NNMs with smaller wavelengths, and from the indirectly excited axial mode to a small set of axial modes, but with larger wavelengths. These intermittent energy transfers resemble energy cascades occurring in turbulent flows. Our results show that

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.

1990-07-01

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of Neural Network known as the multi-layer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author) 15 refs., 7 figs

Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

Science.gov (United States)

Hwang, Chih-Lyang; Jan, Chau

2016-02-01

At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

Science.gov (United States)

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

Institute of Scientific and Technical Information of China (English)

SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

2004-01-01

Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

The evolution time of nucleon spectrum in residual excited nucleus and calculation of the non-linear effects

International Nuclear Information System (INIS)

Kenzhebaev, Sh.K.; Djuraev, Sh.H.; Mannanov, D.E.; Khugaev, A.V.

1994-01-01

The investigation of nonstationary fermi-gas thermalization of nucleons in the residual excited nucleus as an open nonlinearize system and analytical methods of calculation are presented. (author). 9 refs

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.

Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

DEFF Research Database (Denmark)

Chon, K H; Hoyer, D; Armoundas, A A

1999-01-01

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

Dynamics of excited instantons in the system of forced Gursey nonlinear differential equations

Energy Technology Data Exchange (ETDEWEB)

Aydogmus, F., E-mail: fatma.aydogmus@gmail.com [Istanbul University, Department of Physics, Faculty of Science (Turkey)

2015-02-15

The Gursey model is a 4D conformally invariant pure fermionic model with a nonlinear spinor self-coupled term. Gursey proposed his model as a possible basis for a unitary description of elementary particles following the “Heisenberg dream.” In this paper, we consider the system of Gursey nonlinear differential equations (GNDEs) formed by using the Heisenberg ansatz. We use it to understand how the behavior of spinor-type Gursey instantons can be affected by excitations. For this, the regular and chaotic numerical solutions of forced GNDEs are investigated by constructing their Poincaré sections in phase space. A hierarchical cluster analysis method for investigating the forced GNDEs is also presented.

Motion control of servo cylinder using neural network

International Nuclear Information System (INIS)

Hwang, Un Kyoo; Cho, Seung Ho

2004-01-01

In this paper, a neural network controller that can be implemented in parallel with a PD controller is suggested for motion control of a hydraulic servo cylinder. By applying a self-excited oscillation method, the system design parameters of open loop transfer function of servo cylinder system are identified. Based on system design parameters, the PD gains are determined for the desired closed loop characteristics. The neural network is incorporated with PD control in order to compensate the inherent nonlinearities of hydraulic servo system. As an application example, a motion control using PD-NN has been performed and proved its superior performance by comparing with that of a PD control

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.

1991-01-01

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of neural network known as the multilayer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author). 17 refs, 8 figs, 2 tab

Nonlinear optical effect and excited electron dynamics of semiconductor nanocrystals; Handotai nano kessh no hisenkei kogaku koka to reiki denshi dynamics

Energy Technology Data Exchange (ETDEWEB)

Goto, T. [Tohoku University, Sendai (Japan)

1996-08-20

Investigations were given on nanocrystals of CuCl and CdTe with regard to their nonlinear optical mechanism. The experiment used a femto-second pump probe spectroscope. The experiment on CuCl nanocrystals revealed the following facts: in the case where one photon is absorbed into one nanocrystal, cascade mitigation occurs to the pair of electrons and holes, and exciters; and in the case where two photons are absorbed into one nanocrystal, exciter molecules are made via the pair of electrons and holes and the exciters. Thus, it was found that the optical nonlinearity occurs when more than two photons are absorbed into one nanocrystal, and inter-exciter interactions and formation of the exciter molecules are the physical causes thereof. The experiment on CdTe nanocrystals indicated that electrons and holes produced by laser beam are distributed instantaneously between the size-quantized discrete levels, and that temperature in the electron system drops with lapse of time. 9 refs., 6 figs.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

Science.gov (United States)

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

Nonlinear plasma waves excited near resonance

International Nuclear Information System (INIS)

Cohen, B.I.; Kaufman, A.N.

1977-01-01

The nonlinear resonant response of a uniform plasma to an external plane-wave field is formulated in terms of the mismatch Δ/sub n l/ between the driving frequency and the time-dependent, complex, nonlinear normal mode frequency at the driving wavenumber. This formalism is applied to computer simulations of this process, yielding a deduced nonlinear frequency shift. The time dependence of the nonlinear phenomena, at frequency Δ/sub n l/ and at the bounce frequency of the resonant particles, is analyzed. The interdependence of the nonlinear features is described by means of energy and momentum relations

Pre-Trained Neural Networks used for Non-Linear State Estimation

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2011-01-01

of the paramters in the distribution. This transformation is approximated by a neural network using offline training, which is based on monte carlo sampling. In the paper, there will also be presented a method to construct a flexible distributions well suited for covering the effect of the non-linearities......The paper focuses on nonlinear state estimation assuming non-Gaussian distributions of the states and the disturbances. The posterior distribution and the aposteriori distribution is described by a chosen family of paramtric distributions. The state transformation then results in a transformation...

Non-linear quenching of current fluctuations in a self-exciting homopolar dynamo, proved by feedback system theory

OpenAIRE

A. M. de Paor

1998-01-01

International audience; Hide (Nonlinear Processes in Geophysics, 1998) has produced a new mathematical model of a self-exciting homopolar dynamo driving a series- wound motor, as a continuing contribution to the theory of the geomagnetic field. By a process of exact perturbation analysis, followed by combination and partial solution of differential equations, the complete nonlinear quenching of current fluctuations reported by Hide in the case that a parameter ? has the value 1 is proved via ...

Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

Science.gov (United States)

Long, Lijun; Zhao, Jun

2017-07-01

In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

Directory of Open Access Journals (Sweden)

Fengxia Xu

2014-01-01

Full Text Available U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

Nonlinear systems

CERN Document Server

Palmero, Faustino; Lemos, M; Sánchez-Rey, Bernardo; Casado-Pascual, Jesús

2018-01-01

This book presents an overview of the most recent advances in nonlinear science. It provides a unified view of nonlinear properties in many different systems and highlights many  new developments. While volume 1 concentrates on mathematical theory and computational techniques and challenges, which are essential for the study of nonlinear science, this second volume deals with nonlinear excitations in several fields. These excitations can be localized and transport energy and matter in the form of breathers, solitons, kinks or quodons with very different characteristics, which are discussed in the book. They can also transport electric charge, in which case they are known as polarobreathers or solectrons. Nonlinear excitations can influence function and structure in biology, as for example, protein folding. In crystals and other condensed matter, they can modify transport properties, reaction kinetics and interact with defects. There are also engineering applications in electric lattices, Josephson junction a...

Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

Science.gov (United States)

Li, Shuai; Li, Yangming

2013-10-28

The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Directory of Open Access Journals (Sweden)

Leandro L. S. Linhares

2015-01-01

Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

Nonlinear oscillations of the FitzHugh-Nagumo equations under combined external and two-frequency parametric excitations

International Nuclear Information System (INIS)

Tatchim Bemmo, D.; Siewe Siewe, M.; Tchawoua, C.

2011-01-01

The continuous FitzHugh-Nagumo (FHN for short) model is transformed into modified van der Pol oscillator with asymmetry under external and two-frequency parametric excitations. At the first, the dependence of the solutions on a combined external and two-frequency parametric stimulus forcing is investigated. By using the multiple scale method, ranges of applied current and/or parametric forcing in which nonlinear oscillations are observed are described. Second, when the multiple scale method cannot be used, we numerically prove that in the modified van der Pol oscillator with asymmetry under external and two-frequency parametric excitations, chaos and periodic solution depending on the combination between different frequencies of the model should appear. We also show that the amplitude of the oscillations can be reduced or increased. To do this, we perform the study of the FHN model by choosing a range of parameters exhibiting Hopf bifurcation and two qualitative different regimes in phase portrait. - Highlights: → We model both external and two-frequency parametric excitations in FHN equations. → We examine effects of harmonic forcing on coupled nonlinear oscillator. → Jump and hysteresis phenomena are observed in the dynamical response. → By increasing the constant stimulus we obtain limit cycle. → Some combinations of frequencies produce limit cycle and chaos for other.

Non-linear quenching of current fluctuations in a self-exciting homopolar dynamo, proved by feedback system theory

Science.gov (United States)

de Paor, A. M.

Hide (Nonlinear Processes in Geophysics, 1998) has produced a new mathematical model of a self-exciting homopolar dynamo driving a series- wound motor, as a continuing contribution to the theory of the geomagnetic field. By a process of exact perturbation analysis, followed by combination and partial solution of differential equations, the complete nonlinear quenching of current fluctuations reported by Hide in the case that a parameter ɛ has the value 1 is proved via the Popov theorem from feedback system stability theory.

The Nonlinear Behavior of Vibrational Conveyers with Single-Mass Crank-and-Rod Exciters

Directory of Open Access Journals (Sweden)

G. Füsun Alışverişçi

2012-01-01

Full Text Available The single-mass, crank-and-rod exciters vibrational conveyers have a trough supported on elastic stands which are rigidly fastened to the trough and a supporting frame. The trough is oscillated by a common crank drive. This vibration causes the load to move forward and upward. The moving loads jump periodically and move forward with relatively small vibration. The movement is strictly related to vibrational parameters. This is applicable in laboratory conditions in the industry which accommodate a few grams of loads, up to those that accommodate tons of loading capacity. In this study I explore the transitional behavior across resonance, during the starting of a single degree of freedom vibratory system excited by crank-and-rod. A loaded vibratory conveyor is more safe to start than an empty one. Vibrational conveyers with cubic nonlinear spring and ideal vibration exciter have been analyzed analytically for primary and secondary resonance by the Method of Multiple Scales, and numerically. The approximate analytical results obtained in this study have been compared with the numerical results and have been found to be well matched.

Typology of nonlinear activity waves in a layered neural continuum.

Science.gov (United States)

Koch, Paul; Leisman, Gerry

2006-04-01

Neural tissue, a medium containing electro-chemical energy, can amplify small increments in cellular activity. The growing disturbance, measured as the fraction of active cells, manifests as propagating waves. In a layered geometry with a time delay in synaptic signals between the layers, the delay is instrumental in determining the amplified wavelengths. The growth of the waves is limited by the finite number of neural cells in a given region of the continuum. As wave growth saturates, the resulting activity patterns in space and time show a variety of forms, ranging from regular monochromatic waves to highly irregular mixtures of different spatial frequencies. The type of wave configuration is determined by a number of parameters, including alertness and synaptic conditioning as well as delay. For all cases studied, using numerical solution of the nonlinear Wilson-Cowan (1973) equations, there is an interval in delay in which the wave mixing occurs. As delay increases through this interval, during a series of consecutive waves propagating through a continuum region, the activity within that region changes from a single-frequency to a multiple-frequency pattern and back again. The diverse spatio-temporal patterns give a more concrete form to several metaphors advanced over the years to attempt an explanation of cognitive phenomena: Activity waves embody the "holographic memory" (Pribram, 1991); wave mixing provides a plausible cause of the competition called "neural Darwinism" (Edelman, 1988); finally the consecutive generation of growing neural waves can explain the discontinuousness of "psychological time" (Stroud, 1955).

H∞ Excitation Control Design for Stochastic Power Systems with Input Delay Based on Nonlinear Hamiltonian System Theory

Directory of Open Access Journals (Sweden)

Weiwei Sun

2015-01-01

Full Text Available This paper presents H∞ excitation control design problem for power systems with input time delay and disturbances by using nonlinear Hamiltonian system theory. The impact of time delays introduced by remote signal transmission and processing in wide-area measurement system (WAMS is well considered. Meanwhile, the systems under investigation are disturbed by random fluctuation. First, under prefeedback technique, the power systems are described as a nonlinear Hamiltonian system. Then the H∞ excitation controller of generators connected to distant power systems with time delay and stochasticity is designed. Based on Lyapunov functional method, some sufficient conditions are proposed to guarantee the rationality and validity of the proposed control law. The closed-loop systems under the control law are asymptotically stable in mean square independent of the time delay. And we through a simulation of a two-machine power system prove the effectiveness of the results proposed in this paper.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

Science.gov (United States)

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Non-linear quenching of current fluctuations in a self-exciting homopolar dynamo, proved by feedback system theory

Directory of Open Access Journals (Sweden)

A. M. de Paor

1998-01-01

Full Text Available Hide (Nonlinear Processes in Geophysics, 1998 has produced a new mathematical model of a self-exciting homopolar dynamo driving a series- wound motor, as a continuing contribution to the theory of the geomagnetic field. By a process of exact perturbation analysis, followed by combination and partial solution of differential equations, the complete nonlinear quenching of current fluctuations reported by Hide in the case that a parameter ε has the value 1 is proved via the Popov theorem from feedback system stability theory.

A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.

Science.gov (United States)

Liu, Yan-Jun; Gao, Ying; Tong, Shaocheng; Chen, C L Philip

2016-01-01

In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, the dead zone, in this paper, is a kind of nonlinear dead zone. To overcome the noncausal problem, which leads to the control scheme infeasible, the systems can be transformed into a m -step-ahead predictor. Due to nonlinear dead-zone appearance, the transformed predictor still contains the nonaffine function. In addition, it is assumed that the gain function of dead-zone input and the control direction are unknown. These conditions bring about the difficulties and the complicacy in the controller design. Thus, the implicit function theorem is applied to deal with nonaffine dead-zone appearance, the problem caused by the unknown control direction can be resolved through applying the discrete Nussbaum gain, and the neural networks are used to approximate the unknown function. Based on the Lyapunov theory, all the signals of the resulting closed-loop system are proved to be semiglobal uniformly ultimately bounded. Moreover, the tracking error is proved to be regulated to a small neighborhood around zero. The feasibility of the proposed approach is demonstrated by a simulation example.

Nonlinear analysis and synthesis of video images using deep dynamic bottleneck neural networks for face recognition.

Science.gov (United States)

Moghadam, Saeed Montazeri; Seyyedsalehi, Seyyed Ali

2018-05-31

Nonlinear components extracted from deep structures of bottleneck neural networks exhibit a great ability to express input space in a low-dimensional manifold. Sharing and combining the components boost the capability of the neural networks to synthesize and interpolate new and imaginary data. This synthesis is possibly a simple model of imaginations in human brain where the components are expressed in a nonlinear low dimensional manifold. The current paper introduces a novel Dynamic Deep Bottleneck Neural Network to analyze and extract three main features of videos regarding the expression of emotions on the face. These main features are identity, emotion and expression intensity that are laid in three different sub-manifolds of one nonlinear general manifold. The proposed model enjoying the advantages of recurrent networks was used to analyze the sequence and dynamics of information in videos. It is noteworthy to mention that this model also has also the potential to synthesize new videos showing variations of one specific emotion on the face of unknown subjects. Experiments on discrimination and recognition ability of extracted components showed that the proposed model has an average of 97.77% accuracy in recognition of six prominent emotions (Fear, Surprise, Sadness, Anger, Disgust, and Happiness), and 78.17% accuracy in the recognition of intensity. The produced videos revealed variations from neutral to the apex of an emotion on the face of the unfamiliar test subject which is on average 0.8 similar to reference videos in the scale of the SSIM method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

Science.gov (United States)

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

Directory of Open Access Journals (Sweden)

Vandana Sakhre

2015-01-01

Full Text Available Fuzzy Counter Propagation Neural Network (FCPN controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL. FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN and Back Propagation Network (BPN on the basis of Mean Absolute Error (MAE, Mean Square Error (MSE, Best Fit Rate (BFR, and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO and a single input and single output (SISO gas furnace Box-Jenkins time series data.

Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

Science.gov (United States)

Soloway, Donald I.; Bialasiewicz, Jan T.

1992-01-01

A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes

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Sergey M Plis

2010-11-01

Full Text Available The combined analysis of MEG/EEG and functional MRI measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the BOLD response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater SNR, that confirms the expectation arising from the nature of the experiment. The highly nonlinear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

A novel neural-net-based nonlinear adaptive control and application to the cross-direction deviations control of a polymer film spread line

International Nuclear Information System (INIS)

Chen Zengqiang; Li Xiang; Liu Zhongxin; Yuan Zhuzhi

2008-01-01

A novel neural adaptive controller is presented to effectively control multivariable nonlinear systems. The proposed neural controller has been successfully applied to the cross-direction deviation control system of a polymer film spread line, whose good performance has been verified with real-time running results

A novel method to produce nonlinear empirical physical formulas for experimental nonlinear electro-optical responses of doped nematic liquid crystals: Feedforward neural network approach

Energy Technology Data Exchange (ETDEWEB)

Yildiz, Nihat, E-mail: nyildiz@cumhuriyet.edu.t [Cumhuriyet University, Faculty of Science and Literature, Department of Physics, 58140 Sivas (Turkey); San, Sait Eren; Okutan, Mustafa [Department of Physics, Gebze Institute of Technology, P.O. Box 141, Gebze 41400, Kocaeli (Turkey); Kaya, Hueseyin [Cumhuriyet University, Faculty of Science and Literature, Department of Physics, 58140 Sivas (Turkey)

2010-04-15

Among other significant obstacles, inherent nonlinearity in experimental physical response data poses severe difficulty in empirical physical formula (EPF) construction. In this paper, we applied a novel method (namely layered feedforward neural network (LFNN) approach) to produce explicit nonlinear EPFs for experimental nonlinear electro-optical responses of doped nematic liquid crystals (NLCs). Our motivation was that, as we showed in a previous theoretical work, an appropriate LFNN, due to its exceptional nonlinear function approximation capabilities, is highly relevant to EPF construction. Therefore, in this paper, we obtained excellently produced LFNN approximation functions as our desired EPFs for above-mentioned highly nonlinear response data of NLCs. In other words, by using suitable LFNNs, we successfully fitted the experimentally measured response and predicted the new (yet-to-be measured) response data. The experimental data (response versus input) were diffraction and dielectric properties versus bias voltage; and they were all taken from our previous experimental work. We conclude that in general, LFNN can be applied to construct various types of EPFs for the corresponding various nonlinear physical perturbation (thermal, electronic, molecular, electric, optical, etc.) data of doped NLCs.

A novel method to produce nonlinear empirical physical formulas for experimental nonlinear electro-optical responses of doped nematic liquid crystals: Feedforward neural network approach

International Nuclear Information System (INIS)

Yildiz, Nihat; San, Sait Eren; Okutan, Mustafa; Kaya, Hueseyin

2010-01-01

Among other significant obstacles, inherent nonlinearity in experimental physical response data poses severe difficulty in empirical physical formula (EPF) construction. In this paper, we applied a novel method (namely layered feedforward neural network (LFNN) approach) to produce explicit nonlinear EPFs for experimental nonlinear electro-optical responses of doped nematic liquid crystals (NLCs). Our motivation was that, as we showed in a previous theoretical work, an appropriate LFNN, due to its exceptional nonlinear function approximation capabilities, is highly relevant to EPF construction. Therefore, in this paper, we obtained excellently produced LFNN approximation functions as our desired EPFs for above-mentioned highly nonlinear response data of NLCs. In other words, by using suitable LFNNs, we successfully fitted the experimentally measured response and predicted the new (yet-to-be measured) response data. The experimental data (response versus input) were diffraction and dielectric properties versus bias voltage; and they were all taken from our previous experimental work. We conclude that in general, LFNN can be applied to construct various types of EPFs for the corresponding various nonlinear physical perturbation (thermal, electronic, molecular, electric, optical, etc.) data of doped NLCs.

Excitation of nonlinear wave patterns in flowing complex plasmas

Science.gov (United States)

Jaiswal, S.; Bandyopadhyay, P.; Sen, A.

2018-01-01

We describe experimental observations of nonlinear wave structures excited by a supersonic mass flow of dust particles over an electrostatic potential hill in a dusty plasma medium. The experiments have been carried out in a Π- shaped experimental (DPEx) device in which micron sized Kaolin particles are embedded in a DC glow discharge Argon plasma. An equilibrium dust cloud is formed by maintaining the pumping speed and gas flow rate and the dust flow is induced either by suddenly reducing the height of a potential hill or by suddenly reducing the gas flow rate. For a supersonic flow of the dust fluid precursor solitons are seen to propagate in the upstream direction while wake structures propagate in the downstream direction. For flow speeds with a Mach number greater than 2 the dust particles flowing over the potential hill give rise to dispersive dust acoustic shock waves. The experimental results compare favorably with model theories based on forced K-dV and K-dV Burger's equations.

Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network

Directory of Open Access Journals (Sweden)

Shaohua Luo

2014-01-01

Full Text Available This paper is concerned with the problem of the nonlinear dynamic surface control (DSC of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

A simple predistortion technique for suppression of nonlinear effects in periodic signals generated by nonlinear transducers

Science.gov (United States)

Novak, A.; Simon, L.; Lotton, P.

2018-04-01

Mechanical transducers, such as shakers, loudspeakers and compression drivers that are used as excitation devices to excite acoustical or mechanical nonlinear systems under test are imperfect. Due to their nonlinear behaviour, unwanted contributions appear at their output besides the wanted part of the signal. Since these devices are used to study nonlinear systems, it should be required to measure properly the systems under test by overcoming the influence of the nonlinear excitation device. In this paper, a simple method that corrects distorted output signal of the excitation device by means of predistortion of its input signal is presented. A periodic signal is applied to the input of the excitation device and, from analysing the output signal of the device, the input signal is modified in such a way that the undesirable spectral components in the output of the excitation device are cancelled out after few iterations of real-time processing. The experimental results provided on an electrodynamic shaker show that the spectral purity of the generated acceleration output approaches 100 dB after few iterations (1 s). This output signal, applied to the system under test, is thus cleaned from the undesirable components produced by the excitation device; this is an important condition to ensure a correct measurement of the nonlinear system under test.

Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults.

Science.gov (United States)

Li, Yongming; Tong, Shaocheng

The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks

Science.gov (United States)

Rozdeba, Paul J.

The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems. One of the aims of this dissertation is to simultaneously analyze state and parameter estimation in as a regularized inverse problem, where the introduction of a model makes it possible to reverse the forward problem of partial, noisy observation; and as a statistical inference problem using data assimilation to transfer information from measurements to the model states and parameters. Ultimately these two formulations achieve the same goal. Similar aspects that appear in both are highlighted as a means for better understanding the structure of the nonlinear inference problem. An alternative approach to data assimilation that uses model reduction is then examined as a way to eliminate unresolved nonlinear gating variables from neuron models. In this formulation, only measured variables enter into the model, and the resulting errors are themselves modeled by nonlinear stochastic processes with memory. Finally, variational annealing, a data assimilation method previously applied to dynamical systems, is introduced as a potentially useful tool for understanding deep neural network training in machine learning by exploiting similarities between the two problems.

Generating Localized Nonlinear Excitations in the Fermi-Pasta-Ulam-Tsingou chains

Science.gov (United States)

Westley, Alexandra; Sen, Surajit

Here, we will discuss properties of energy trapping in the decorated Fermi-Pasta-Ulam-Tsingou (FPUT) mass-spring chains with quadratic and quartic coupling terms. It is well-known that the FPUT system admits highly localized nonlinear excitations (LNE) which are stable for long periods of time. We seek to generate these LNEs at will by creating regions in the chain of stiffer or softer springs, or by placing mass impurities throughout. We will show that NLEs tend to coalesce in regions of stiff springs from random perturbations throughout the system. These locations may serve as extremely powerful energy traps or heat sinks in certain materials. Furthermore, we will demonstrate that this process occurs by means of trapping solitary (or anti-solitary) waves into tight spaces.

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

Science.gov (United States)

Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

Morphological neural networks

Energy Technology Data Exchange (ETDEWEB)

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

Simplified LQG Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

Nonlinear quenching of current fluctuations in a self-exciting homopolar dynamo

Science.gov (United States)

Hide, R.

In the interpretation of geomagnetic polarity reversals with their highly variable frequency over geological time it is necessary, as with other irregularly fluctuating geophysical phenomena, to consider the relative importance of forced contributions associated with changing boundary conditions and of free contributions characteristic of the behaviour of nonlinear systems operating under fixed boundary conditions. New evidence -albeit indirect- in favour of the likely predominance of forced contributions is provided by the discovery reported here of the possibility of complete quenching by nonlineax effects of current fluctuations in a self-exciting homopolar dynamo with its single Faraday disk driven into rotation with angular speed y(τ) (where τ denotes time) by a steady applied couple. The armature of an electric motor connected in series with the coil of the dynamo is driven into rotation' with angular speed z(τ) by a torque xf (x) due to Lorentz forces associated with the electric current x(τ) in the system (just as certain parts of the spectrum of eddies within the liquid outer core are generated largely by Lorentz forces associated with currents generated by the self-exciting magnetohydrodynamic (MHD) geodynamo). The discovery is based on bifurcation analysis supported by computational studies of the following (mathematically novel) autonomous set of nonlinear ordinary differential equations: dx/dt = x(y - 1) - βzf(x), dy/dt = α(1 - x²) - κy, dz/dt = xf (x) -λz, where f (x) = 1 - ɛ + ɛσx, in cases when the dimensionless parameters (α, β, κ, λ, σ) are all positive and 0 ≤ ɛ ≤ 1. Within those regions of (α, β, κ, λ, σ) parameter space where the applied couple, as measured by α, is strong enough for persistent dynamo action (i.e. x ≠ 0) to occur at all, there are in general extensive regions where x(τ) exhibits large amplitude regular or irregular (chaotic) fluctuations. But these fluctuating régimes shrink in size as increases

Optical nonlinearities of colloidal InP@ZnS core-shell quantum dots probed by Z-scan and two-photon excited emission

International Nuclear Information System (INIS)

Wawrzynczyk, Dominika; Szeremeta, Janusz; Samoc, Marek; Nyk, Marcin

2015-01-01

Spectrally resolved nonlinear optical properties of colloidal InP@ZnS core-shell quantum dots of various sizes were investigated with the Z-scan technique and two-photon fluorescence excitation method using a femtosecond laser system tunable in the range from 750 nm to 1600 nm. In principle, both techniques should provide comparable results and can be interchangeably used for determination of the nonlinear optical absorption parameters, finding maximal values of the cross sections and optimizing them. We have observed slight differences between the two-photon absorption cross sections measured by the two techniques and attributed them to the presence of non-radiative paths of absorption or relaxation. The most significant value of two-photon absorption cross section σ 2 for 4.3 nm size InP@ZnS quantum dot was equal to 2200 GM, while the two-photon excitation action cross section σ 2 Φ was found to be 682 GM at 880 nm. The properties of these cadmium-free colloidal quantum dots can be potentially useful for nonlinear bioimaging

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

Science.gov (United States)

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

Science.gov (United States)

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Neural Networks for Optimal Control

DEFF Research Database (Denmark)

Sørensen, O.

1995-01-01

Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

Self-Organized Biological Dynamics and Nonlinear Control

Science.gov (United States)

Walleczek, Jan

2006-04-01

The frontiers and challenges of biodynamics research Jan Walleczek; Part I. Nonlinear Dynamics in Biology and Response to Stimuli: 1. External signals and internal oscillation dynamics - principal aspects and response of stimulated rhythmic processes Friedemann Kaiser; 2. Nonlinear dynamics in biochemical and biophysical systems: from enzyme kinetics to epilepsy Raima Larter, Robert Worth and Brent Speelman; 3. Fractal mechanisms in neural control: human heartbeat and gait dynamics in health and disease Chung-Kang Peng, Jeffrey M. Hausdorff and Ary L. Goldberger; 4. Self-organising dynamics in human coordination and perception Mingzhou Ding, Yanqing Chen, J. A. Scott Kelso and Betty Tuller; 5. Signal processing in biochemical reaction networks Adam P. Arkin; Part II. Nonlinear Sensitivity of Biological Systems to Electromagnetic Stimuli: 6. Electrical signal detection and noise in systems with long-range coherence Paul C. Gailey; 7. Oscillatory signals in migrating neutrophils: effects of time-varying chemical and electrical fields Howard R. Petty; 8. Enzyme kinetics and nonlinear biochemical amplification in response to static and oscillating magnetic fields Jan Walleczek and Clemens F. Eichwald; 9. Magnetic field sensitivity in the hippocampus Stefan Engström, Suzanne Bawin and W. Ross Adey; Part III. Stochastic Noise-Induced Dynamics and Transport in Biological Systems: 10. Stochastic resonance: looking forward Frank Moss; 11. Stochastic resonance and small-amplitude signal transduction in voltage-gated ion channels Sergey M. Bezrukov and Igor Vodyanoy; 12. Ratchets, rectifiers and demons: the constructive role of noise in free energy and signal transduction R. Dean Astumian; 13. Cellular transduction of periodic and stochastic energy signals by electroconformational coupling Tian Y. Tsong; Part IV. Nonlinear Control of Biological and Other Excitable Systems: 14. Controlling chaos in dynamical systems Kenneth Showalter; 15. Electromagnetic fields and biological

Linear modeling of nonlinear systems using artificial neural networks based on I/O data and its application in power plant boiler modeling

International Nuclear Information System (INIS)

Ghaffari, A.; Nikkhah Bahrami, M.; Mohammadzaheri, M.

2005-01-01

In this paper a new method for linear modeling of nonlinear systems is presented. The method is based on the design of an artificial neural network with two layers. The network is trained only according to the input-output data of the system. The weights of connections in this network, represents the coefficients of the transfer function. For systems with linear behavior the method of least square error represents the best linear model of the system. However, for nonlinear systems, such as some subsystems in power plants boilers LSE does not represent the best linear approximation of the system, necessarily. In this paper a new linear modeling method is presented and applied to some subsystems in a power plant boiler. Comparison between the transfer function obtained in this way and by least square error method,shows that the neural network method gives better linear models for these nonlinear systems

A Nonlinear Autoregressive Exogenous (NARX Neural Network Model for the Prediction of the Daily Direct Solar Radiation

Directory of Open Access Journals (Sweden)

Zina Boussaada

2018-03-01

Full Text Available The solar photovoltaic (PV energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

Identification of nonlinear dynamics in power plant components using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Fernandez, B.; Tsai, W.K.

1990-01-01

Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Science.gov (United States)

Hesthaven, J. S.; Ubbiali, S.

2018-06-01

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and employs artificial neural networks (ANNs), particularly multi-layer perceptrons (MLPs), to accurately approximate the coefficients of the reduced model. The search for the optimal number of neurons and the minimum amount of training samples to avoid overfitting is carried out in the offline phase through an automatic routine, relying upon a joint use of the Latin hypercube sampling (LHS) and the Levenberg-Marquardt (LM) training algorithm. This guarantees a complete offline-online decoupling, leading to an efficient RB method - referred to as POD-NN - suitable also for general nonlinear problems with a non-affine parametric dependence. Numerical studies are presented for the nonlinear Poisson equation and for driven cavity viscous flows, modeled through the steady incompressible Navier-Stokes equations. Both physical and geometrical parametrizations are considered. Several results confirm the accuracy of the POD-NN method and show the substantial speed-up enabled at the online stage as compared to a traditional RB strategy.

A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

Science.gov (United States)

Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

Linking structure and activity in nonlinear spiking networks.

Directory of Open Access Journals (Sweden)

Gabriel Koch Ocker

2017-06-01

Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Linking structure and activity in nonlinear spiking networks.

Science.gov (United States)

Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

2017-06-01

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Zero-sum two-player game theoretic formulation of affine nonlinear discrete-time systems using neural networks.

Science.gov (United States)

Mehraeen, Shahab; Dierks, Travis; Jagannathan, S; Crow, Mariesa L

2013-12-01

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.

Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

Science.gov (United States)

Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

2016-03-01

The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Study of a nonlinear system with shocks under broadband excitation. Application to a steam generator tube

International Nuclear Information System (INIS)

Thenint, Th.

2011-01-01

The steam generator is a heat exchanger and participates to the nuclear safety. Energy is transferred from the primary to the secondary fluid through many U-tubes maintained vertically by support plates. A sludge deposit tends to modify the boundary conditions and the secondary fluid flow. A fluid-elastic instability can then occur and lead to quick tube ruin. This thesis seeks a better understanding of the effect of contact nonlinearity on the dynamics of a tube in-air intermittently impacting the support plates and its consequences in regards with instability. The use of discretized contact conditions with circular obstacles distributed over the thickness of the plates and the use of enriched reduction bases allow quick and relevant nonlinear numerical simulations. These simulations are well correlated with experimental measurements and valid even with strong nonlinearity or negative modal damping. The evolution of power spectral densities (PSD) with growing excitation amplitude is analyzed: padding of the anti-resonances, peak shift and spread. It is then shown that an apparent stiffness associated with a permanent bilateral contact is pertinent to describe these transitions. In the case of an unstable linear system, one demonstrates that the nonlinearity keeps the responses bounded or stabilised, thus paving the way for future work with real or simulated fluid flows. (author)

Chronic Ankle Instability and Neural Excitability of the Lower Extremity.

Science.gov (United States)

McLeod, Michelle M; Gribble, Phillip A; Pietrosimone, Brian G

2015-08-01

Neuromuscular dysfunction of the leg and thigh musculature, including decreased strength and postural control, is common in patients with chronic ankle instability (CAI). Understanding how CAI affects specific neural pathways may provide valuable information for targeted therapies. To investigate differences in spinal reflexive and corticospinal excitability of the fibularis longus and vastus medialis between limbs in patients with unilateral CAI and between CAI patients and participants serving as healthy controls. Case-control study. Research laboratory. A total of 56 participants volunteered, and complete data for 21 CAI patients (9 men, 12 women; age = 20.81 ± 1.63 years, height = 171.57 ± 11.44 cm, mass = 68.84 ± 11.93 kg) and 24 healthy participants serving as controls (7 men, 17 women; age = 22.54 ± 2.92 years, height = 172.35 ± 10.85 cm, mass = 69.15 ± 12.30 kg) were included in the final analyses. Control participants were matched to CAI patients on sex, age, and limb dominance. We assigned "involved" limbs, which corresponded with the involved limbs of the CAI patients, to control participants. Spinal reflexive excitability was assessed via the Hoffmann reflex and normalized to a maximal muscle response. Corticospinal excitability was assessed using transcranial magnetic stimulation. Active motor threshold (AMT) was defined as the lowest transcranial magnetic stimulation intensity required to elicit motor-evoked potentials equal to or greater than 100 μV in 5 of 10 consecutive stimuli. We obtained motor-evoked potentials (MEPs) at percentages ranging from 100% to 140% of AMT. Fibularis longus MEP amplitudes were greater in control participants than in CAI patients bilaterally at 100% AMT (control involved limb: 0.023 ± 0.031; CAI involved limb: 0.014 ± 0.008; control uninvolved limb: 0.021 ± 0.022; CAI uninvolved limb: 0.015 ± 0.007; F1,41 = 4.551, P = .04) and 105% AMT (control involved limb: 0.029 ± 0.026; CAI involved limb: 0.021 ± 0

Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

Directory of Open Access Journals (Sweden)

Xin Zhang

2014-01-01

Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

Nonlinear quenching of current fluctuations in a self-exciting homopolar dynamo

Directory of Open Access Journals (Sweden)

R. Hide

1997-01-01

Full Text Available In the interpretation of geomagnetic polarity reversals with their highly variable frequency over geological time it is necessary, as with other irregularly fluctuating geophysical phenomena, to consider the relative importance of forced contributions associated with changing boundary conditions and of free contributions characteristic of the behaviour of nonlinear systems operating under fixed boundary conditions.  New evidence -albeit indirect- in favour of the likely predominance of forced contributions is provided by the discovery reported here of the possibility of complete quenching by nonlineax effects of current fluctuations in a self-exciting homopolar dynamo with its single Faraday disk driven into rotation with angular speed y(τ (where τ denotes time by a steady applied couple.  The armature of an electric motor connected in series with the coil of the dynamo is driven into rotation' with angular speed z(τ by a torque xf (x due to Lorentz forces associated with the electric current x(τ in the system (just as certain parts of the spectrum of eddies within the liquid outer core are generated largely by Lorentz forces associated with currents generated by the self-exciting magnetohydrodynamic (MHD geodynamo.   The discovery is based on bifurcation analysis supported by computational studies of the following (mathematically novel autonomous set of nonlinear ordinary differential equations: dx/dt = x(y - 1 - βzf(x, dy/dt = α(1 - x² - κy, dz/dt = xf (x -λz,          where f (x = 1 - ε + εσx, in cases when the dimensionless parameters (α, β, κ, λ, σ are all positive and 0 ≤ ε ≤ 1. Within those regions of (α, β, κ, λ, σ parameter space where the applied couple, as measured by α, is strong enough for persistent dynamo action (i.e. x ≠ 0 to occur at all, there are in general extensive regions where x(τ exhibits large amplitude regular or irregular (chaotic fluctuations.  But these fluctuating r

Pure odd-order oscillators with constant excitation

Science.gov (United States)

Cveticanin, L.

2011-02-01

In this paper the excited vibrations of a truly nonlinear oscillator are analyzed. The excitation is assumed to be constant and the nonlinearity is pure (without a linear term). The mathematical model is a second-order nonhomogeneous differential equation with strong nonlinear term. Using the first integral, the exact value of period of vibration i.e., angular frequency of oscillator described with a pure nonlinear differential equation with constant excitation is analytically obtained. The closed form solution has the form of gamma function. The period of vibration depends on the value of excitation and of the order and coefficient of the nonlinear term. For the case of pure odd-order-oscillators the approximate solution of differential equation is obtained in the form of trigonometric function. The solution is based on the exact value of period of vibration. For the case when additional small perturbation of the pure oscillator acts, the so called 'Cveticanin's averaging method' for a truly nonlinear oscillator is applied. Two special cases are considered: one, when the additional term is a function of distance, and the second, when damping acts. To prove the correctness of the method the obtained results are compared with those for the linear oscillator. Example of pure cubic oscillator with constant excitation and linear damping is widely discussed. Comparing the analytically obtained results with exact numerical ones it is concluded that they are in a good agreement. The investigations reported in the paper are of special interest for those who are dealing with the problem of vibration reduction in the oscillator with constant excitation and pure nonlinear restoring force the examples of which can be found in various scientific and engineering systems. For example, such mechanical systems are seats in vehicles, supports for machines, cutting machines with periodical motion of the cutting tools, presses, etc. The examples can be find in electronics

Excitation of lateral habenula neurons as a neural mechanism underlying ethanol-induced conditioned taste aversion.

Science.gov (United States)

Tandon, Shashank; Keefe, Kristen A; Taha, Sharif A

2017-02-15

The lateral habenula (LHb) has been implicated in regulation of drug-seeking behaviours through aversion-mediated learning. In this study, we recorded neuronal activity in the LHb of rats during an operant task before and after ethanol-induced conditioned taste aversion (CTA) to saccharin. Ethanol-induced CTA caused significantly higher baseline firing rates in LHb neurons, as well as elevated firing rates in response to cue presentation, lever press and saccharin taste. In a separate cohort of rats, we found that bilateral LHb lesions blocked ethanol-induced CTA. Our results strongly suggest that excitation of LHb neurons is required for ethanol-induced CTA, and point towards a mechanism through which LHb firing may regulate voluntary ethanol consumption. Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol's aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion-mediated learning, show accelerated escalation of voluntary ethanol consumption. To understand neural encoding in the LHb contributing to ethanol-induced aversion, we recorded neural firing in the LHb of freely behaving, water-deprived rats before and after an ethanol-induced (1.5 g kg -1 20% ethanol, i.p.) conditioned taste aversion (CTA) to saccharin taste. Ethanol-induced CTA strongly decreased motivation for saccharin in an operant task to obtain the tastant. Comparison of LHb neural firing before and after CTA induction revealed four main differences in firing properties. First, baseline firing after CTA induction was significantly higher. Second, firing evoked by cues signalling saccharin availability shifted from a pattern of primarily inhibition before CTA to primarily excitation after CTA induction. Third, CTA induction reduced

Excitation of lateral habenula neurons as a neural mechanism underlying ethanol‐induced conditioned taste aversion

Science.gov (United States)

Keefe, Kristen A.; Taha, Sharif A.

2016-01-01

Key points The lateral habenula (LHb) has been implicated in regulation of drug‐seeking behaviours through aversion‐mediated learning.In this study, we recorded neuronal activity in the LHb of rats during an operant task before and after ethanol‐induced conditioned taste aversion (CTA) to saccharin.Ethanol‐induced CTA caused significantly higher baseline firing rates in LHb neurons, as well as elevated firing rates in response to cue presentation, lever press and saccharin taste.In a separate cohort of rats, we found that bilateral LHb lesions blocked ethanol‐induced CTA.Our results strongly suggest that excitation of LHb neurons is required for ethanol‐induced CTA, and point towards a mechanism through which LHb firing may regulate voluntary ethanol consumption. Abstract Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol's aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion‐mediated learning, show accelerated escalation of voluntary ethanol consumption. To understand neural encoding in the LHb contributing to ethanol‐induced aversion, we recorded neural firing in the LHb of freely behaving, water‐deprived rats before and after an ethanol‐induced (1.5 g kg−1 20% ethanol, i.p.) conditioned taste aversion (CTA) to saccharin taste. Ethanol‐induced CTA strongly decreased motivation for saccharin in an operant task to obtain the tastant. Comparison of LHb neural firing before and after CTA induction revealed four main differences in firing properties. First, baseline firing after CTA induction was significantly higher. Second, firing evoked by cues signalling saccharin availability shifted from a pattern of primarily inhibition before CTA to primarily excitation after CTA

A study of the cavity polariton under strong excitation:dynamics and nonlinearities in II-VI micro-cavities

International Nuclear Information System (INIS)

Muller, Markus

2000-01-01

This work contains an experimental study of the photoluminescence dynamics of cavity polaritons in strong coupling micro-cavities based on II-VI semiconductor compounds. The small exciton size and the strong exciton binding energy in these materials allowed us to study the strong coupling regime between photon and exciton up to high excitation densities, exploring the linear and non-linear emission regimes. Our main experimental techniques are picosecond time-resolved and angular photoluminescence spectroscopy. In the linear regime and for a negative photon-exciton detuning, we observe a suppression of the polariton relaxation by the emission of acoustic phonons leading to a non-equilibrium polariton distribution on the lower branch. This 'bottleneck' effect, which has already been described for polaritons in bulk semiconductors, results from the pronounced photon like character of the polaritons near k(parallel) = 0 in this configuration. At high excitation densities, non-linear relaxation processes, namely final state stimulation of the relaxation and polariton-polariton scattering, bypass this bottleneck giving rise to a very rapid relaxation down to the bottom of the band. We show that this dramatic change in the relaxation dynamics is finally responsible of the super-linear increase of the polariton emission from these states. (author) [fr

Suppression of chaos by weak resonant excitations in a non-linear oscillator with a non-symmetric potential

International Nuclear Information System (INIS)

Litak, Grzegorz; Syta, Arkadiusz; Borowiec, Marek

2007-01-01

We examine the Melnikov criterion for transition to chaos in case of one degree of freedom non-linear oscillator with non-symmetric potential. This system, when subjected to an external periodic force, shows homoclinic transition from regular vibrations to chaos just before escape from a potential well. We focus especially on the effect of a second resonant excitation with a different phase on the system transition to chaos. We propose a way of its control

Parametric excitation of nonlinear longitudinal oscillations in a magnetoactive plasma

International Nuclear Information System (INIS)

Demchenko, V.V.

1977-01-01

Parametric excitation by HF field of nonlinear longitudinal electron oscillations in the region of hybrid resonances of a cold nonrelativistic plasma has been investigated. It is shown that the inhomogeneity of a pumping field and that of the equilibrium plasma density result in the parametric instability. Expressions are derived for the increments of instable oscillations and the widths of the instability regions are determined. The increments of instable oscillations in the order of magnitude due to the inhomogeneities of the pumping field (γsub(E)) or of the plasma density (γsub(N)) are egual to γsub(E) approximately k(zetasub(0)) ωsub(pe), γsub(N) approximately (zetasub(0))/Lωsub(pe), where (zetasub(0))=(e)Esub(0)/msub(e)ωsub(0)sup(2) is the amplitude of displacement of an electron from the equilibrium state, k, ω 0 , E 0 are the wave number, frequency and amplitude of the pumping field, L is the characteristic size of the inhomogeneity of the plasma density, ωsub(pe) is the electron plasma frequency

Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

Science.gov (United States)

Si, Wenjie; Dong, Xunde; Yang, Feifei

2018-03-01

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Numeral eddy current sensor modelling based on genetic neural network

International Nuclear Information System (INIS)

Yu Along

2008-01-01

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

Nonlinear eigen-mode structures in complex astroclouds

Science.gov (United States)

Karmakar, P. K.; Haloi, A.

2017-05-01

The evolutionary dynamics of strongly nonlinear waves (of arbitrary amplitude) in an inhomogeneous complex astrophysical viscous cloud is investigated without recourse to any kind of swindle. It consists of warm lighter electrons and ions (Boltzmanian); and cold massive bi-polar dust grains (inertial fluids) alongside vigorous neutral dynamics in quasi-neural hydrodynamic equilibrium. Application of the Sagdeev pseudo-potential method transforms the analytic model into a conjugated pair of intermixed non-integrable energy integral laws. A natural excitation of electrostatic quasi-monotonic compressive dispersive shock-like eigen-modes is numerically demonstrated. In contrast, the self-gravitational waves grow purely as non-monotonic compressive oscillatory shock-like structures. The unique features of both the distinct classes are depicted. Their non-trivial significance in the astro-context is emphasized.

Nonlinear optical response of some Graphene oxide and Graphene fluoride derivatives

OpenAIRE

Liaros Nikolaos; Orfanos Ioannis; Papadakis Ioannis; Couris Stelios

2016-01-01

The nonlinear optical properties of two graphene derivatives, graphene oxide and graphene fluoride, are investigated by means of the Z-scan technique employing 35 ps and 4 ns, visible (532 nm) laser excitation. Both derivatives were found to exhibit significant third-order nonlinear optical response at both excitation regimes, with the nonlinear absorption being relatively stronger and concealing the presence of nonlinear refraction under ns excitation, while ps excita...

Coupled nonlinear oscillators

Energy Technology Data Exchange (ETDEWEB)

Chandra, J; Scott, A C

1983-01-01

Topics discussed include transitions in weakly coupled nonlinear oscillators, singularly perturbed delay-differential equations, and chaos in simple laser systems. Papers are presented on truncated Navier-Stokes equations in a two-dimensional torus, on frequency locking in Josephson point contacts, and on soliton excitations in Josephson tunnel junctions. Attention is also given to the nonlinear coupling of radiation pulses to absorbing anharmonic molecular media, to aspects of interrupted coarse-graining in stimulated excitation, and to a statistical analysis of long-term dynamic irregularity in an exactly soluble quantum mechanical model.

Study of nonlinear optical absorption properties of V{sub 2}O{sub 5} nanoparticles in the femtosecond excitation regime

Energy Technology Data Exchange (ETDEWEB)

Molli, Muralikrishna; Bhat Kademane, Abhijit; Pradhan, Prabin; Sai Muthukumar, V. [Sri Sathya Sai Institute of Higher Learning, Department of Physics, Puttaparthi, Andhra Pradesh (India)

2016-08-15

In this work, we report for the first time, the nonlinear optical absorption properties of vanadium pentoxide (V{sub 2}O{sub 5}) nanoparticles in the femtosecond excitation regime. V{sub 2}O{sub 5} nanoparticles were synthesized through solution combustion technique. The as-synthesized samples were further characterized using XRD, FESEM, EDAX, TEM and UV-visible spectroscopy. X-ray diffraction results revealed the crystalline nature of the nanoparticles. Electron microscopy studies showed the size of the nanoparticles to be ∝200 nm. Open-aperture z-scan technique was employed to study the nonlinear optical absorption behavior of the synthesized samples using a 100-fs laser pulses at 800 nm from a regeneratively amplified Ti: sapphire laser. The mechanism of nonlinear absorption was found to be a three-photon absorption process which was explained using the density of states of V{sub 2}O{sub 5} obtained using density functional theory. These nanoparticles exhibit strong intensity-dependent nonlinear optical absorption and hence could be considered for optical-power-limiting applications. (orig.)

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

Science.gov (United States)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Finite element solution of nonlinear eddy current problems with periodic excitation and its industrial applications.

Science.gov (United States)

Bíró, Oszkár; Koczka, Gergely; Preis, Kurt

2014-05-01

An efficient finite element method to take account of the nonlinearity of the magnetic materials when analyzing three-dimensional eddy current problems is presented in this paper. The problem is formulated in terms of vector and scalar potentials approximated by edge and node based finite element basis functions. The application of Galerkin techniques leads to a large, nonlinear system of ordinary differential equations in the time domain. The excitations are assumed to be time-periodic and the steady-state periodic solution is of interest only. This is represented either in the frequency domain as a finite Fourier series or in the time domain as a set of discrete time values within one period for each finite element degree of freedom. The former approach is the (continuous) harmonic balance method and, in the latter one, discrete Fourier transformation will be shown to lead to a discrete harmonic balance method. Due to the nonlinearity, all harmonics, both continuous and discrete, are coupled to each other. The harmonics would be decoupled if the problem were linear, therefore, a special nonlinear iteration technique, the fixed-point method is used to linearize the equations by selecting a time-independent permeability distribution, the so-called fixed-point permeability in each nonlinear iteration step. This leads to uncoupled harmonics within these steps. As industrial applications, analyses of large power transformers are presented. The first example is the computation of the electromagnetic field of a single-phase transformer in the time domain with the results compared to those obtained by traditional time-stepping techniques. In the second application, an advanced model of the same transformer is analyzed in the frequency domain by the harmonic balance method with the effect of the presence of higher harmonics on the losses investigated. Finally a third example tackles the case of direct current (DC) bias in the coils of a single-phase transformer.

TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

DEFF Research Database (Denmark)

Yao, Wei; Fang, Jiakun; Zhao, Ping

2013-01-01

the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power...... system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency......In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have...

Solitonic dynamics and excitations of the nonlinear Schrödinger equation with third-order dispersion in non-Hermitian PT-symmetric potentials.

Science.gov (United States)

Chen, Yong; Yan, Zhenya

2016-03-22

Solitons are of the important significant in many fields of nonlinear science such as nonlinear optics, Bose-Einstein condensates, plamas physics, biology, fluid mechanics, and etc. The stable solitons have been captured not only theoretically and experimentally in both linear and nonlinear Schrödinger (NLS) equations in the presence of non-Hermitian potentials since the concept of the parity-time -symmetry was introduced in 1998. In this paper, we present novel bright solitons of the NLS equation with third-order dispersion in some complex -symmetric potentials (e.g., physically relevant -symmetric Scarff-II-like and harmonic-Gaussian potentials). We find stable nonlinear modes even if the respective linear -symmetric phases are broken. Moreover, we also use the adiabatic changes of the control parameters to excite the initial modes related to exact solitons to reach stable nonlinear modes. The elastic interactions of two solitons are exhibited in the third-order NLS equation with -symmetric potentials. Our results predict the dynamical phenomena of soliton equations in the presence of third-order dispersion and -symmetric potentials arising in nonlinear fiber optics and other physically relevant fields.

Theta phase precession and phase selectivity: a cognitive device description of neural coding

Science.gov (United States)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to

Effects due to induced azimuthal eddy currents in a self-exciting Faraday disk homopolar dynamo with a nonlinear series motor. I.. Two special cases

Science.gov (United States)

Hide, Raymond; Moroz, Irene M.

1999-10-01

The elucidation of the behaviour of physically realistic self-exciting Faraday-disk dynamos bears inter alia on attempts by theoretical geophysicists to interpret observations of geomagnetic polarity reversals. Hide [The nonlinear differential equations governing a hierarchy of self-exciting coupled Faraday-disk homopolar dynamos, Phys. Earth Planet. Interiors 103 (1997) 281-291; Nonlinear quenching of current fluctuations in a self-exciting homopolar dynamo, Nonlinear Processes in Geophysics 4 (1998) 201-205] has introduced a novel 4-mode set of nonlinear ordinary differential equations to describe such a dynamo in which a nonlinear electric motor is connected in series with the coil. The applied couple, α, driving the disk is steady and the Lorentz couple driving the motor is a quadratic function, x(1-ɛ)+ɛσx 2, of the dynamo-generated current x, with 0≤ɛ≤1. When there are no additional biasing effects due to background magnetic fields etc., the behaviour of the dynamo is determined by eight independent non-negative control parameters. These include ρ, proportional to the resistance of the disk to azimuthal eddy currents, and β, an inverse measure of the moment of inertia of the armature of the motor. When β=0 (the case when the motor is absent and ɛ and σ are redundant) and ρ -1≠0 , the 4-mode dynamo equations reduce to the 3-mode Lorenz equations, which can behave chaotically [E. Knobloch, Chaos in the segmented disc dynamo, Phys. Lett. A 82 (1981) 439-440]. When β≠0 but ρ -1=0 , the 4-mode set of equations reduces to a 3-mode dynamo [R. Hide (1997), see above], which can also behave chaotically when ɛ=0 [R. Hide, A.C. Skeldon, D.J. Acheson, A study of two novel self-exciting single-disk homopolar dynamos: theory, Proc. R. Soc. Lond. A 452 (1996) 1369-1395] but not when ɛ=1 [R. Hide (1998), see above]. In the latter case, however, all persistent fluctuations are completely quenched [R. Hide (1998), see above]. In this paper we investigate

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems.

Science.gov (United States)

Han, Seong-Ik; Lee, Jang-Myung

2014-01-01

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Nonlinear eigen-mode structures in complex astroclouds

International Nuclear Information System (INIS)

Karmakar, P K; Haloi, A

2017-01-01

The evolutionary dynamics of strongly nonlinear waves (of arbitrary amplitude) in an inhomogeneous complex astrophysical viscous cloud is investigated without recourse to any kind of swindle. It consists of warm lighter electrons and ions (Boltzmanian); and cold massive bi-polar dust grains (inertial fluids) alongside vigorous neutral dynamics in quasi-neural hydrodynamic equilibrium. Application of the Sagdeev pseudo-potential method transforms the analytic model into a conjugated pair of intermixed non-integrable energy integral laws. A natural excitation of electrostatic quasi-monotonic compressive dispersive shock-like eigen-modes is numerically demonstrated. In contrast, the self-gravitational waves grow purely as non-monotonic compressive oscillatory shock-like structures. The unique features of both the distinct classes are depicted. Their non-trivial significance in the astro-context is emphasized. (paper)

Analysis of orbitally excited B-mesons

CERN Document Server

Albrecht, Zoltan; Quast, Gunter

2003-01-01

This thesis reports on the study of orbitally excited B** mesons in DELPHI b-events taken in the years 1994 to 2000 with the DELPHI detector at the LEP collider. The analyses presented represent the result of applying much improved and extended techniques of spectroscopy since the first DELPHI publication in 1995. A major improvement has occurred in the area of particle identification, where a neural network approach has been implemented in the DELPHI software package. Developments in the area of neural networks have led to much improved enrichment of the excited B states. The Bˆ{**} neural networks identify, on a track-by-track basis, the decay pion/kaon originating from the Bˆ{**} decay, suppressing background and keeping signal events in an efficient way. To improve detector resolution, a further application of neural networks has been applied to reconstruct the underlying Q-value. The corresponding network gives a correction on existing measurements of the Q-value in the form of a probability density fu...

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

Science.gov (United States)

Hua, Changchun; Zhang, Liuliu; Guan, Xinping

2017-01-01

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

Directory of Open Access Journals (Sweden)

Metin Demirtas

2011-07-01

Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

Science.gov (United States)

Yu, Bing; Shu, Wenjun; Cao, Can

2018-05-01

A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine's dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

Nonlinear graphene plasmonics

Science.gov (United States)

Ooi, Kelvin J. A.; Tan, Dawn T. H.

2017-10-01

The rapid development of graphene has opened up exciting new fields in graphene plasmonics and nonlinear optics. Graphene's unique two-dimensional band structure provides extraordinary linear and nonlinear optical properties, which have led to extreme optical confinement in graphene plasmonics and ultrahigh nonlinear optical coefficients, respectively. The synergy between graphene's linear and nonlinear optical properties gave rise to nonlinear graphene plasmonics, which greatly augments graphene-based nonlinear device performance beyond a billion-fold. This nascent field of research will eventually find far-reaching revolutionary technological applications that require device miniaturization, low power consumption and a broad range of operating wavelengths approaching the far-infrared, such as optical computing, medical instrumentation and security applications.

NNSYSID and NNCTRL Tools for system identification and control with neural networks

DEFF Research Database (Denmark)

Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

2001-01-01

choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

NNSYSID and NNCTRL Tools for system identification and control with neural networks

DEFF Research Database (Denmark)

Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

2001-01-01

a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

Quantum Nonlinear Optics

CERN Document Server

Hanamura, Eiichi; Yamanaka, Akio

2007-01-01

This graduate-level textbook gives an introductory overview of the fundamentals of quantum nonlinear optics. Based on the quantum theory of radiation, Quantum Nonlinear Optics incorporates the exciting developments in novel nonlinear responses of materials (plus laser oscillation and superradiance) developed over the past decade. It deals with the organization of radiation field, interaction between electronic system and radiation field, statistics of light, mutual manipulation of light and matter, laser oscillation, dynamics of light, nonlinear optical response, and nonlinear spectroscopy, as well as ultrashort and ultrastrong laser pulse. Also considered are Q-switching, mode locking and pulse compression. Experimental and theoretical aspects are intertwined throughout.

Backreaction of excitations on a vortex

OpenAIRE

Arodz, Henryk; Hadasz, Leszek

1996-01-01

Excitations of a vortex are usually considered in a linear approximation neglecting their backreaction on the vortex. In the present paper we investigate backreaction of Proca type excitations on a straightlinear vortex in the Abelian Higgs model. We propose exact Ansatz for fields of the excited vortex. From initial set of six nonlinear field equations we obtain (in a limit of weak excitations) two linear wave equations for the backreaction corrections. Their approximate solutions are found ...

Design of a nonlinear torsional vibration absorber

Science.gov (United States)

Tahir, Ammaar Bin

Tuned mass dampers (TMD) utilizing linear spring mechanisms to mitigate destructive vibrations are commonly used in practice. A TMD is usually tuned for a specific resonant frequency or an operating frequency of a system. Recently, nonlinear vibration absorbers attracted attention of researchers due to some potential advantages they possess over the TMDs. The nonlinear vibration absorber, or the nonlinear energy sink (NES), has an advantage of being effective over a broad range of excitation frequencies, which makes it more suitable for systems with several resonant frequencies, or for a system with varying excitation frequency. Vibration dissipation mechanism in an NES is passive and ensures that there is no energy backflow to the primary system. In this study, an experimental setup of a rotational system has been designed for validation of the concept of nonlinear torsional vibration absorber with geometrically induced cubic stiffness nonlinearity. Dimensions of the primary system have been optimized so as to get the first natural frequency of the system to be fairly low. This was done in order to excite the dynamic system for torsional vibration response by the available motor. Experiments have been performed to obtain the modal parameters of the system. Based on the obtained modal parameters, the design optimization of the nonlinear torsional vibration absorber was carried out using an equivalent 2-DOF modal model. The optimality criterion was chosen to be maximization of energy dissipation in the nonlinear absorber attached to the equivalent 2-DOF system. The optimized design parameters of the nonlinear absorber were tested on the original 5-DOF system numerically. A comparison was made between the performance of linear and nonlinear absorbers using the numerical models. The comparison showed the superiority of the nonlinear absorber over its linear counterpart for the given set of primary system parameters as the vibration energy dissipation in the former is

Nonlinear Excitations in Strongly-Coupled Fermi-Dirac Plasmas

OpenAIRE

Akbari-Moghanjoughi, M.

2012-01-01

In this paper we use the conventional quantum hydrodynamics (QHD) model in combination with the Sagdeev pseudopotential method to explore the effects of Thomas-Fermi nonuniform electron distribution, Coulomb interactions, electron exchange and ion correlation on the large-amplitude nonlinear soliton dynamics in Fermi-Dirac plasmas. It is found that in the presence of strong interactions significant differences in nonlinear wave dynamics of Fermi-Dirac plasmas in the two distinct regimes of no...

Terahertz Solitons in Biomolecular Systems and their Excitation by External Electromagnetic Field

Directory of Open Access Journals (Sweden)

Bugay А.N.

2015-01-01

Full Text Available Nonlinear dynamics of charge and acoustic excitations in cellular microtubules is considered. Different types of nonlinear solitary waves were studied taking account for dissipation. The mechanism of electro-acoustic pulse excitation by external electromagnetic field of terahertz frequency is recognized.

Non-linear auto-regressive models for cross-frequency coupling in neural time series

Science.gov (United States)

Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

2017-01-01

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

Neural control of magnetic suspension systems

Science.gov (United States)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

Directory of Open Access Journals (Sweden)

J. Humberto Pérez-Cruz

2014-01-01

Full Text Available The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

A Neural-Network-Based Nonlinear Adaptive State-Observer for Pressurized Water Reactors

Directory of Open Access Journals (Sweden)

Zhe Dong

2013-10-01

Full Text Available Although there have been some severe nuclear accidents such as Three Mile Island (USA, Chernobyl (Ukraine and Fukushima (Japan, nuclear fission energy is still a source of clean energy that can substitute for fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR is the most widely used nuclear fission reactor, its safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs. Compared with the classical power-level controllers, the advanced power-level regulators could strengthen both the closed-loop stability and control performance by feeding back the internal state-variables. However, not all of the internal state variables of a PWR can be obtained directly by measurements. To implement advanced PWR power-level control law, it is necessary to develop a state-observer to reconstruct the unmeasurable state-variables. Since a PWR is naturally a complex nonlinear system with parameters varying with power-level, fuel burnup, xenon isotope production, control rod worth and etc., it is meaningful to design a nonlinear observer for the PWR with adaptability to system uncertainties. Due to this and the strong learning capability of the multi-layer perceptron (MLP neural network, an MLP-based nonlinear adaptive observer is given for PWRs. Based upon Lyapunov stability theory, it is proved theoretically that this newly-built observer can provide bounded and convergent state-observation. This observer is then applied to the state-observation of a special PWR, i.e., the nuclear heating reactor (NHR, and numerical simulation results not only verify its feasibility but also give the relationship between the observation performance and observer parameters.

Nonlinear dynamical system approaches towards neural prosthesis

International Nuclear Information System (INIS)

Torikai, Hiroyuki; Hashimoto, Sho

2011-01-01

An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

Stability of a neural predictive controller scheme on a neural model

DEFF Research Database (Denmark)

Luther, Jim Benjamin; Sørensen, Paul Haase

2009-01-01

In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

Energy transfer in coupled nonlinear phononic waveguides: transition from wandering breather to nonlinear self-trapping

International Nuclear Information System (INIS)

Kosevich, Y A; Manevitch, L I; Savin, A V

2007-01-01

We consider, both analytically and numerically, the dynamics of stationary and slowly-moving breathers (localized short-wavelength excitations) in two weakly coupled nonlinear oscillator chains (nonlinear phononic waveguides). We show that there are two qualitatively different dynamical regimes of the coupled breathers: the oscillatory exchange of the low-amplitude breather between the phononic waveguides (wandering breather), and one-waveguide-localization (nonlinear self-trapping) of the high-amplitude breather. We also show that phase-coherent dynamics of the coupled breathers in two weakly linked nonlinear phononic waveguides has a profound analogy, and is described by a similar pair of equations, to the tunnelling quantum dynamics of two weakly linked Bose-Einstein condensates in a symmetric double-well potential (single bosonic Josephson junction). The exchange of phonon energy and excitations between the coupled phononic waveguides takes on the role which the exchange of atoms via quantum tunnelling plays in the case of the coupled condensates. On the basis of this analogy, we predict a new tunnelling mode of the coupled Bose-Einstein condensates in a single bosonic Josephson junction in which their relative phase oscillates around π/2. The dynamics of relative phase of two weakly linked Bose-Einstein condensates can be studied by means of interference, while the dynamics of the exchange of lattice excitations in coupled nonlinear phononic waveguides can be observed by means of light scattering

Modal Techniques for Remote Identification of Nonlinear Reactions at Gap-Supported Tubes Under Turbulent Excitation

International Nuclear Information System (INIS)

Delaune, X.; Piteau, Ph.; Borsoi, L.; Antunes, J.; Debut, V.

2010-01-01

Predictive computation of the nonlinear dynamical responses of gap-supported tubes subjected to flow excitation has been the subject of very active research. Nevertheless, experimental results are still very important, for validation of the theoretical predictions as well as for asserting the integrity of field components. Because carefully instrumented test tubes and tube-supports are seldom possible, due to space limitations and to the severe environment conditions, there is a need for robust techniques capable of extracting, from the actual vibratory response data, information that is relevant for asserting the components integrity. The dynamical contact/impact (vibro-impact) forces are of paramount significance, as are the tube/support gaps. Following our previous studies in this area using wave-propagation techniques (De Araujo, Antunes, and Piteau, 1998, 'Remote Identification of Impact Forces on Loosely Supported Tubes: Part 1-Basic Theory and Experiments', J. Sound Vib., 215, pp. 1015-1041; Antunes, Paulino, and Piteau, 1998, 'Remote Identification of Impact Forces on Loosely Supported Tubes: Part 2-Complex Vibro-Impact Motions', J. Sound Vib., 215, pp. 1043-1064; Paulino, Antunes, and Izquierdo, 1999, 'Remote Identification of Impact Forces on Loosely Supported Tubes: Analysis of Multi-Supported Systems', ASME J. Pressure Vessel Technol., 121, pp. 61-70), we apply modal methods in the present paper for extracting such information. The dynamical support forces, as well as the vibratory responses at the support locations, are identified from one or several vibratory response measurements at remote transducers, from which the support gaps can be inferred. As for most inverse problems, the identification results may prove quite sensitive to noise and modeling errors. Therefore, topics discussed in the paper include regularization techniques to mitigate the effects of non-measured noise perturbations. In particular, a method is proposed to improve the

Nonlinear excitations and charge transport in lithium niobate crystals investigated using femtosecond-light gratings; Nichtlineare Anregungen und Ladungstransport in Lithiumniobatkristallen untersucht mit Femtosekunden-Lichtgittern

Energy Technology Data Exchange (ETDEWEB)

Maxein, Karl Dominik

2009-12-15

Lithium niobate (LiNbO{sub 3}) is a widely employed material in nonlinear optics and photonics. Its usage is hampered by the photorefractive effect, which can destroy beam profiles and phase matching conditions. Existing methods to suppress photorefraction fail for the interesting regime of very high intensities and short pulses. Therefore, the photorefractive effect is investigated using femtosecond laser pulses: By utilizing so-called 2K holography, the occupation of energetically shallow traps is observed to occur in less than 100 fs after a two-photon excitation. Writing of photorefractive gratings into oxidized iron-doped LiNbO{sub 3} is much faster with pulses than with cw light. This is explained by the sensitization of the crystal due to charge trapping in photorefractive centers after nonlinear excitations. Finally, light-induced scattering of pulse light is suppressed compared to the scattering of cw light due to the small coherence length of pulses. (orig.)

Nonlinear Dynamics of a Diffusing Interface

Science.gov (United States)

Duval, Walter M. B.

2001-01-01

Excitation of two miscible-viscous liquids inside a bounded enclosure in a microgravity environment has shown the evolution of quasi-stationary waves of various modes for a range of parameters. We examine computationally the nonlinear dynamics of the system as the interface breakup and bifurcates to resonance structures typified by the Rayleigh-Taylor instability mechanism. Results show that when the mean steady field is much smaller than the amplitude of the sinusoidal excitation, the system behaves linearly, and growth of quasi-stationary waves occurs through the Kelvin-Helmholtz instability mechanism. However, as the amplitude of excitation increases, nonlinearity occurs through subharmonic bifurcation prior to broadband chaos.

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.

The nonlinear differential equations governing a hierarchy of self-exciting coupled Faraday-disk homopolar dynamos

Science.gov (United States)

Hide, Raymond

1997-02-01

This paper discusses the derivation of the autonomous sets of dimensionless nonlinear ordinary differential equations (ODE's) that govern the behaviour of a hierarchy of related electro-mechanical self-exciting Faraday-disk homopolar dynamo systems driven by steady mechanical couples. Each system comprises N interacting units which could be arranged in a ring or lattice. Within each unit and connected in parallel or in series with the coil are electric motors driven into motion by the dynamo, all having linear characteristics, so that nonlinearity arises entirely through the coupling between components. By introducing simple extra terms into the equations it is possible to represent biasing effects arising from impressed electromotive forces due to thermoelectric or chemical processes and from the presence of ambient magnetic fields. Dissipation in the system is due not only to ohmic heating but also to mechanical friction in the disk and the motors, with the latter agency, no matter how weak, playing an unexpectedly crucial rôle in the production of régimes of chaotic behaviour. This has already been demonstrated in recent work on a case of a single unit incorporating just one series motor, which is governed by a novel autonomous set of nonlinear ODE's with three time-dependent variables and four control parameters. It will be of mathematical as well as geophysical and astrophysical interest to investigate systematically phase and amplitude locking and other types of behaviour in the more complicated cases that arise when N > 1, which can typically involve up to 6 N dependent variables and 19 N-5 control parameters. Even the simplest members of the hierarchy, with N as low as 1, 2 or 3, could prove useful as physically-realistic low-dimensional models in theoretical studies of fluctuating stellar and planetary magnetic fields. Geomagnetic polarity reversals could be affected by the presence of the Earth's solid metallic inner core, driven like an electric motor

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

Directory of Open Access Journals (Sweden)

J. Humberto Pérez-Cruz

2012-01-01

Full Text Available This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term.

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

Stability of a nonlinear second order equation under parametric bounded noise excitation

International Nuclear Information System (INIS)

Wiebe, Richard; Xie, Wei-Chau

2016-01-01

The motivation for the following work is a structural column under dynamic axial loads with both deterministic (harmonic transmitted forces from the surrounding structure) and random (wind and/or earthquake) loading components. The bounded noise used herein is a sinusoid with an argument composed of a random (Wiener) process deviation about a mean frequency. By this approach, a noise parameter may be used to investigate the behavior through the spectrum from simple harmonic forcing, to a bounded random process with very little harmonic content. The stability of both the trivial and non-trivial stationary solutions of an axially-loaded column (which is modeled as a second order nonlinear equation) under parametric bounded noise excitation is investigated by use of Lyapunov exponents. Specifically the effect of noise magnitude, amplitude of the forcing, and damping on stability of a column is investigated. First order averaging is employed to obtain analytical approximations of the Lyapunov exponents of the trivial solution. For the non-trivial stationary solution however, the Lyapunov exponents are obtained via Monte Carlo simulation as the stability equations become analytically intractable. (paper)

NONLINEAR TIDES IN CLOSE BINARY SYSTEMS

International Nuclear Information System (INIS)

Weinberg, Nevin N.; Arras, Phil; Quataert, Eliot; Burkart, Josh

2012-01-01

We study the excitation and damping of tides in close binary systems, accounting for the leading-order nonlinear corrections to linear tidal theory. These nonlinear corrections include two distinct physical effects: three-mode nonlinear interactions, i.e., the redistribution of energy among stellar modes of oscillation, and nonlinear excitation of stellar normal modes by the time-varying gravitational potential of the companion. This paper, the first in a series, presents the formalism for studying nonlinear tides and studies the nonlinear stability of the linear tidal flow. Although the formalism we present is applicable to binaries containing stars, planets, and/or compact objects, we focus on non-rotating solar-type stars with stellar or planetary companions. Our primary results include the following: (1) The linear tidal solution almost universally used in studies of binary evolution is unstable over much of the parameter space in which it is employed. More specifically, resonantly excited internal gravity waves in solar-type stars are nonlinearly unstable to parametric resonance for companion masses M' ∼> 10-100 M ⊕ at orbital periods P ≈ 1-10 days. The nearly static 'equilibrium' tidal distortion is, however, stable to parametric resonance except for solar binaries with P ∼ 3 [P/10 days] for a solar-type star) and drives them as a single coherent unit with growth rates that are a factor of ≈N faster than the standard three-wave parametric instability. These are local instabilities viewed through the lens of global analysis; the coherent global growth rate follows local rates in the regions where the shear is strongest. In solar-type stars, the dynamical tide is unstable to this collective version of the parametric instability for even sub-Jupiter companion masses with P ∼< a month. (4) Independent of the parametric instability, the dynamical and equilibrium tides excite a wide range of stellar p-modes and g-modes by nonlinear inhomogeneous forcing

Real-time neural network-based self-tuning control of a nonlinear electro-hydraulic servomotor

Energy Technology Data Exchange (ETDEWEB)

Canelon, J.I.; Ortega, A.G. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Electrical Engineering; Shieh, L.S. [Houston Univ., Houston, TX (United States). Dept. of Electrical and Computer Engineering; Bastidas, J.I. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Mechanical Engineering; Zhang, Y.; Akujuobi, C.M. [Prairie View A and M Univ., Prairie View, TX (United States). Center of Excellence for Communication Systems Technology Research and Dept. of Engineering Technology

2010-08-13

For high power applications, hydraulic actuators offer many advantages over electromagnetic actuators, including higher torque/mass ratios; smaller control gains; excellent torque capability; filtered high frequency noise; better heat transfer characteristics; smaller size; higher speed of response of the servomechanism; cheaper hardware; and higher reliability. Therefore, any application that requires a large force applied smoothly by an actuator is a candidate for hydraulic power. Examples of such applications include vehicle steering and braking systems; roll mills; drilling rigs; heavy duty crane and presses; and industrial robots and actuators for aircraft control surfaces such as ailerons and flaps. It is extremely important to create effective control strategies for hydraulic systems. This paper outlined the real-time implementation of a neural network-based approach, for self-tuning control of the angular position of a nonlinear electro-hydraulic servomotor. Using an online training algorithm, a neural network autoregressive moving-average model with exogenous input (ARMAX) model of the system was identified and continuously updated and an optimal linear ARMAX model was determined. The paper briefly depicted the neural network-based self-tuning control approach and a description of the experimental equipment (hardware and software) was presented including the implementation details. The experimental results were discussed and conclusions were summarized. It was found that the approach proved to be very effective in the control of this fast dynamics system, outperforming a fine tuned PI controller. Therefore, although the self-tuning approach was computationally demanding, it was feasible for real-time implementation. 22 refs., 6 figs.

Empirical modeling of nuclear power plants using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.; Chong, K.T.

1991-01-01

A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, the feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

Science.gov (United States)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Thermoelastic steam turbine rotor control based on neural network

Science.gov (United States)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

A constructive nonlinear array (CNA) method for barely visible impact detection in composite materials

Science.gov (United States)

Malfense Fierro, Gian Piero; Meo, Michele

2017-04-01

Currently there are numerous phased array techniques such as Full Matrix Capture (FMC) and Total Focusing Method (TFM) that provide good damage assessment for composite materials. Although, linear methods struggle to evaluate and assess low levels of damage, while nonlinear methods have shown great promise in early damage detection. A sweep and subtraction evaluation method coupled with a constructive nonlinear array method (CNA) is proposed in order to assess damage specific nonlinearities, address issues with frequency selection when using nonlinear ultrasound imaging techniques and reduce equipment generated nonlinearities. These methods were evaluated using multiple excitation locations on an impacted composite panel with a complex damage (barely visible impact damage). According to various recent works, damage excitation can be accentuated by exciting at local defect resonance (LDR) frequencies; although these frequencies are not always easily determinable. The sweep methodology uses broadband excitation to determine both local defect and material resonances, by assessing local defect generated nonlinearities using a laser vibrometer it is possible to assess which frequencies excite the complex geometry of the crack. The dual effect of accurately determining local defect resonances, the use of an image subtraction method and the reduction of equipment based nonlinearities using CNA result in greater repeatability and clearer nonlinear imaging (NIM).

The observation of nonlinear ion cyclotron wave excitation during high-harmonic fast wave heating in the large helical device

International Nuclear Information System (INIS)

Kasahara, H.; Seki, T.; Kumazawa, R.; Saito, K.; Mutoh, T.; Kubo, S.; Shimozuma, T.; Igami, H.; Yoshimura, Y.; Takahashi, H.; Yamada, I.; Tokuzawa, T.; Ohdachi, S.; Morita, S.; Nomura, G.; Shimpo, F.; Komori, A.; Motojima, O.; Oosako, T.; Takase, Y.

2008-01-01

A wave detector, a newly designed magnetic probe, is installed in the large helical device (LHD). This wave detector is a 100-turn loop coil with electrostatic shield. Comparing a one-loop coil to this detector, this detector has roughly constant power coupling in the lower frequency range of 40 MHz, and it can easily detect magnetic wave in the frequency of a few megahertz. During high-harmonic fast wave heating, lower frequency waves (<10 MHz) were observed in the LHD for the first time, and for the power density threshold of lower frequency wave excitation (7.5 MHz) the power density of excited pumped wave (38.47 MHz) was approximately -46 dBm/Hz. These lower frequencies are kept constant for electron density and high energy particle distribution, and these lower frequency waves seem to be ion cyclotron waves caused by nonlinear wave-particle interaction, for example, parametric decay instability.

Sequential neural models with stochastic layers

DEFF Research Database (Denmark)

Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

2016-01-01

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

Comparison of the Gen Expression Programming, Nonlinear Time Series and Artificial Neural Network in Estimating the River Daily Flow (Case Study: The Karun River

Directory of Open Access Journals (Sweden)

R. Zamani

2015-06-01

Full Text Available Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series models and artificial intelligence (Artificial Neural Network and Gen Expression Programming, the daily flow modeling has been at the time interval (1981-2012 in the Armand hydrometric station on the Karun River. Armand station upstream basin is one of the most basins in the North Karun basin and includes four sub basins (Vanak, Middle Karun, Beheshtabad and Kohrang.The results of this study shown that artificial intelligence models have superior than nonlinear time series in flow daily simulation in the Karun River. As well as, modeling and comparison of artificial intelligence models showed that the Gen Expression Programming have evaluation criteria better than artificial neural network.

The use of multisine excitations to characterise damage in structures

Science.gov (United States)

Vanhoenacker, K.; Schoukens, J.; Guillaume, P.; Vanlanduit, S.

2004-01-01

In order to detect the presence of damage and imperfections in materials, a new and promising method for non-destructive material testing has been developed. The technique focuses on the non-linear distortions that are present in the results of a frequency response function (FRF) or transfer function measurement of the sample. The kernel idea in the described method is to use well-chosen periodic excitations where only some of the considered frequency components are excited. The non-excited frequency lines are used to detect, qualify (even or odd non-linear distortions) and quantify (What is the level of the non-linear distortions?) the non-linear distortions. Undamaged materials are often essentially linear in their response. However, the non-linear behaviour of the same material increases significantly when damage appears. The method is applied in the field of damage detection and health monitoring. The method is illustrated by experiments on uncracked and cracked artificial slate beams used in civil constructions and during mechanical cyclic fatigue loading. The developed technique demonstrated to be a very fast and efficient tool to assess global damage in a material.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

Science.gov (United States)

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Using function approximation to determine neural network accuracy

International Nuclear Information System (INIS)

Wichman, R.F.; Alexander, J.

2013-01-01

Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)

Simple neural substrate predicts complex rhythmic structure in duetting birds

Science.gov (United States)

Amador, Ana; Trevisan, M. A.; Mindlin, G. B.

2005-09-01

Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis.

Universality in an information-theoretic motivated nonlinear Schrodinger equation

International Nuclear Information System (INIS)

Parwani, R; Tabia, G

2007-01-01

Using perturbative methods, we analyse a nonlinear generalization of Schrodinger's equation that had previously been obtained through information-theoretic arguments. We obtain analytical expressions for the leading correction, in terms of the nonlinearity scale, to the energy eigenvalues of the linear Schrodinger equation in the presence of an external potential and observe some generic features. In one space dimension these are (i) for nodeless ground states, the energy shifts are subleading in the nonlinearity parameter compared to the shifts for the excited states; (ii) the shifts for the excited states are due predominantly to contribution from the nodes of the unperturbed wavefunctions, and (iii) the energy shifts for excited states are positive for small values of a regulating parameter and negative at large values, vanishing at a universal critical value that is not manifest in the equation. Some of these features hold true for higher dimensional problems. We also study two exactly solved nonlinear Schrodinger equations so as to contrast our observations. Finally, we comment on the possible significance of our results if the nonlinearity is physically realized

Intramolecular and nonlinear dynamics

Energy Technology Data Exchange (ETDEWEB)

Davis, M.J. [Argonne National Laboratory, IL (United States)

1993-12-01

Research in this program focuses on three interconnected areas. The first involves the study of intramolecular dynamics, particularly of highly excited systems. The second area involves the use of nonlinear dynamics as a tool for the study of molecular dynamics and complex kinetics. The third area is the study of the classical/quantum correspondence for highly excited systems, particularly systems exhibiting classical chaos.

Issues in the use of neural networks in information retrieval

CERN Document Server

Iatan, Iuliana F

2017-01-01

This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules. Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Cognitive-Neural Effects of Brush Writing of Chinese Characters: Cortical Excitation of Theta Rhythm

Directory of Open Access Journals (Sweden)

Min Xu

2013-01-01

Full Text Available Chinese calligraphy has been scientifically investigated within the contexts and principles of psychology, cognitive science, and the cognitive neuroscience. On the basis of vast amount of research in the last 30 years, we have developed a cybernetic theory of handwriting and calligraphy to account for the intricate interactions of several psychological dimensions involved in the dynamic act of graphic production. Central to this system of writing are the role of sensory, bio-, cognitive, and neurofeedback mechanisms for the initiation, guidance, and regulation of the writing motions vis-a-vis visual-geometric variations of Chinese characters. This experiment provided the first evidence of cortical excitation in EEG theta wave as a neural hub that integrates information coming from changes in the practitioner’s body, emotions, and cognition. In addition, it has also confirmed neurofeedback as an essential component of the cybernetic theory of handwriting and calligraphy.

Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays

Directory of Open Access Journals (Sweden)

Xuefei Wu

2012-01-01

Full Text Available The generalized projective synchronization (GPS between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.

Controlling nonlinear waves in excitable media

International Nuclear Information System (INIS)

Puebla, Hector; Martin, Roland; Alvarez-Ramirez, Jose; Aguilar-Lopez, Ricardo

2009-01-01

A new feedback control method is proposed to control the spatio-temporal dynamics in excitable media. Applying suitable external forcing to the system's slow variable, successful suppression and control of propagating pulses as well as spiral waves can be obtained. The proposed controller is composed by an observer to infer uncertain terms such as diffusive transport and kinetic rates, and an inverse-dynamics feedback function. Numerical simulations shown the effectiveness of the proposed feedback control approach.

Controlling nonlinear waves in excitable media

Energy Technology Data Exchange (ETDEWEB)

Puebla, Hector [Departamento de Energia, Universidad Autonoma Metropolitana, Av. San Pablo No. 180, Reynosa-Tamaulipas, Azcapotzalco 02200, DF, Mexico (Mexico)], E-mail: hpuebla@correo.azc.uam.mx; Martin, Roland [Laboratoire de Modelisation et d' Imagerie en Geosciences, CNRS UMR and INRIA Futurs Magique-3D, Universite de Pau (France); Alvarez-Ramirez, Jose [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana-Iztapalapa (Mexico); Aguilar-Lopez, Ricardo [Departamento de Biotecnologia y Bioingenieria, CINVESTAV-IPN (Mexico)

2009-01-30

A new feedback control method is proposed to control the spatio-temporal dynamics in excitable media. Applying suitable external forcing to the system's slow variable, successful suppression and control of propagating pulses as well as spiral waves can be obtained. The proposed controller is composed by an observer to infer uncertain terms such as diffusive transport and kinetic rates, and an inverse-dynamics feedback function. Numerical simulations shown the effectiveness of the proposed feedback control approach.

Application of neural networks in coastal engineering

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.

the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...

Nonlinear drift tearing mode

International Nuclear Information System (INIS)

Zelenyj, L.M.; Kuznetsova, M.M.

1989-01-01

Nonlinear study of magnetic perturbation development under single-mode conditions in collision-free plasma in configurations with the magnetic field shear is investigated. Results are obtained with regard of transverse component of electrical field and its effect on ion dynamics within wide range of ion Larmor radius value and values of magnetic field shear. Increments of nonlinear drift tearing mode are obtained and it is shown that excitation drastic conditions of even linearly stable modes are possible. Mechanism of instability nonlinear stabilization is considered and the value of magnetic island at the saturation threshold is estimeted. Energy of nonlinear drift tearing mode is discussed

A Solution Method for Linear and Geometrically Nonlinear MDOF Systems with Random Properties subject to Random Excitation

DEFF Research Database (Denmark)

Micaletti, R. C.; Cakmak, A. S.; Nielsen, Søren R. K.

structural properties. The resulting state-space formulation is a system of ordinary stochastic differential equations with random coefficient and deterministic initial conditions which are subsequently transformed into ordinary stochastic differential equations with deterministic coefficients and random......A method for computing the lower-order moments of randomly-excited multi-degree-of-freedom (MDOF) systems with random structural properties is proposed. The method is grounded in the techniques of stochastic calculus, utilizing a Markov diffusion process to model the structural system with random...... initial conditions. This transformation facilitates the derivation of differential equations which govern the evolution of the unconditional statistical moments of response. Primary consideration is given to linear systems and systems with odd polynomial nonlinearities, for in these cases...

Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

Changes in neural network homeostasis trigger neuropsychiatric symptoms.

Science.gov (United States)

Winkelmann, Aline; Maggio, Nicola; Eller, Joanna; Caliskan, Gürsel; Semtner, Marcus; Häussler, Ute; Jüttner, René; Dugladze, Tamar; Smolinsky, Birthe; Kowalczyk, Sarah; Chronowska, Ewa; Schwarz, Günter; Rathjen, Fritz G; Rechavi, Gideon; Haas, Carola A; Kulik, Akos; Gloveli, Tengis; Heinemann, Uwe; Meier, Jochen C

2014-02-01

The mechanisms that regulate the strength of synaptic transmission and intrinsic neuronal excitability are well characterized; however, the mechanisms that promote disease-causing neural network dysfunction are poorly defined. We generated mice with targeted neuron type-specific expression of a gain-of-function variant of the neurotransmitter receptor for glycine (GlyR) that is found in hippocampectomies from patients with temporal lobe epilepsy. In this mouse model, targeted expression of gain-of-function GlyR in terminals of glutamatergic cells or in parvalbumin-positive interneurons persistently altered neural network excitability. The increased network excitability associated with gain-of-function GlyR expression in glutamatergic neurons resulted in recurrent epileptiform discharge, which provoked cognitive dysfunction and memory deficits without affecting bidirectional synaptic plasticity. In contrast, decreased network excitability due to gain-of-function GlyR expression in parvalbumin-positive interneurons resulted in an anxiety phenotype, but did not affect cognitive performance or discriminative associative memory. Our animal model unveils neuron type-specific effects on cognition, formation of discriminative associative memory, and emotional behavior in vivo. Furthermore, our data identify a presynaptic disease-causing molecular mechanism that impairs homeostatic regulation of neural network excitability and triggers neuropsychiatric symptoms.

Nonlinear temporal modulation of pulsar radioemission

International Nuclear Information System (INIS)

Chian, A.C.-L.

1984-01-01

A nonlinear theory is discussed for self-modulation of pulsar radio pulses. A nonlinear Schroedinger equation is derived for strong electromagnetic waves propagating in an electron-positron plasma. The nonlinearities arising from wave intensity induced relativistic particle mass variation may excite the modulational instability of circularly and linearly polarized pulsar radiation. The resulting wave envelopes can take the form of periodic wave trains or solitons. These nonlinear stationary wave forms may account for the formation of pulsar microstructures. (Author) [pt

Structural System Identification with Extended Kalman Filter and Orthogonal Decomposition of Excitation

Directory of Open Access Journals (Sweden)

Y. Ding

2014-01-01

Full Text Available Both the structural parameter and external excitation have coupling influence on structural response. A new system identification method in time domain is proposed to simultaneously evaluate structural parameter and external excitation. The method can be used for linear and hysteresis nonlinear structural condition assessment based on incomplete structural responses. In this method, the structural excitation is decomposed by orthogonal approximation. With this approximation, the strongly time-variant excitation identification is transformed to gentle time-variant, even constant parameters identification. Then the extended Kalman filter is applied to simultaneously identify state vector including the structural parameters and excitation orthogonal parameters in state space based on incomplete measurements. The proposed method is validated numerically with the simulation of three-story linear and nonlinear structures subject to external force. The external force on the top floor and the structural parameters are simultaneously identified with the proposed system identification method. Results from both simulations indicate that the proposed method is capable of identifing the dynamic load and structural parameters fairly accurately with contaminated incomplete measurement for both of the linear and nonlinear structural systems.

Nonlinear Motion Tracking by Deep Learning Architecture

Science.gov (United States)

Verma, Arnav; Samaiya, Devesh; Gupta, Karunesh K.

2018-03-01

In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.

Active Control of Sound based on Diagonal Recurrent Neural Network

NARCIS (Netherlands)

Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

2002-01-01

Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The

Back reaction of excitations on a vortex

Science.gov (United States)

Arodź, Henryk; Hadasz, Leszek

1997-01-01

Excitations of a vortex are usually considered in a linear approximation neglecting their back reaction on the vortex. In the present paper we investigate back reaction of Proca-type excitations on a straight linear vortex in the Abelian Higgs model. We propose an exact ansatz for fields of the excited vortex. From an initial set of six nonlinear field equations we obtain (in a limit of weak excitations) two linear wave equations for the back reaction corrections. Their approximate solutions are found in the cases of plane wave and wave-packet-type excitations. We find that the excited vortex radiates the vector field and that the Higgs field has a very broad oscillating component.

Some remarks on coherent nonlinear coupling of waves in plasmas

International Nuclear Information System (INIS)

Wilhelmsson, H.

1976-01-01

The analysis of nonlinear processes in plasma physics has given rise to a basic set of coupled equations. These equations describe the coherent nonlinear evolution of plasma waves. In this paper various possibilities of analysing these equations are discussed and inherent difficulties in the description of nonlinear interactions between different types of waves are pointed out. Specific examples of stimulated excitation of waves are considered. These are the parametric excitation of hybrid resonances in hot magnetized multi-ion component plasma and laser-plasma interactions. (B.D.)

Adaptive Filtering Using Recurrent Neural Networks

Science.gov (United States)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Nonlinear Dynamics of Electrostatically Actuated MEMS Arches

KAUST Repository

Al Hennawi, Qais M.

2015-05-01

In this thesis, we present theoretical and experimental investigation into the nonlinear statics and dynamics of clamped-clamped in-plane MEMS arches when excited by an electrostatic force. Theoretically, we first solve the equation of motion using a multi- mode Galarkin Reduced Order Model (ROM). We investigate the static response of the arch experimentally where we show several jumps due to the snap-through instability. Experimentally, a case study of in-plane silicon micromachined arch is studied and its mechanical behavior is measured using optical techniques. We develop an algorithm to extract various parameters that are needed to model the arch, such as the induced axial force, the modulus of elasticity, and the initially induced initial rise. After that, we excite the arch by a DC electrostatic force superimposed to an AC harmonic load. A softening spring behavior is observed when the excitation is close to the first resonance frequency due to the quadratic nonlinearity coming from the arch geometry and the electrostatic force. Also, a hardening spring behavior is observed when the excitation is close to the third (second symmetric) resonance frequency due to the cubic nonlinearity coming from mid-plane stretching. Then, we excite the arch by an electric load of two AC frequency components, where we report a combination resonance of the summed type. Agreement is reported among the theoretical and experimental work.

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Directory of Open Access Journals (Sweden)

Shu-Min Lu

2017-01-01

Full Text Available An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

Research on Fault Diagnosis Method Based on Rule Base Neural Network

Directory of Open Access Journals (Sweden)

Zheng Ni

2017-01-01

Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

Non-linear seismic response of base-isolated liquid storage tanks to bi-directional excitation

International Nuclear Information System (INIS)

Shrimali, M.K.; Jangid, R.S.

2002-01-01

Seismic response of the liquid storage tanks isolated by lead-rubber bearings is investigated for bi-directional earthquake excitation (i.e. two horizontal components). The biaxial force-deformation behaviour of the bearings is considered as bi-linear modelled by coupled non-linear differential equations. The continuous liquid mass of the tank is modelled as lumped masses known as convective mass, impulsive mass and rigid mass. The corresponding stiffness associated with these lumped masses has been worked out depending upon the properties of the tank wall and liquid mass. Since the force-deformation behaviour of the bearings is non-linear, as a result, the seismic response is obtained by the Newmark's step-by-step method. The seismic responses of two types of the isolated tanks (i.e. slender and broad) are investigated under several recorded earthquake ground to study the effects of bi-directional interaction. Further, a parametric study is also carried out to study the effects of important system parameters on the effectiveness of seismic isolation for liquid storage tanks. The various important parameters considered are: (i) the period of isolation, (ii) the damping of isolation bearings and (iii) the yield strength level of the bearings. It has been observed that the seismic response of isolated tank is found to be insensitive to interaction effect of the bearing forces. Further, there exists an optimum value of isolation damping for which the base shear in the tank attains the minimum value. Therefore, increasing the bearing damping beyond a certain value may decrease the bearing and sloshing displacements but it may increase the base shear

Analysis of elastic nonlinearity for impact damage detection in composite laminates

International Nuclear Information System (INIS)

Frau, A; Porcu, M C; Aymerich, F; Pieczonka, L; Staszewski, W J

2015-01-01

This paper concerns the experimental analysis of nonlinear response features of a composite laminate plate for impact damage detection. The measurement procedure is based on the Scaling Subtraction Method (SSM) and consists in exciting the damaged specimen with two sinusoidal signals at different amplitude. The linearly rescaled response signal at low amplitude excitation is subtracted from the response at large amplitude excitation to extract the nonlinear signatures. The latter are analysed in the time domain to infer the presence of damage. Results are compared with frequency domain analyses using the nonlinear vibro-acoustic modulation technique (NWMS). Changes in amplitude and phase as well as modulation effects of the acquired responses are also monitored. Surface-bonded, low profile piezoceramic transducers are used for excitation and sensing. Both measurements techniques are applied to detect barely visible impact damage in laminate composite plate. Non-destructive penetrant-enhanced X-ray inspections are carried out to characterize the extent of internal damage. The behavior of the nonlinear features and the sensitivity of each technique are also investigated in the paper. (paper)

Searching for Cross-Diagnostic Convergence: Neural Mechanisms Governing Excitation and Inhibition Balance in Schizophrenia and Autism Spectrum Disorders.

Science.gov (United States)

Foss-Feig, Jennifer H; Adkinson, Brendan D; Ji, Jie Lisa; Yang, Genevieve; Srihari, Vinod H; McPartland, James C; Krystal, John H; Murray, John D; Anticevic, Alan

2017-05-15

Recent theoretical accounts have proposed excitation and inhibition (E/I) imbalance as a possible mechanistic, network-level hypothesis underlying neural and behavioral dysfunction across neurodevelopmental disorders, particularly autism spectrum disorder (ASD) and schizophrenia (SCZ). These two disorders share some overlap in their clinical presentation as well as convergence in their underlying genes and neurobiology. However, there are also clear points of dissociation in terms of phenotypes and putatively affected neural circuitry. We highlight emerging work from the clinical neuroscience literature examining neural correlates of E/I imbalance across children and adults with ASD and adults with both chronic and early-course SCZ. We discuss findings from diverse neuroimaging studies across distinct modalities, conducted with electroencephalography, magnetoencephalography, proton magnetic resonance spectroscopy, and functional magnetic resonance imaging, including effects observed both during task and at rest. Throughout this review, we discuss points of convergence and divergence in the ASD and SCZ literature, with a focus on disruptions in neural E/I balance. We also consider these findings in relation to predictions generated by theoretical neuroscience, particularly computational models predicting E/I imbalance across disorders. Finally, we discuss how human noninvasive neuroimaging can benefit from pharmacological challenge studies to reveal mechanisms in ASD and SCZ. Collectively, we attempt to shed light on shared and divergent neuroimaging effects across disorders with the goal of informing future research examining the mechanisms underlying the E/I imbalance hypothesis across neurodevelopmental disorders. We posit that such translational efforts are vital to facilitate development of neurobiologically informed treatment strategies across neuropsychiatric conditions. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights

Adaptive optimization and control using neural networks

Energy Technology Data Exchange (ETDEWEB)

Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

1993-10-22

Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Suliang Ma

2016-11-01

Full Text Available Photovoltaic (PV systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP. Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL non-linear controller combined with an artificial neural network (ANN is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

Non-linear vibrating systems excited by a nonideal energy source with a large slope characteristic

Science.gov (United States)

González-Carbajal, Javier; Domínguez, Jaime

2017-11-01

This paper revisits the problem of an unbalanced motor attached to a fixed frame by means of a nonlinear spring and a linear damper. The excitation provided by the motor is, in general, nonideal, which means it is affected by the vibratory response. Since the system behaviour is highly dependent on the order of magnitude of the motor characteristic slope, the case of large slope is considered herein. Some Perturbation Methods are applied to the system of equations, which allows transforming the original 4D system into a much simpler 2D system. The fixed points of this reduced system and their stability are carefully studied. We find the existence of a Hopf bifurcation which, to the authors' knowledge, has not been addressed before in the literature. These analytical results are supported by numerical simulations. We also compare our approach and results with those published by other authors.

Geometrically nonlinear resonance of higher-order shear deformable functionally graded carbon-nanotube-reinforced composite annular sector plates excited by harmonic transverse loading

Science.gov (United States)

Gholami, Raheb; Ansari, Reza

2018-02-01

This article presents an attempt to study the nonlinear resonance of functionally graded carbon-nanotube-reinforced composite (FG-CNTRC) annular sector plates excited by a uniformly distributed harmonic transverse load. To this purpose, first, the extended rule of mixture including the efficiency parameters is employed to approximately obtain the effective material properties of FG-CNTRC annular sector plates. Then, the focus is on presenting the weak form of discretized mathematical formulation of governing equations based on the variational differential quadrature (VDQ) method and Hamilton's principle. The geometric nonlinearity and shear deformation effects are considered based on the von Kármán assumptions and Reddy's third-order shear deformation plate theory, respectively. The discretization process is performed via the generalized differential quadrature (GDQ) method together with numerical differential and integral operators. Then, an efficient multi-step numerical scheme is used to obtain the nonlinear dynamic behavior of the FG-CNTRC annular sector plates near their primary resonance as the frequency-response curve. The accuracy of the present results is first verified and then a parametric study is presented to show the impacts of CNT volume fraction, CNT distribution pattern, geometry of annular sector plate and sector angle on the nonlinear frequency-response curve of FG-CNTRC annular sector plates with different edge supports.

Sweep excitation with order tracking: A new tactic for beam crack analysis

Science.gov (United States)

Wei, Dongdong; Wang, KeSheng; Zhang, Mian; Zuo, Ming J.

2018-04-01

Crack detection in beams and beam-like structures is an important issue in industry and has attracted numerous investigations. A local crack leads to global system dynamics changes and produce non-linear vibration responses. Many researchers have studied these non-linearities for beam crack diagnosis. However, most reported methods are based on impact excitation and constant frequency excitation. Few studies have focused on crack detection through external sweep excitation which unleashes abundant dynamic characteristics of the system. Together with a signal resampling technique inspired by Computed Order Tracking, this paper utilize vibration responses under sweep excitations to diagnose crack status of beams. A data driven method for crack depth evaluation is proposed and window based harmonics extracting approaches are studied. The effectiveness of sweep excitation and the proposed method is experimentally validated.

Equivalent non-Gaussian excitation method for response moment calculation of systems under non-Gaussian random excitation

International Nuclear Information System (INIS)

Tsuchida, Takahiro; Kimura, Koji

2015-01-01

Equivalent non-Gaussian excitation method is proposed to obtain the moments up to the fourth order of the response of systems under non-Gaussian random excitation. The excitation is prescribed by the probability density and power spectrum. Moment equations for the response can be derived from the stochastic differential equations for the excitation and the system. However, the moment equations are not closed due to the nonlinearity of the diffusion coefficient in the equation for the excitation. In the proposed method, the diffusion coefficient is replaced with the equivalent diffusion coefficient approximately to obtain a closed set of the moment equations. The square of the equivalent diffusion coefficient is expressed by the second-order polynomial. In order to demonstrate the validity of the method, a linear system to non-Gaussian excitation with generalized Gaussian distribution is analyzed. The results show the method is applicable to non-Gaussian excitation with the widely different kurtosis and bandwidth. (author)

Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

Directory of Open Access Journals (Sweden)

David Breuer

2014-03-01

Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

Back reaction of excitations on a vortex

International Nuclear Information System (INIS)

Arodz, H.; Hadasz, L.

1997-01-01

Excitations of a vortex are usually considered in a linear approximation neglecting their back reaction on the vortex. In the present paper we investigate back reaction of Proca-type excitations on a straight linear vortex in the Abelian Higgs model. We propose an exact ansatz for fields of the excited vortex. From an initial set of six nonlinear field equations we obtain (in a limit of weak excitations) two linear wave equations for the back reaction corrections. Their approximate solutions are found in the cases of plane wave and wave-packet-type excitations. We find that the excited vortex radiates the vector field and that the Higgs field has a very broad oscillating component. copyright 1997 The American Physical Society

Origin of optical non-linear response in TiN owing to excitation dynamics of surface plasmon resonance electronic oscillations

Science.gov (United States)

Divya, S.; Nampoori, V. P. N.; Radhakrishnan, P.; Mujeeb, A.

2014-08-01

TiN nanoparticles of average size 55 nm were investigated for their optical non-linear properties. During the experiment the irradiated laser wavelength coincided with the surface plasmon resonance (SPR) peak of the nanoparticle. The large non-linearity of the nanoparticle was attributed to the plasmon resonance, which largely enhanced the local field within the nanoparticle. Both open and closed aperture Z-scan experiments were performed and the corresponding optical constants were explored. The post-excitation absorption spectra revealed the interesting phenomenon of photo fragmentation leading to the blue shift in band gap and red shift in the SPR. The results are discussed in terms of enhanced interparticle interaction simultaneous with size reduction. Here, the optical constants being intrinsic constants for a particular sample change unusually with laser power intensity. The dependence of χ(3) is discussed in terms of the size variation caused by photo fragmentation. The studies proved that the TiN nanoparticles are potential candidates in photonics technology offering huge scope to study unexplored research for various expedient applications.

Probing the interatomic potential of solids with strong-field nonlinear phononics

Science.gov (United States)

von Hoegen, A.; Mankowsky, R.; Fechner, M.; Först, M.; Cavalleri, A.

2018-03-01

Nonlinear optical techniques at visible frequencies have long been applied to condensed matter spectroscopy. However, because many important excitations of solids are found at low energies, much can be gained from the extension of nonlinear optics to mid-infrared and terahertz frequencies. For example, the nonlinear excitation of lattice vibrations has enabled the dynamic control of material functions. So far it has only been possible to exploit second-order phonon nonlinearities at terahertz field strengths near one million volts per centimetre. Here we achieve an order-of-magnitude increase in field strength and explore higher-order phonon nonlinearities. We excite up to five harmonics of the A1 (transverse optical) phonon mode in the ferroelectric material lithium niobate. By using ultrashort mid-infrared laser pulses to drive the atoms far from their equilibrium positions, and measuring the large-amplitude atomic trajectories, we can sample the interatomic potential of lithium niobate, providing a benchmark for ab initio calculations for the material. Tomography of the energy surface by high-order nonlinear phononics could benefit many aspects of materials research, including the study of classical and quantum phase transitions.

Behaviour in O of the Neural Networks Training Cost

DEFF Research Database (Denmark)

Goutte, Cyril

1998-01-01

We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location. These calc......We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location....... These calculations arerelated to practical and theoretical aspects of neural networks training....

Neural networks in continuous optical media

International Nuclear Information System (INIS)

Anderson, D.Z.

1987-01-01

The authors' interest is to see to what extent neural models can be implemented using continuous optical elements. Thus these optical networks represent a continuous distribution of neuronlike processors rather than a discrete collection. Most neural models have three characteristic features: interconnections; adaptivity; and nonlinearity. In their optical representation the interconnections are implemented with linear one- and two-port optical elements such as lenses and holograms. Real-time holographic media allow these interconnections to become adaptive. The nonlinearity is achieved with gain, for example, from two-beam coupling in photorefractive media or a pumped dye medium. Using these basic optical elements one can in principle construct continuous representations of a number of neural network models. The authors demonstrated two devices based on continuous optical elements: an associative memory which recalls an entire object when addressed with a partial object and a tracking novelty filter which identifies time-dependent features in an optical scene. These devices demonstrate the potential of distributed optical elements to implement more formal models of neural networks

Is DNA a nonlinear dynamical system where solitary conformational ...

Indian Academy of Sciences (India)

Unknown

DNA is considered as a nonlinear dynamical system in which solitary conformational waves can be excited. The ... nonlinear differential equations and their soliton-like solu- .... structure and dynamics can be added till the most accurate.

Nonlinear NDT: A Route to Conventional Ultrasonic Testing

OpenAIRE

Igor Solodov

2016-01-01

The bottleneck problem of nonlinear NDT is a low efficiency of conversion from fundamental frequency to nonlinear frequency components. In this paper, it is proposed to use a combination of nonlinearity with Local Defect Resonance (LDR) to enhance substantially the input-output conversion. Since LDR is an efficient resonance “amplifier” of the local vibrations, it manifests a profound nonlinearity even at moderate ultrasonic excitation level. As the driving frequency matches the LDR-frequency...

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Nonlinear Parametric Excitation Effect Induces Stability Transitions in Swimming Direction of Flexible Superparamagnetic Microswimmers.

Science.gov (United States)

Harduf, Yuval; Jin, Dongdong; Or, Yizhar; Zhang, Li

2018-04-05

Microscopic artificial swimmers have recently become highly attractive due to their promising potential for biomedical microrobotic applications. Previous pioneering work has demonstrated the motion of a robotic microswimmer with a flexible chain of superparamagnetic beads, which is actuated by applying an oscillating external magnetic field. Interestingly, they have shown that the microswimmer's orientation undergoes a 90°-transition when the magnetic field's oscillation amplitude is increased above a critical value. This unexpected transition can cause severe problems in steering and manipulation of flexible magnetic microrobotic swimmers. Thus, theoretical understanding and analysis of the physical origins of this effect are of crucial importance. In this work, we investigate this transition both theoretically and experimentally by using numerical simulations and presenting a novel flexible microswimmer with an anisotropic superparamagnetic head. We prove that this effect depends on both frequency and amplitude of the oscillating magnetic field, and demonstrate existence of an optimal amplitude achieving maximal swimming speed. Asymptotic analysis of a minimal two-link model reveals that the changes in the swimmer's direction represent stability transitions, which are induced by a nonlinear parametric excitation.

Studies on linear, nonlinear optical and excited state dynamics of silicon nanoparticles prepared by picosecond laser ablation

Energy Technology Data Exchange (ETDEWEB)

Hamad, Syed; Nageswara Rao, S. V. S.; Pathak, A. P. [School of Physics, University of Hyderabad, Hyderabad 500046, Telangana (India); Krishna Podagatlapalli, G.; Mounika, R.; Venugopal Rao, S., E-mail: soma-venu@yahoo.com, E-mail: soma-venu@uohyd.ac.in [Advanced Center of Research in High Energy Materials (ACRHEM), University of Hyderabad, Hyderabad 500046, Telangana (India)

2015-12-15

We report results from our studies on the fabrication and characterization of silicon (Si) nanoparticles (NPs) and nanostructures (NSs) achieved through the ablation of Si target in four different liquids using ∼2 picosecond (ps) pulses. The consequence of using different liquid media on the ablation of Si target was investigated by studying the surface morphology along with material composition of Si based NPs. The recorded mean sizes of these NPs were ∼9.5 nm, ∼37 nm, ∼45 nm and ∼42 nm obtained in acetone, water, dichloromethane (DCM) and chloroform, respectively. The generated NPs were characterized by selected area electron diffraction (SAED), high resolution transmission microscopy (HRTEM), Raman spectroscopic techniques and Photoluminescence (PL) studies. SAED, HRTEM and Raman spectroscopy data confirmed that the material composition was Si NPs in acetone, Si/SiO{sub 2} NPs in water, Si-C NPs in DCM and Si-C NPs in chloroform and all of them were confirmed to be polycrystalline in nature. Surface morphological information of the fabricated Si substrates was obtained using the field emission scanning electron microscopic (FESEM) technique. FESEM data revealed the formation of laser induced periodic surface structures (LIPSS) for the case of ablation in acetone and water while random NSs were observed for the case of ablation in DCM and chloroform. Femtosecond (fs) nonlinear optical properties and excited state dynamics of these colloidal Si NPs were investigated using the Z-scan and pump-probe techniques with ∼150 fs (100 MHz) and ∼70 fs (1 kHz) laser pulses, respectively. The fs pump-probe data obtained at 600 nm consisted of single and double exponential decays which were tentatively assigned to electron-electron collisional relaxation (<1 ps) and non-radiative transitions (>1 ps). Large third order optical nonlinearities (∼10{sup −14} e.s.u.) for these colloids have been estimated from Z-scan data at an excitation wavelength of 680 nm

Studies on linear, nonlinear optical and excited state dynamics of silicon nanoparticles prepared by picosecond laser ablation

Directory of Open Access Journals (Sweden)

Syed Hamad

2015-12-01

Full Text Available We report results from our studies on the fabrication and characterization of silicon (Si nanoparticles (NPs and nanostructures (NSs achieved through the ablation of Si target in four different liquids using ∼2 picosecond (ps pulses. The consequence of using different liquid media on the ablation of Si target was investigated by studying the surface morphology along with material composition of Si based NPs. The recorded mean sizes of these NPs were ∼9.5 nm, ∼37 nm, ∼45 nm and ∼42 nm obtained in acetone, water, dichloromethane (DCM and chloroform, respectively. The generated NPs were characterized by selected area electron diffraction (SAED, high resolution transmission microscopy (HRTEM, Raman spectroscopic techniques and Photoluminescence (PL studies. SAED, HRTEM and Raman spectroscopy data confirmed that the material composition was Si NPs in acetone, Si/SiO2 NPs in water, Si-C NPs in DCM and Si-C NPs in chloroform and all of them were confirmed to be polycrystalline in nature. Surface morphological information of the fabricated Si substrates was obtained using the field emission scanning electron microscopic (FESEM technique. FESEM data revealed the formation of laser induced periodic surface structures (LIPSS for the case of ablation in acetone and water while random NSs were observed for the case of ablation in DCM and chloroform. Femtosecond (fs nonlinear optical properties and excited state dynamics of these colloidal Si NPs were investigated using the Z-scan and pump-probe techniques with ∼150 fs (100 MHz and ∼70 fs (1 kHz laser pulses, respectively. The fs pump-probe data obtained at 600 nm consisted of single and double exponential decays which were tentatively assigned to electron-electron collisional relaxation (1 ps. Large third order optical nonlinearities (∼10−14 e.s.u. for these colloids have been estimated from Z-scan data at an excitation wavelength of 680 nm suggesting that the colloidal Si NPs find

Nonlinear identification and control a neural network approach

CERN Document Server

Liu, G P

2001-01-01

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series otTers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The time for nonlinear control to enter routine application seems to be approaching. Nonlinear control has had a long gestation period but much ofthe past has been concerned with methods that involve formal nonlinear functional model representations. It seems more likely that the breakthough will come through the use of other more flexible and ame...

Synaptic control of motoneuronal excitability

DEFF Research Database (Denmark)

Rekling, J C; Funk, G D; Bayliss, D A

2000-01-01

important in understanding the transformation of neural activity to motor behavior. Here, we review recent studies on the control of motoneuronal excitability, focusing on synaptic and cellular properties. We first present a background description of motoneurons: their development, anatomical organization......, and membrane properties, both passive and active. We then describe the general anatomical organization of synaptic input to motoneurons, followed by a description of the major transmitter systems that affect motoneuronal excitability, including ligands, receptor distribution, pre- and postsynaptic actions...... and norepinephrine, and neuropeptides, as well as the glutamate and GABA acting at metabotropic receptors, modulate motoneuronal excitability through pre- and postsynaptic actions. Acting principally via second messenger systems, their actions converge on common effectors, e.g., leak K(+) current, cationic inward...

Dynamics of Solid Body in Magnetic Suspension under Periodic Excitation

Directory of Open Access Journals (Sweden)

A. M. Gouskov

2017-01-01

Full Text Available The article studies dynamics of ferromagnetic body in hybrid magnetic suspension (HMS. The body is supposed to have one degree of freedom and a nonlinear magnetic force dependence on the current and displacement. The magnetic force induced in the HMS is divided into a passive component and an active one. Specifying the law of current variation in the coil allows us to generate nonlinear oscillations under electromagnet action. To provide periodic excitation the appropriate law of the current variation in the electromagnet coil is proposed. The mathematical model includes external periodic step-excitation. The equation of motion is formed. The scales of similarity are highlighted in the system, and the equation of motion is reduced to dimensionless form.The motion dynamics is studied numerically. The relaxation method was used to determine the periodic motions at different values of dimensionless frequency of the electromagnet excitation as well as to estimate the influence of other dimensionless parameters on the system dynamics. The amplitude-frequency curve analysis allows us to come to conclusion that the nature of system nonlinearity is rigid. Adding the external periodic step-excitation leads to the qualitative change in the nature of movement. This points to the occurrence of bifurcation.

Mobile robot nonlinear feedback control based on Elman neural network observer

Directory of Open Access Journals (Sweden)

Khaled Al-Mutib

2015-12-01

Full Text Available This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input–output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.

Dynamics of nonlinear feedback control

OpenAIRE

Snippe, H.P.; Hateren, J.H. van

2007-01-01

Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain signal (resp. the attenuation signal) is obtained through a concatenation of an instantaneous nonlinearity and a linear low-pass filter operating on the output of the feedback loop. For input step...

Optical nonlinearity of organic dyes as studied by Z-scan and ...

Indian Academy of Sciences (India)

Unknown

Keywords. Excited state absorption; Z-scan; nonlinear refractive index; excited .... The σes value can be determined with the help of three and four level .... laser pulse the molecules in the thermal equilibrium position of first excited state (level 2.

Autoresonant Excitation of Antiproton Plasmas

CERN Document Server

Andresen, Gorm B; Baquero-Ruiz, Marcelo; Bertsche, William; Bowe, Paul D; Butler, Eoin; Carpenter, P T; Cesar, Claudio L; Chapman, Steven; Charlton, Michael; Fajans, Joel; Friesen, Tim; Fujiwara, Makoto C; Gill, David R; Hangst, Jeffrey S; Hardy, Walter N; Hayden, Michael E; Humphries, Andrew J; Hurt, J L; Hydomako, Richard; Jonsell, Svante; Madsen, Niels; Menary, Scott; Nolan, Paul; Olchanski, Konstantin; Olin, Art; Povilus, Alexander; Pusa, Petteri; Robicheaux, Francis; Sarid, Eli; Silveira, Daniel M; So, Chukman; Storey, James W; Thompson, Robert I; van der Werf, Dirk P; Wurtele, Jonathan S; Yamazaki, Yasunori

2011-01-01

We demonstrate controllable excitation of the center-of-mass longitudinal motion of a thermal antiproton plasma using a swept-frequency autoresonant drive. When the plasma is cold, dense, and highly collective in nature, we observe that the entire system behaves as a single-particle nonlinear oscillator, as predicted by a recent theory. In contrast, only a fraction of the antiprotons in a warm plasma can be similarly excited. Antihydrogen was produced and trapped by using this technique to drive antiprotons into a positron plasma, thereby initiating atomic recombination.

Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Directory of Open Access Journals (Sweden)

Jaime Buitrago

2017-01-01

Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

Quantitative theory of driven nonlinear brain dynamics.

Science.gov (United States)

Roberts, J A; Robinson, P A

2012-09-01

Strong periodic stimuli such as bright flashing lights evoke nonlinear responses in the brain and interact nonlinearly with ongoing cortical activity, but the underlying mechanisms for these phenomena are poorly understood at present. The dominant features of these experimentally observed dynamics are reproduced by the dynamics of a quantitative neural field model subject to periodic drive. Model power spectra over a range of drive frequencies show agreement with multiple features of experimental measurements, exhibiting nonlinear effects including entrainment over a range of frequencies around the natural alpha frequency f(α), subharmonic entrainment near 2f(α), and harmonic generation. Further analysis of the driven dynamics as a function of the drive parameters reveals rich nonlinear dynamics that is predicted to be observable in future experiments at high drive amplitude, including period doubling, bistable phase-locking, hysteresis, wave mixing, and chaos indicated by positive Lyapunov exponents. Moreover, photosensitive seizures are predicted for physiologically realistic model parameters yielding bistability between healthy and seizure dynamics. These results demonstrate the applicability of neural field models to the new regime of periodically driven nonlinear dynamics, enabling interpretation of experimental data in terms of specific generating mechanisms and providing new tests of the theory. Copyright © 2012 Elsevier Inc. All rights reserved.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

Science.gov (United States)

1996-01-01

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

Neural network based online simultaneous policy update algorithm for solving the HJI equation in nonlinear H∞ control.

Science.gov (United States)

Wu, Huai-Ning; Luo, Biao

2012-12-01

It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm.

Effects of quadratic and cubic nonlinearities on a perfectly tuned parametric amplifier

DEFF Research Database (Denmark)

Neumeyer, Stefan; Sorokin, Vladislav; Thomsen, Jon Juel

2016-01-01

We consider the performance of a parametric amplifier with perfect tuning (two-to-one ratio between the parametric and direct excitation frequencies) and quadratic and cubic nonlinearities. A forced Duffing–Mathieu equation with appended quadratic nonlinearity is considered as the model system......, and approximate analytical steady-state solutions and corresponding stabilities are obtained by the method of varying amplitudes. Some general effects of pure quadratic, and mixed quadratic and cubic nonlinearities on parametric amplification are shown. In particular, the effects of mixed quadratic and cubic...... nonlinearities may generate additional amplitude–frequency solutions. In this case an increased response and a more phase sensitive amplitude (phase between excitation frequencies) is obtained, as compared to the case with either pure quadratic or cubic nonlinearity. Furthermore, jumps and bi...

A new method to estimate parameters of linear compartmental models using artificial neural networks

International Nuclear Information System (INIS)

Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

1998-01-01

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

Optimal Brain Surgeon on Artificial Neural Networks in

DEFF Research Database (Denmark)

Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

2012-01-01

It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

Nonlinear performance of asymmetric coupler based on dual-core photonic crystal fiber: Towards sub-nanojoule solitonic ultrafast all-optical switching

Science.gov (United States)

Curilla, L.; Astrauskas, I.; Pugzlys, A.; Stajanca, P.; Pysz, D.; Uherek, F.; Baltuska, A.; Bugar, I.

2018-05-01

We demonstrate ultrafast soliton-based nonlinear balancing of dual-core asymmetry in highly nonlinear photonic crystal fiber at sub-nanojoule pulse energy level. The effect of fiber asymmetry was studied experimentally by selective excitation and monitoring of individual fiber cores at different wavelengths between 1500 nm and 1800 nm. Higher energy transfer rate to non-excited core was observed in the case of fast core excitation due to nonlinear asymmetry balancing of temporal solitons, which was confirmed by the dedicated numerical simulations based on the coupled generalized nonlinear Schrödinger equations. Moreover, the simulation results correspond qualitatively with the experimentally acquired dependences of the output dual-core extinction ratio on excitation energy and wavelength. In the case of 1800 nm fast core excitation, narrow band spectral intensity switching between the output channels was registered with contrast of 23 dB. The switching was achieved by the change of the excitation pulse energy in sub-nanojoule region. The performed detailed analysis of the nonlinear balancing of dual-core asymmetry in solitonic propagation regime opens new perspectives for the development of ultrafast nonlinear all-optical switching devices.

A Volterra series approach to the approximation of stochastic nonlinear dynamics

NARCIS (Netherlands)

Wouw, van de N.; Nijmeijer, H.; Campen, van D.H.

2002-01-01

A response approximation method for stochastically excited, nonlinear, dynamic systems is presented. Herein, the output of the nonlinear system isapproximated by a finite-order Volterra series. The original nonlinear system is replaced by a bilinear system in order to determine the kernels of this

Nonlinear interaction of strong microwave beam with the ionosphere MINIX rocket experiment

Energy Technology Data Exchange (ETDEWEB)

Kaya, N.; Matsumoto, H.; Miyatake, S.; Kimura, I.; Nagatomo, M.; Obayashi, T.

1986-01-01

A rocket-borne experiment called MINIX was carried out to investigate the nonlinear interaction of a strong microwave energy beam with the ionosphere. The MINIX stands for Microwave-Ionosphere Nonlinear Interaction Experiment and was carried out on August 29, 1983. The objectives of the MINIX is to study possible impacts of the SPS microwave energy beam on the ionosphere such as the Ohmic heating and plasma wave excitation. The experiment showed that the microwave with f = 2.45 GHz nonlinearly excites various electrostatic plasma waves, though no Ohmic heating effects were detected. 4 figures.

Nonlinear interaction of strong microwave beam with the ionosphere MINIX rocket experiment

Science.gov (United States)

Kaya, N.; Matsumoto, H.; Miyatake, S.; Kimura, I.; Nagatomo, M.

A rocket-borne experiment called 'MINIX' was carried out to investigate the nonlinear interaction of a strong microwave energy beam with the ionosphere. The MINIX stands for Microwave-Ionosphere Nonlinear Interaction eXperiment and was carried out on August 29, 1983. The objective of the MINIX is to study possible impacts of the SPS microwave energy beam on the ionosphere, such as the ohmic heating and plasma wave excitation. The experiment showed that the microwave with f = 2.45 GHz nonlinearly excites various electrostatic plasma waves, though no ohmic heating effects were detected.

Nonlinear interaction of strong microwave beam with the ionosphere MINIX rocket experiment

International Nuclear Information System (INIS)

Kaya, N.; Matsumoto, H.; Miyatake, S.; Kimura, I.; Nagatomo, M.; Obayashi, T.

1986-01-01

A rocket-borne experiment called MINIX was carried out to investigate the nonlinear interaction of a strong microwave energy beam with the ionosphere. The MINIX stands for Microwave-Ionosphere Nonlinear Interaction Experiment and was carried out on August 29, 1983. The objectives of the MINIX is to study possible impacts of the SPS microwave energy beam on the ionosphere such as the Ohmic heating and plasma wave excitation. The experiment showed that the microwave with f = 2.45 GHz nonlinearly excites various electrostatic plasma waves, though no Ohmic heating effects were detected. 4 figures

Robust energy harvesting from walking vibrations by means of nonlinear cantilever beams

Science.gov (United States)

Kluger, Jocelyn M.; Sapsis, Themistoklis P.; Slocum, Alexander H.

2015-04-01

In the present work we examine how mechanical nonlinearity can be appropriately utilized to achieve strong robustness of performance in an energy harvesting setting. More specifically, for energy harvesting applications, a great challenge is the uncertain character of the excitation. The combination of this uncertainty with the narrow range of good performance for linear oscillators creates the need for more robust designs that adapt to a wider range of excitation signals. A typical application of this kind is energy harvesting from walking vibrations. Depending on the particular characteristics of the person that walks as well as on the pace of walking, the excitation signal obtains completely different forms. In the present work we study a nonlinear spring mechanism that is composed of a cantilever wrapping around a curved surface as it deflects. While for the free cantilever, the force acting on the free tip depends linearly on the tip displacement, the utilization of a contact surface with the appropriate distribution of curvature leads to essentially nonlinear dependence between the tip displacement and the acting force. The studied nonlinear mechanism has favorable mechanical properties such as low frictional losses, minimal moving parts, and a rugged design that can withstand excessive loads. Through numerical simulations we illustrate that by utilizing this essentially nonlinear element in a 2 degrees-of-freedom (DOF) system, we obtain strongly nonlinear energy transfers between the modes of the system. We illustrate that this nonlinear behavior is associated with strong robustness over three radically different excitation signals that correspond to different walking paces. To validate the strong robustness properties of the 2DOF nonlinear system, we perform a direct parameter optimization for 1DOF and 2DOF linear systems as well as for a class of 1DOF and 2DOF systems with nonlinear springs similar to that of the cubic spring that are physically realized

A novel nonlinear damage resonance intermodulation effect for structural health monitoring

Science.gov (United States)

Ciampa, Francesco; Scarselli, Gennaro; Meo, Michele

2017-04-01

This paper is aimed at developing a theoretical model able to predict the generation of nonlinear elastic effects associated to the interaction of ultrasonic waves with the steady-state nonlinear response of local defect resonance (LDR). The LDR effect is used in nonlinear elastic wave spectroscopy to enhance the excitation of the material damage at its local resonance, thus to dramatically increase the vibrational amplitude of material nonlinear phenomena. The main result of this work is to prove both analytically and experimentally the generation of novel nonlinear elastic wave effects, here named as nonlinear damage resonance intermodulation, which correspond to a nonlinear intermodulation between the driving frequency and the LDR one. Beside this intermodulation effect, other nonlinear elastic wave phenomena such as higher harmonics of the input frequency and superharmonics of LDR frequency were found. The analytical model relies on solving the nonlinear equation of motion governing bending displacement under the assumption of both quadratic and cubic nonlinear defect approximation. Experimental tests on a damaged composite laminate confirmed and validated these predictions and showed that using continuous periodic excitation, the nonlinear structural phenomena associated to LDR could also be featured at locations different from the damage resonance. These findings will provide new opportunities for material damage detection using nonlinear ultrasounds.

IMNN: Information Maximizing Neural Networks

Science.gov (United States)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

A comparison of linear and nonlinear statistical techniques in performance attribution.

Science.gov (United States)

Chan, N H; Genovese, C R

2001-01-01

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.

Dynamic training algorithm for dynamic neural networks

International Nuclear Information System (INIS)

Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

1996-01-01

The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

The quest for a Quantum Neural Network

OpenAIRE

Schuld, M.; Sinayskiy, I.; Petruccione, F.

2014-01-01

With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quant...

Nonlinear optical studies in semiconductor-doped glasses under ...

Indian Academy of Sciences (India)

Abstract. Nonlinear optical studies in semiconductor-doped glasses (SDGs) are per- formed under femtosecond laser pulse excitation. Z-scan experiments with 800 nm wave- length pulses are used to excite SDG samples in the resonance and non-resonance regimes. Schott colour glass filter OG 515 shows stronger ...

Lyapunov Functions to Caputo Fractional Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Ravi Agarwal

2018-05-01

Full Text Available One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability and often the direct Lyapunov method is used to study stability properties (usually these Lyapunov functions do not depend on the time variable. In connection with the Lyapunov fractional method we present a brief overview of the most popular fractional order derivatives of Lyapunov functions among Caputo fractional delay differential equations. These derivatives are applied to various types of neural networks with variable coefficients and time-varying delays. We show that quadratic Lyapunov functions and their Caputo fractional derivatives are not applicable in some cases when one studies stability properties. Some sufficient conditions for stability of equilibrium of nonlinear Caputo fractional neural networks with time dependent transmission delays, time varying self-regulating parameters of all units and time varying functions of the connection between two neurons in the network are obtained. The cases of time varying Lipschitz coefficients as well as nonLipschitz activation functions are studied. We illustrate our theory on particular nonlinear Caputo fractional neural networks.

Bifurcation analysis and spatio-temporal patterns of nonlinear oscillations in a delayed neural network with unidirectional coupling

International Nuclear Information System (INIS)

Song Yongli; Tadé, Moses O; Zhang Tonghua

2009-01-01

In this paper, a delayed neural network with unidirectional coupling is considered which consists of two two-dimensional nonlinear differential equation systems with exponential decay where one system receives a delayed input from the other system. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the centre manifold theorem. We also investigate the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay-differential equations combined with representation theory of Lie groups. Then the global continuation of phase-locked periodic solutions is investigated. Numerical simulations are given to illustrate the results obtained

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

Science.gov (United States)

Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-07-20

Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Seng-Chi Chen

2014-01-01

Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

Mathematical modeling and applications in nonlinear dynamics

CERN Document Server

Merdan, Hüseyin

2016-01-01

The book covers nonlinear physical problems and mathematical modeling, including molecular biology, genetics, neurosciences, artificial intelligence with classical problems in mechanics and astronomy and physics. The chapters present nonlinear mathematical modeling in life science and physics through nonlinear differential equations, nonlinear discrete equations and hybrid equations. Such modeling can be effectively applied to the wide spectrum of nonlinear physical problems, including the KAM (Kolmogorov-Arnold-Moser (KAM)) theory, singular differential equations, impulsive dichotomous linear systems, analytical bifurcation trees of periodic motions, and almost or pseudo- almost periodic solutions in nonlinear dynamical systems. Provides methods for mathematical models with switching, thresholds, and impulses, each of particular importance for discontinuous processes Includes qualitative analysis of behaviors on Tumor-Immune Systems and methods of analysis for DNA, neural networks and epidemiology Introduces...

Coupled bending and torsional vibration of a rotor system with nonlinear friction

International Nuclear Information System (INIS)

Hua, Chunli; Cao, Guohua; Zhu, Zhencai; Rao, Zhushi; Ta, Na

2017-01-01

Unacceptable vibrations induced by the nonlinear friction in a rotor system seriously affect the health and reliability of the rotating ma- chinery. To find out the basic excitation mechanism and characteristics of the vibrations, a coupled bending and torsional nonlinear dynamic model of rotor system with nonlinear friction is presented. The dynamic friction characteristic is described with a Stribeck curve, which generates nonlinear friction related to relative velocity. The motion equations of unbalance rotor system are established by the Lagrangian approach. Through numerical calculation, the coupled vibration characteristics of a rotor system under nonlinear friction are well investigated. The influence of main system parameters on the behaviors of the system is discussed. The bifurcation diagrams, waterfall plots, the times series, orbit trails, phase plane portraits and Poincaré maps are obtained to analyze dynamic characteristics of the rotor system and the results reveal multiform complex nonlinear dynamic responses of rotor system under rubbing. These analysis results of the present paper can effectively provide a theoretical reference for structural design of rotor systems and be used to diagnose self- excited vibration faults in this kind of rotor systems. The present research could contribute to further understanding on the self-excited vibration and the bending and torsional coupling vibration of the rotor systems with Stribeck friction model.

Coupled bending and torsional vibration of a rotor system with nonlinear friction

Energy Technology Data Exchange (ETDEWEB)

Hua, Chunli; Cao, Guohua; Zhu, Zhencai [China University of Mining and Technology, Xuzhou (China); Rao, Zhushi; Ta, Na [Shanghai Jiao Tong University, Shanghai (China)

2017-06-15

Unacceptable vibrations induced by the nonlinear friction in a rotor system seriously affect the health and reliability of the rotating ma- chinery. To find out the basic excitation mechanism and characteristics of the vibrations, a coupled bending and torsional nonlinear dynamic model of rotor system with nonlinear friction is presented. The dynamic friction characteristic is described with a Stribeck curve, which generates nonlinear friction related to relative velocity. The motion equations of unbalance rotor system are established by the Lagrangian approach. Through numerical calculation, the coupled vibration characteristics of a rotor system under nonlinear friction are well investigated. The influence of main system parameters on the behaviors of the system is discussed. The bifurcation diagrams, waterfall plots, the times series, orbit trails, phase plane portraits and Poincaré maps are obtained to analyze dynamic characteristics of the rotor system and the results reveal multiform complex nonlinear dynamic responses of rotor system under rubbing. These analysis results of the present paper can effectively provide a theoretical reference for structural design of rotor systems and be used to diagnose self- excited vibration faults in this kind of rotor systems. The present research could contribute to further understanding on the self-excited vibration and the bending and torsional coupling vibration of the rotor systems with Stribeck friction model.

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

Directory of Open Access Journals (Sweden)

Toly Chen

2012-01-01

Full Text Available A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.

Electron-lattice Interaction and Nonlinear Excitations in Cuprate Structures

International Nuclear Information System (INIS)

Paulsen, J.; Eschrig, H.; Drechsler, S.L.; Malek, J.

1995-01-01

A low temperature lattice modulation of the chains of the YBa 2 Cu 3 O 7 is considered by deriving a Hamiltonian of electron-lattice interaction from density-functional calculations for deformed lattice and solving it for the groundstate. Hubbard-type Coulomb interaction is included. The obtained groundstate is a charge-density-wave state with a pereodicity of four lattice constants and a gap for one-electron excitations of about 1eV, sensitively depending on parameters of the Hamiltonian. There are lots of polaronic and solitonic excitations with formation energies deep in the gap, which can pin the Fermi level and thus produce again metallicity of the chain. They might also contribute to pairing of holes in adjacent CuO 2 -planes. (author)

Progress in nonlinear nano-optics

CERN Document Server

Lienau, Christoph; Grunwald, Rüdiger

2015-01-01

This book presents the state of the art in nonlinear nanostructures for ultrafast laser applications. Most recent results in two emerging fields are presented: (i) generation of laser-induced nanostructures in materials like metals, metal oxides and semiconductors, and (ii) ultrafast excitation and energy transfer in nanoscale physical, chemical and hybrid systems. Particular emphasis is laid on the up-to-date controversially discussed mechanisms of sub-wavelength ripple formation including models of self-organized material transport and multiphoton excitation channels, nonlinear optics of plasmonic structures (nanotips, nanowires, 3D-metamaterials), and energy localization and transport on ultrafast time scale and spatial nanoscale. High-resolution spectroscopy, simulation and characterization techniques are reported. New applications of ultrashort-pulsed lasers for materials processing and the use of nanostructured materials for characterizing laser fields and laser-matter-interactions are discussed.

Stochastic Parameter Estimation of Non-Linear Systems Using Only Higher Order Spectra of the Measured Response

Science.gov (United States)

Vasta, M.; Roberts, J. B.

1998-06-01

Methods for using fourth order spectral quantities to estimate the unknown parameters in non-linear, randomly excited dynamic systems are developed. Attention is focused on the case where only the response is measurable and the excitation is unmeasurable and known only in terms of a stochastic process model. The approach is illustrated through application to a non-linear oscillator with both non-linear damping and stiffness and with excitation modelled as a stationary Gaussian white noise process. The methods have applications in studies of the response of structures to random environmental loads, such as wind and ocean wave forces.

Nonlinear dynamic response of whole pool multiple spent fuel racks subject to three-dimensional excitations

International Nuclear Information System (INIS)

Zhao, Y.; Wilson, P.R.; Stevenson, J.D.

1995-01-01

The seismic evaluation of submerged free standing spent fuel storage racks is more complicated than most other nuclear structural systems. When subjected to three dimensional (3-D) floor seismic excitations the dynamic responses of racks in a pool are hydro dynamically coupled with each other, with the fuel assemblies water in gaps. The motion behavior of the racks is significantly different from that observed using a 3D single rack mode. Few seismic analyses using 3-D whole pool multiple rack models are available in the literature. I this paper an analysis was performed for twelve racks using potential theory for the fluid-structure interaction, and using a 3-D whole pool multi-rack finite element model developed herein. The analysis includes the potential nonlinear dynamic behavior of the impact of fuel-rack, rack-rack and rack-pool wall, the tilting or uplift and the frictional sliding of rack supports, and the impact of the rack supports to the pool floor. (author). 12 refs., 7 figs., 1 tab

Dissimilar trend of nonlinearity in ultrasound transducers and systems at resonance and non-resonance frequencies

DEFF Research Database (Denmark)

Ghasemi, Negareh; Zare, Firuz; Davari, Pooya

2017-01-01

Several factors can affect performance of an ultrasound system such as quality of excitation signal and ultrasound transducer behaviour. Nonlinearity of piezoelectric ultrasound transducers is a key determinant in designing a proper driving power supply. Although, the nonlinearity of piezoelectric...... was excited at different frequencies. Different excitation signals were generated using a linear power amplifier and a multilevel converter within a range of 30–200 V. Empirical relation was developed to express the resistance of the piezoelectric transducer as a nonlinear function of both excitation voltage...... and resonance frequency. The impedance measurements revealed that at higher voltage ranges, the piezoelectric transducer can be easily saturated. Also, it was shown that for the developed ultrasound system composed of two transducers (one transmitter and one receiver), the output voltage measured across...

Nonlinear theory of transverse-multimode plasma accelerators

International Nuclear Information System (INIS)

Kuzelev, M.V.; Panin, V.A.; Plotnikov, A.P.

1991-01-01

The excitation of the higher transverse modes in a plasma-filled waveguide by a high-power electron beam is considered. General nonlinear equations are obtained which treat the excitation of the higher transverse plasma waves by a high-current relativistic beam. Results are presented of the numerical solutions of these equations. In the case of ultrarelativistic beams analytical expressions are found for the maximum amplitudes of the excited modes and the Q of the amplification. Numerical estimates are presented for realistic parameters

Prediction based chaos control via a new neural network

International Nuclear Information System (INIS)

Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

2008-01-01

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

Parameter estimation in space systems using recurrent neural networks

Science.gov (United States)

Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

1991-01-01

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

Excitation of Accelerating Plasma Waves by Counter-propagating Laser Beams

International Nuclear Information System (INIS)

Gennady Shvets; Nathaniel J. Fisch; Alexander Pukhov

2001-01-01

Generation of accelerating plasma waves using two counter-propagating laser beams is considered. Colliding-beam accelerator requires two laser pulses: the long pump and the short timing beam. We emphasize the similarities and differences between the conventional laser wakefield accelerator and the colliding-beam accelerator (CBA). The highly nonlinear nature of the wake excitation is explained using both nonlinear optics and plasma physics concepts. Two regimes of CBA are considered: (i) the short-pulse regime, where the timing beam is shorter than the plasma period, and (ii) the parametric excitation regime, where the timing beam is longer than the plasma period. Possible future experiments are also outlined

Dynamics of nonlinear feedback control

NARCIS (Netherlands)

Snippe, H.P.; Hateren, J.H. van

Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain

Modelling the permeability of polymers: a neural network approach

NARCIS (Netherlands)

Wessling, Matthias; Mulder, M.H.V.; Bos, A.; Bos, A.; van der Linden, M.K.T.; Bos, M.; van der Linden, W.E.

1994-01-01

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a

Nonlinear drift tearing mode. Strong mode of excitation and stabilization mechanisms

International Nuclear Information System (INIS)

Galeev, A.A.; Zelenyj, L.M.; Kuznetsova, M.M.

1985-01-01

A nonlinear theory of magnetic disturbance development in collisionless configurations with magnetic field shear is considered. The instability evolution is investigated with account for the dynamics of ions and potential electric fields which determine the mode stabilization. It has been found that the drift tearing mode possesses metastable properties: in a nonlinear mode even the growth of linearly stable disturbances of the finite amplitude is possible

Enhanced Size Selection in Two-Photon Excitation for CsPbBr3 Perovskite Nanocrystals.

Science.gov (United States)

Chen, Junsheng; Chábera, Pavel; Pascher, Torbjörn; Messing, Maria E; Schaller, Richard; Canton, Sophie; Zheng, Kaibo; Pullerits, Tõnu

2017-10-19

Cesium lead bromide (CsPbBr 3 ) perovskite nanocrystals (NCs), with large two-photon absorption (TPA) cross-section and bright photoluminescence (PL), have been demonstrated as stable two-photon-pumped lasing medium. With two-photon excitation, red-shifted PL spectrum and increased PL lifetime is observed compared with one-photon excitation. We have investigated the origin of such difference using time-resolved laser spectroscopies. We ascribe the difference to the enhanced size selection of NCs by two-photon excitation. Because of inherent nonlinearity, the size dependence of absorption cross-section under TPA is stronger. Consequently, larger size NCs are preferably excited, leading to longer excited-state lifetime and red-shifted PL emission. In a broad view, the enhanced size selection in two-photon excitation of CsPbBr 3 NCs is likely a general feature of the perovskite NCs and can be tuned via NC size distribution to influence their performance within NC-based nonlinear optical materials and devices.

Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

Directory of Open Access Journals (Sweden)

Feng Zhao

2015-01-01

Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed 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.

Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism.

Science.gov (United States)

Liu, Zhi; Chen, Ci; Zhang, Yun; Chen, C L P

2015-03-01

To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

Power laws and elastic nonlinearity in materials with complex microstructure

Energy Technology Data Exchange (ETDEWEB)

Scalerandi, M., E-mail: marco.scalerandi@infm.polito.it

2016-01-28

Nonlinear ultrasonic methods have been widely used to characterize the microstructure of damaged solids and consolidated granular media. Besides distinguishing between materials exhibiting classical nonlinear behaviors from those exhibiting hysteresis, it could be of importance the discrimination between ultrasonic indications from different physical sources (scatterers). Elastic hysteresis could indeed be due to dislocations, grain boundaries, stick-slip at interfaces, etc. Analyzing data obtained on various concrete samples, we show that the power law behavior of the nonlinear indicator vs. the energy of excitation could be used to classify different microscopic features. In particular, the power law exponent ranges between 1 and 3, depending on the nature of nonlinearity. We also provide a theoretical interpretation of the collected data using models for clapping and hysteretic nonlinearities. - Highlights: • Several materials exhibit a nontrivial nonlinear elastic behavior which can be ascribed to different physical sources. • The quantitative nonlinear response is dependent on the type of microstructure present in the material. • A nonlinear indicator could be defined which depends on the excitation energy of the sample. • Assuming a power law dependence, the exponent depends on the microstructure of the material and could evolve in time. • Experimental results on concrete are discussed and a theoretical description is proposed.

Zonal flow excitation by Shukla-Varma modes in a nonuniform dusty magnetoplasma

International Nuclear Information System (INIS)

Shukla, P.K.; Stenflo, L.

2002-01-01

The nonlinear coupling between the Shukla-Varma (SV) modes and the zonal flows in a nonuniform dusty magnetoplasma is considered. By using a two-fluid model and the guiding center particle drifts, a pair of coupled mode equations is obtained. The latter are Fourier analyzed to obtain a nonlinear dispersion relation, which exhibits the excitation of zonal flows by the ponderomotive force of the SV modes. The increment of the parametrically excited zonal flows is presented. The relevance of our investigation to laboratory and space plasmas is discussed

Synchro-betatron resonance excitation in LEP

International Nuclear Information System (INIS)

Myers, S.

1987-01-01

The excitation of synchrotro-betatron resonances due to spurious dispersion and induced transverse deflecting fields at the RF cavities has been simulated for the LEP storage ring. These simulations have been performed for various possible modes of operation. In particular, a scenario has been studied in which LEP is operated at the maximum possible value of the synchrotron tune throughout the acceleration cycle, in an attempt to maximise the threshold intensity at which the Transverse Mode Coupling Instability (TMCI) occurs. This mode of operation necessitates the crossing of synchro-betatron resonances at some points in the acceleration cycle if low order non-linear machine resonances are to be avoided. Simulations have been performed in which the machine tune is swept across these synchro-betratron resonances at a rate given by the bandwidth of the magnet plus power supply circuits of the main quadrupole chain. The effect of longitudinal and transverse wake-fields on the excitation of these resonances has been investigated. These studies indicate that the distortion of the RF potential well caused by the longitudinal wake fields increases the non-linear content of the synchrotron motion and consequently increases significantly the excitation of the higher order synchro-betatron resonances

Localized excitations in a nonlinearly coupled magnetic drift wave-zonal flow system

International Nuclear Information System (INIS)

Shukla, Nitin; Shukla, P.K.

2010-01-01

We consider the amplitude modulation of the magnetic drift wave (MDW) by zonal flows (ZFs) in a nonuniform magnetoplasma. For this purpose, we use the two-fluid model to derive a nonlinear Schroedinger equation for the amplitude modulated MDWs in the presence of the ZF potential, and an evolution equation for the ZF potential which is reinforced by the nonlinear Lorentz force of the MDWs. Our nonlinearly coupled MDW-ZFs system of equations admits stationary solutions in the form of a localized MDW envelope and a shock-like ZF potential profile.

Response moments of dynamic systems under non-Gaussian random excitation by the equivalent non-Gaussian excitation method

International Nuclear Information System (INIS)

Tsuchida, Takahiro; Kimura, Koji

2016-01-01

Equivalent non-Gaussian excitation method is proposed to obtain the response moments up to the 4th order of dynamic systems under non-Gaussian random excitation. The non-Gaussian excitation is prescribed by the probability density and the power spectrum, and is described by an Ito stochastic differential equation. Generally, moment equations for the response, which are derived from the governing equations for the excitation and the system, are not closed due to the nonlinearity of the diffusion coefficient in the equation for the excitation even though the system is linear. In the equivalent non-Gaussian excitation method, the diffusion coefficient is replaced with the equivalent diffusion coefficient approximately to obtain a closed set of the moment equations. The square of the equivalent diffusion coefficient is expressed by a quadratic polynomial. In numerical examples, a linear system subjected to nonGaussian excitations with bimodal and Rayleigh distributions is analyzed by using the present method. The results show that the method yields the variance, skewness and kurtosis of the response with high accuracy for non-Gaussian excitation with the widely different probability densities and bandwidth. The statistical moments of the equivalent non-Gaussian excitation are also investigated to describe the feature of the method. (paper)

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

The challenges of neural mind-reading paradigms

OpenAIRE

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neura...

Nonlinear Time Series Analysis via Neural Networks

Science.gov (United States)

Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

Nonlinear-Optical and Fluorescent Properties of Ag Aqueous Colloid Prepared by Silver Nitrate Reduction

Directory of Open Access Journals (Sweden)

Xiaoqiang Zhang

2010-01-01

Full Text Available The nonlinear-optical properties of metal Ag colloidal solutions, which were prepared by the reduction of silver nitrate, were investigated using Z-scan method. Under picosecond 532 nm excitation, the Ag colloidal solution exhibited negative nonlinear refractive index (n2=−5.17×10−4 cm2/W and reverse saturable absorption coefficient (β=4.32 cm/GW. The data fitting result of optical limiting (OL response of metal Ag colloidal solution indicated that the nonlinear absorption was attributed to two-photon absorption effect at 532 nm. Moreover, the fluorescence emission spectra of Ag colloidal solution were recorded under excitations at both 280 nm and 350 nm. Two fluorescence peaks, 336 nm and 543 nm for 280 nm excitation, while 544 nm and 694 nm for 350 nm excitation, were observed.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

Science.gov (United States)

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

XXIII International Conference on Nonlinear Dynamics of Electronic Systems

CERN Document Server

Stoop, Ruedi; Stramaglia, Sebastiano

2017-01-01

This book collects contributions to the XXIII international conference “Nonlinear dynamics of electronic systems”. Topics range from non-linearity in electronic circuits to synchronisation effects in complex networks to biological systems, neural dynamics and the complex organisation of the brain. Resting on a solid mathematical basis, these investigations address highly interdisciplinary problems in physics, engineering, biology and biochemistry.

Bubble nonlinear dynamics and stimulated scattering process

Science.gov (United States)

Jie, Shi; De-Sen, Yang; Sheng-Guo, Shi; Bo, Hu; Hao-Yang, Zhang; Shi-Yong, Hu

2016-02-01

A complete understanding of the bubble dynamics is deemed necessary in order to achieve their full potential applications in industry and medicine. For this purpose it is first needed to expand our knowledge of a single bubble behavior under different possible conditions including the frequency and pressure variations of the sound field. In addition, stimulated scattering of sound on a bubble is a special effect in sound field, and its characteristics are associated with bubble oscillation mode. A bubble in liquid can be considered as a representative example of nonlinear dynamical system theory with its resonance, and its dynamics characteristics can be described by the Keller-Miksis equation. The nonlinear dynamics of an acoustically excited gas bubble in water is investigated by using theoretical and numerical analysis methods. Our results show its strongly nonlinear behavior with respect to the pressure amplitude and excitation frequency as the control parameters, and give an intuitive insight into stimulated sound scattering on a bubble. It is seen that the stimulated sound scattering is different from common dynamical behaviors, such as bifurcation and chaos, which is the result of the nonlinear resonance of a bubble under the excitation of a high amplitude acoustic sound wave essentially. The numerical analysis results show that the threshold of stimulated sound scattering is smaller than those of bifurcation and chaos in the common condition. Project supported by the Program for Changjiang Scholars and Innovative Research Team in University, China (Grant No. IRT1228) and the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 11204050 and 11204049).

Nonlinear physics of shear Alfvén waves

International Nuclear Information System (INIS)

Zonca, Fulvio; Chen, Liu

2014-01-01

Shear Alfvén waves (SAW) play fundamental roles in thermonuclear plasmas of fusion interest, since they are readily excited by energetic particles in the MeV range as well as by the thermal plasma components. Thus, understanding fluctuation induced transport in burning plasmas requires understanding nonlinear SAW physics. There exist two possible routes to nonlinear SAW physics: (i) wave-wave interactions and the resultant spectral energy transfer; (ii) nonlinear wave-particle interactions of SAW instabilities with energetic particles. Within the first route, it is advantageous to understand and describe nonlinear processes in term of proximity of the system to the Alfvénic state, where wave-wave interactions are minimized due to the cancellation of Reynolds and Maxwell stresses. Here, various wave-wave nonlinear dynamics are elucidated in terms of how they break the Alfvénic state. In particular, we discuss the qualitative and quantitative modification of the SAW parametric decay process due to finite ion compressibility and finite ion Larmor radius. We also show that toroidal geometry plays a crucial role in the nonlinear excitation of zonal structures by Alfvén eigenmodes. Within the second route, the coherent nonlinear dynamics of structures in the energetic particle phase space, by which secular resonant particle transport can occur on meso- and macro-scales, must be addressed and understood. These 'nonlinear equilibria' or 'phase-space zonal structures' dynamically evolve on characteristic (fluctuation induced) turbulent transport time scales, which are generally of the same order of the nonlinear time scale of the underlying fluctuations. In this work, we introduce the general structure of nonlinear Schrödinger equations with complex integro-differential nonlinear terms, which govern these physical processes. To elucidate all these aspects, theoretical analyses are presented together with numerical simulation results

Nonlinear physics of shear Alfvén waves

Science.gov (United States)

Zonca, Fulvio; Chen, Liu

2014-02-01

Shear Alfvén waves (SAW) play fundamental roles in thermonuclear plasmas of fusion interest, since they are readily excited by energetic particles in the MeV range as well as by the thermal plasma components. Thus, understanding fluctuation induced transport in burning plasmas requires understanding nonlinear SAW physics. There exist two possible routes to nonlinear SAW physics: (i) wave-wave interactions and the resultant spectral energy transfer; (ii) nonlinear wave-particle interactions of SAW instabilities with energetic particles. Within the first route, it is advantageous to understand and describe nonlinear processes in term of proximity of the system to the Alfvénic state, where wave-wave interactions are minimized due to the cancellation of Reynolds and Maxwell stresses. Here, various wave-wave nonlinear dynamics are elucidated in terms of how they break the Alfvénic state. In particular, we discuss the qualitative and quantitative modification of the SAW parametric decay process due to finite ion compressibility and finite ion Larmor radius. We also show that toroidal geometry plays a crucial role in the nonlinear excitation of zonal structures by Alfvén eigenmodes. Within the second route, the coherent nonlinear dynamics of structures in the energetic particle phase space, by which secular resonant particle transport can occur on meso- and macro-scales, must be addressed and understood. These "nonlinear equilibria" or "phase-space zonal structures" dynamically evolve on characteristic (fluctuation induced) turbulent transport time scales, which are generally of the same order of the nonlinear time scale of the underlying fluctuations. In this work, we introduce the general structure of nonlinear Schrödinger equations with complex integro-differential nonlinear terms, which govern these physical processes. To elucidate all these aspects, theoretical analyses are presented together with numerical simulation results.

Multi-mode excitation of a clamped–clamped microbeam resonator

KAUST Repository

Younis, Mohammad I.

2015-02-18

We present modeling and simulation of the nonlinear dynamics of a microresonator subjected to two-source electrostatic excitation. The resonator is composed of a clamped–clamped beam excited by a DC voltage load superimposed to two AC voltage loads of different frequencies. One frequency is tuned close to the first natural frequency of the beam and the other is close to the third (second symmetric) natural frequency. A multi-mode Galerkin procedure is applied to extract a reduced-order model, which forms the basis of the numerical simulations. Time history response, Poincare’ sections, Fast Fourier Transforms FFT, and bifurcation diagrams are used to reveal the dynamics of the system. The results indicate complex nonlinear phenomena, which include quasiperiodic motion, torus bifurcations, and modulated chaotic attractors.

Three-mode resonant coupling of collective excitations in a Bose-Einstein condensate

International Nuclear Information System (INIS)

Ma Yongli; Huang, Guoxiang; Hu Bambi

2005-01-01

We make a systematic study of the resonant mode coupling of the collective excitations at zero temperature in a Bose-Einstein condensate (BEC). (i) Based on the Gross-Pitaevskii equation we derive a set of nonlinearly coupled envelope equations for a three-mode resonant interaction (TMRI) by means of a method of multiple scales. (ii) We calculate the coupling matrix elements for the TMRI and show that the divergence appearing in previous studies can be eliminated completely by using a Fetter-like variational approximation for the ground-state wave function of the condensate. (iii) We provide the selection rules in mode-mode interaction processes [including TMRI and second-harmonic generation (SHG)] according to the symmetry of the excitations. (iv) By solving the nonlinearly coupled envelope equations we obtain divergence-free nonlinear amplitudes for the TMRI and SHG processes and show that our theoretical results on the shape oscillations of the condensate agree well with the experimental ones. We suggest also an experiment to check the theoretical prediction of the present study on the TMRI of collective excitations in a BEC

FRF decoupling of nonlinear systems

Science.gov (United States)

Kalaycıoğlu, Taner; Özgüven, H. Nevzat

2018-03-01

Structural decoupling problem, i.e. predicting dynamic behavior of a particular substructure from the knowledge of the dynamics of the coupled structure and the other substructure, has been well investigated for three decades and led to several decoupling methods. In spite of the inherent nonlinearities in a structural system in various forms such as clearances, friction and nonlinear stiffness, all decoupling studies are for linear systems. In this study, decoupling problem for nonlinear systems is addressed for the first time. A method, named as FRF Decoupling Method for Nonlinear Systems (FDM-NS), is proposed for calculating FRFs of a substructure decoupled from a coupled nonlinear structure where nonlinearity can be modeled as a single nonlinear element. Depending on where nonlinear element is, i.e., either in the known or unknown subsystem, or at the connection point, the formulation differs. The method requires relative displacement information between two end points of the nonlinear element, in addition to point and transfer FRFs at some points of the known subsystem. However, it is not necessary to excite the system from the unknown subsystem even when the nonlinear element is in that subsystem. The validation of FDM-NS is demonstrated with two different case studies using nonlinear lumped parameter systems. Finally, a nonlinear experimental test structure is used in order to show the real-life application and accuracy of FDM-NS.

Enhanced Nonlinear Effects in Metamaterials and Plasmonics

Directory of Open Access Journals (Sweden)

C. Argyropoulos

2012-07-01

Full Text Available In this paper we provide an overview of the anomalous and enhanced nonlinear effects available when optical nonlinear materials are combined inside plasmonic waveguide structures. Broad, bistable and all-optical switching responses are exhibited at the cut-off frequency of these waveguides, characterized by reduced Q-factor resonances. These phenomena are due to the large field enhancement obtained inside specific plasmonic gratings, which ensures a significant boosting of the nonlinear operation. Several exciting applications are proposed, which may potentially lead to new optical components and add to the optical nanocircuit paradigm.

Experimental verification of the intrinsic ultrafast delayed nonlinearity of gold

DEFF Research Database (Denmark)

Bache, Morten; Lysenko, Oleg; Olivier, Nicolas

2017-01-01

Surface plasmon polaritons (SPPs) in plasmonic metal waveguides can excite a third-order nonlinear response [1] much akin the well-known self-phase modulation (SPM) and two-photon absorption seen in light propagating in dielectric waveguides. In metals, the nonlinearity mainly arises as a self...

Small systems of Duffing oscillators and the Fermi-Pasta-Ulam-Tsingou system An examination of the possible reasons for the unusual stability of localized nonlinear excitations in these systems

Science.gov (United States)

Kashyap, Rahul; Westley, Alexandra; Sen, Surajit

The Duffing oscillator, a nonlinear oscillator with a potential energy with both quadratic and cubic terms, is known to show highly chaotic solutions in certain regions of its parameter space. Here, we examine the behaviors of small chains of harmonically and anharmonically coupled Duffing oscillators and show that these chains exhibit localized nonlinear excitations (LNEs) similar to the ones seen in the Fermi-Pasta-Ulam-Tsingou (FPUT) system. These LNEs demonstrate properties such as long-time energy localization, high periodicity, and slow energy leaking which rapidly accelerates upon frequency matching with the adjacent particles all of which have been observed in the FPUT system. Furthermore, by examining bifurcation diagrams, we will show that many qualitative properties of this system during the transition from weakly to strongly nonlinear behavior depend directly upon the frequencies associated with the individual Duffing oscillators.

Theoretical analysis of open aperture reflection Z-scan on materials with high-order optical nonlinearities

International Nuclear Information System (INIS)

Petris, Adrian I.; Vlad, Valentin I.

2010-03-01

We present a theoretical analysis of open aperture reflection Z-scan in nonlinear media with third-, fifth-, and higher-order nonlinearities. A general analytical expression for the normalized reflectance when third-, fifth- and higher-order optical nonlinearities are excited is derived and its consequences on RZ-scan in media with high-order nonlinearities are discussed. We show that by performing RZ-scan experiments at different incident intensities it is possible to put in evidence the excitation of different order nonlinearities in the medium. Their contributions to the overall nonlinear response can be discriminated by using formulas derived by us. A RZ-scan numerical simulation using these formulas and data taken from literature, measured by another method for the third-, fifth-, and seventh-order nonlinear refractive indices of As 2 S 3 chalcogenide glass, is performed. (author)

Self-Compensating Excitation of Fluxgate Sensors for Space Magnetometers

DEFF Research Database (Denmark)

Cerman, Alec; Merayo, José M.G.; Brauer, Peter

2008-01-01

The paper presents design and implementation of the new self-compensating excitation circuitry to the new generation of high-precise space vector magnetometers. The application starts with complex study including design of new robust model of the non-linear inductor leading to investigation...... of the most crucial points, continuous by design of the self-compensating excitation unit and concludes with unit complex testing and application to the magnetometer. The application of the self-compensation of the excitation decreases temperature drift of the magnetometer offset caused by the temperature...

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.

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

Directory of Open Access Journals (Sweden)

Guido Gigante

2015-11-01

Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

Stability and nonlinear dynamics of gyrotrons at cyclotron harmonics

International Nuclear Information System (INIS)

Saraph, G.P.; Nusinovich, G.S.; Antonsen, T.M. Jr.; Levush, B.

1992-01-01

Gyrotrons operating at higher harmonics of the cyclotron frequency can overcome the frequency limitations caused by achievable strength of the magnetic field. However, the excitation of modes at the fundamental frequency exhibit a major problem for stable operation of harmonic gyrotron at high power with high efficiency. Therefore the issues of stability of gyrotron operation at the cyclotron harmonics and nonlinear dynamics of mode interaction are of great importance. The results of the authors stability analysis and multimode simulation are presented here. A detailed nonlinear theory of steady state single mode operation at cyclotron harmonics has been presented previously, taking into account beam-wave coupling and nonlinear gain function at cyclotron harmonics. A set of equations describing low gain regime interaction of modes resonant at different cyclotron harmonics was studied before. The multifrequency time-dependent nonlinear analysis presented here is based on previous gyrotron studies and beam-wave interaction at cyclotron harmonics. The authors have determined the parameter space for stable single mode operation at the second harmonic. The nonlinear dynamics of mode evolution and mode interaction for a harmonic gyrotron is presented. A new nonlinear effect in which the parasite at the fundamental harmonic helps excite the operating mode at the second harmonic has been demonstrated

PWR system simulation and parameter estimation with neural networks

Energy Technology Data Exchange (ETDEWEB)

Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

2002-11-01

A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

PWR system simulation and parameter estimation with neural networks

International Nuclear Information System (INIS)

Akkurt, Hatice; Colak, Uener

2002-01-01

A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

Damage detection and locating using tone burst and continuous excitation modulation method

Science.gov (United States)

Li, Zheng; Wang, Zhi; Xiao, Li; Qu, Wenzhong

2014-03-01

Among structural health monitoring techniques, nonlinear ultrasonic spectroscopy methods are found to be effective diagnostic approach to detecting nonlinear damage such as fatigue crack, due to their sensitivity to incipient structural changes. In this paper, a nonlinear ultrasonic modulation method was developed to detect and locate a fatigue crack on an aluminum plate. The method is different with nonlinear wave modulation method which recognizes the modulation of low-frequency vibration and high-frequency ultrasonic wave; it recognizes the modulation of tone burst and high-frequency ultrasonic wave. In the experiment, a Hanning window modulated sinusoidal tone burst and a continuous sinusoidal excitation were simultaneously imposed on the PZT array which was bonded on the surface of an aluminum plate. The modulations of tone burst and continuous sinusoidal excitation was observed in different actuator-sensor paths, indicating the presence and location of fatigue crack. The results of experiments show that the proposed method is capable of detecting and locating the fatigue crack successfully.

Neural network error correction for solving coupled ordinary differential equations

Science.gov (United States)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Nonlinear Modeling and Identification of an Aluminum Honeycomb Panel with Multiple Bolts

Directory of Open Access Journals (Sweden)

Yongpeng Chu

2016-01-01

Full Text Available This paper focuses on the nonlinear dynamics modeling and parameter identification of an Aluminum Honeycomb Panel (AHP with multiple bolted joints. Finite element method using eight-node solid elements is exploited to model the panel and the bolted connection interface as a homogeneous, isotropic plate and as a thin layer of nonlinear elastic-plastic material, respectively. The material properties of a thin layer are defined by a bilinear elastic plastic model, which can describe the energy dissipation and softening phenomena in the bolted joints under nonlinear states. Experimental tests at low and high excitation levels are performed to reveal the dynamic characteristics of the bolted structure. In particular, the linear material parameters of the panel are identified via experimental tests at low excitation levels, whereas the nonlinear material parameters of the thin layer are updated by using the genetic algorithm to minimize the residual error between the measured and the simulation data at a high excitation level. It is demonstrated by comparing the frequency responses of the updated FEM and the experimental system that the thin layer of bilinear elastic-plastic material is very effective for modeling the nonlinear joint interface of the assembled structure with multiple bolts.

Third Conference on nonlinear science and complexity (NSC)

CERN Document Server

Machado, José; Baleanu, Dumitru; Dynamical Systems and Methods

2012-01-01

Nonlinear Systems and Methods For Mechanical, Electrical and Biosystems presents topics observed at the 3rd Conference on Nonlinear Science and Complexity(NSC), focusing on energy transfer and synchronization in hybrid nonlinear systems. The studies focus on fundamental theories and principles,analytical and symbolic approaches, computational techniques in nonlinear physical science and mathematics. Broken into three parts, the text covers:\\ Parametrical excited pendulum, nonlinear dynamics in hybrid systems, dynamical system synchronization and (N+1) body dynamics as well as new views different from the existing results in nonlinear dynamics. Mathematical methods for dynamical systems including conservation laws, dynamical symmetry in nonlinear differential equations and invex energies. Nonlinear phenomena in physical problems such as solutions, complex flows, chemical kinetics, Toda lattices and parallel manipulator. This book is useful to scholars, researchers and advanced technical members of industrial l...

Nonlinear saturation controller for vibration supersession of a nonlinear composite beam

Energy Technology Data Exchange (ETDEWEB)

Hamed, Y. S. [Menofia University, Menouf (Egypt); Amer, Y. A. [Zagazig University, Zagazig (Egypt)

2014-08-15

In this paper, a study for nonlinear saturation controller (NSC) is presented that used to suppress the vibration amplitude of a structural dynamic model simulating nonlinear composite beam at simultaneous sub-harmonic and internal resonance excitation. The absorber exploits the saturation phenomenon that is known to occur in dynamical systems with quadratic non-linearities of the feedback gain and a two-to-one internal resonance. The analytical solution for the system and the nonlinear saturation controller are obtained using method of multiple time scales perturbation up to the second order approximation. All possible resonance cases were extracted at this approximation order and studied numerically. The stability of the system at the worst resonance case (Ω = 2ω{sub s} and ω{sub s} =2ω{sub C}) is investigated using both frequency response equations and phase-plane trajectories. The effects of different parameters on the system and the controller are studied numerically. The effect of some types of controller on the system is investigated numerically. The simulation results are achieved using Matlab and Maple programs.

A seesaw-type approach for enhancing nonlinear energy harvesting

Science.gov (United States)

Deng, Huaxia; Wang, Zhemin; Du, Yu; Zhang, Jin; Ma, Mengchao; Zhong, Xiang

2018-05-01

Harvesting sustainable mechanical energy is the ultimate objective of nonlinear energy harvesters. However, overcoming potential barriers, especially without the use of extra excitations, poses a great challenge for the development of nonlinear generators. In contrast to the existing methods, which typically modify the barrier height or utilize additional excitations, this letter proposes a seesaw-type approach to facilitate escape from potential wells by transfer of internal energy, even under low-intensity excitation. This approach is adopted in the design of a seesaw-type nonlinear piezoelectric energy harvester and the energy transfer process is analyzed by deriving expressions for the energy to reveal the working mechanism. Comparison experiments demonstrate that this approach improves energy harvesting in terms of an increase in the working frequency bandwidth by a factor of 60.14 and an increase in the maximum output voltage by a factor of 5.1. Moreover, the output power is increased by a factor of 51.3, which indicates that this approach significantly improves energy collection efficiency. This seesaw-type approach provides a welcome boost to the development of renewable energy collection methods by improving the efficiency of harvesting of low-intensity ambient mechanical energy.

Selected Problems in Nonlinear Dynamics and Sociophysics

Science.gov (United States)

Westley, Alexandra Renee

This Ph.D. dissertation focuses on a collection of problems on the dynamical behavior of nonlinear many-body systems, drawn from two substantially different areas. First, the dynamical behavior seen in strongly nonlinear lattices such as in the Fermi-Pasta-Ulam-Tsingou (FPUT) system (part I) and second, time evolution behavior of interacting living objects which can be broadly considered as sociophysics systems (part II). The studies on FPUT-like systems will comprise of five chapters, dedicated to the properties of solitary and anti-solitary waves in the system, how localized nonlinear excitations decay and spread throughout these lattices, how two colliding solitary waves can precipitate highly localized and stable excitations, a possible alternative way to view these localized excitations through Duffing oscillators, and finally an exploration of parametric resonance in an FPUT-like lattice. Part II consists of two problems in the context of sociophysics. I use molecular dynamics inspired simulations to study the size and the stability of social groups of chimpanzees (such as those seen in central Africa) and compare the results with existing observations on the stability of chimpanzee societies. Secondly, I use an agent-based model to simulate land battles between an intelligent army and an insurgency when both have access to equally powerful weaponry. The study considers genetic algorithm based adaptive strategies to infer the strategies needed for the intelligent army to win the battles.

Equivalent circuit simulation of HPEM-induced transient responses at nonlinear loads

Directory of Open Access Journals (Sweden)

M. Kotzev

2017-09-01

Full Text Available In this paper the equivalent circuit modeling of a nonlinearly loaded loop antenna and its transient responses to HPEM field excitations are investigated. For the circuit modeling the general strategy to characterize the nonlinearly loaded antenna by a linear and a nonlinear circuit part is pursued. The linear circuit part can be determined by standard methods of antenna theory and numerical field computation. The modeling of the nonlinear circuit part requires realistic circuit models of the nonlinear loads that are given by Schottky diodes. Combining both parts, appropriate circuit models are obtained and analyzed by means of a standard SPICE circuit simulator. It is the main result that in this way full-wave simulation results can be reproduced. Furthermore it is clearly seen that the equivalent circuit modeling offers considerable advantages with respect to computation speed and also leads to improved physical insights regarding the coupling between HPEM field excitation and nonlinearly loaded loop antenna.

Nonlinear laser-plasma interactions

Science.gov (United States)

Kaw, P. K.

2017-12-01

Soon after lasers were invented, there was tremendous curiosity on the nonlinear phenomena which would result in their interaction with a fully ionized plasma. Apart from the basic interest, it was realized that it could be used for the achievement of nuclear fusion in the laboratory. This led us to a paper on the propagation of a laser beam into an inhomogeneous fusion plasma, where it was first demonstrated that light would go up to the critical layer (where the frequency matches the plasma frequency) and get reflected from there with a reflection coefficient of order unity. The reflection coefficient was determined by collisional effects. Since the wave was expected to slow down to near zero group speed at the reflection point, the dominant collision frequency determining the reflection coefficient was the collision frequency at the reflection point. It turned out that the absorption of light was rather small for fusion temperatures. This placed a premium on investigation of nonlinear phenomena which might contribute to the absorption and penetration of the light into high-density plasma. An early investigation showed that electron jitter with respect to ions would be responsible for the excitation of decay instabilities which convert light waves into electrostatic plasma waves and ion waves near the critical frequency. These electrostatic waves would then get absorbed into the plasma even in the collisionless case and lead to plasma heating which is nonlinear. Detailed estimates of this heating were made. Similar nonlinear processes which could lead to stimulated scattering of light in the underdense region (ω >ω _p) were investigated together with a number of other workers. All these nonlinear processes need a critical threshold power for excitation. Another important process which was discovered around the same time had to do with filamentation and trapping of light when certain thresholds were exceeded. All of this work has been extensively verified in

Collapse of solitary excitations in the nonlinear Schrödinger equation with nonlinear damping and white noise

DEFF Research Database (Denmark)

Christiansen, Peter Leth; Gaididei, Yuri Borisovich; Rasmussen, Kim

1996-01-01

in an exponentially decreasing width of the solution in the long-time limit. We also find that a sufficiently large noise variance may cause an initially localized distribution to spread instead of contracting, and that the critical variance necessary to cause dispersion will for small damping be the same......We study the effect of adding noise and nonlinear damping in the two-dimensional nonlinear Schrodinger equation (NLS). Using a collective approach, we find that for initial conditions where total collapse occurs in the unperturbed NLS, the presence of the damping term will instead...

Multi-frequency Defect Selective Imaging via Nonlinear Ultrasound

Science.gov (United States)

Solodov, Igor; Busse, Gerd

The concept of defect-selective ultrasonic nonlinear imaging is based on visualization of strongly nonlinear inclusions in the form of localized cracked defects. For intense excitation, the ultrasonic response of defects is affected by mechanical constraint between their fragments that makes their vibrations extremely nonlinear. The cracked flaws, therefore, efficiently generate multiple new frequencies, which can be used as a nonlinear "tag" to detect and image them. In this paper, the methodologies of nonlinear scanning laser vibrometry (NSLV) and nonlinear air-coupled emission (NACE) are applied for nonlinear imaging of various defects in hi-tech and constructional materials. A broad database obtained demonstrates evident advantages of the nonlinear approach over its linear counterpart. The higher-order nonlinear frequencies provide increase in signal-to-noise ratio and enhance the contrast of imaging. Unlike conventional ultrasonic instruments, the nonlinear approach yields abundant multi-frequency information on defect location. The application of image recognition and processing algorithms is described and shown to improve reliability and quality of ultrasonic imaging.

Performance Analysis of a Hybrid One-Sided Magnetic Exciter Mounted on a Piezoelectric Stack

OpenAIRE

Nandi, A.; Neogy, S.

2010-01-01

The present work proposes a non-contact hybrid exciter especially useful for harmonic excitation of lightly damped structures/rotors. In the proposed exciter an electromagnet is placed on a piezoelectric stack and the extension of the piezoelectric stack is made almost equal to the displacement of the structure using a simple tracking control. This largely eliminates stiffness coupling between the structure/rotor and the exciter and non-linearity in the excitation force due to the vibration o...

Nonlinear Optics of Hexaphenyl Nanofibers

DEFF Research Database (Denmark)

Balzer, Frank; Al-Shamery, Katharina; Neuendorf, Rolf

2003-01-01

The nonlinear optical response of films of needle-shaped para-hexaphenyl nanoaggregates on mica surfaces is investigated. Two-photon luminescence as well as optical second harmonic generation (SHG) are observed following excitation with femtosecond pulses at 770 nm. Polarization dependent...... measurements reveal that the nonlinear optical transition dipole moment is oriented with an angle of 75° with respect to the needles long axes. The absolute value of the macroscopic second-order susceptibility, averaged over a size distribution of p-6P nanoaggregates, is estimated to be of the order of 6...

Multi-modal vibration amplitudes of taut inclined cables due to direct and/or parametric excitation

Science.gov (United States)

Macdonald, J. H. G.

2016-02-01

Cables are often prone to potentially damaging large amplitude vibrations. The dynamic excitation may be from external loading or motion of the cable ends, the latter including direct excitation, normally from components of end motion transverse to the cable, and parametric excitation induced by axial components of end motion causing dynamic tension variations. Geometric nonlinearity can be important, causing stiffening behaviour and nonlinear modal coupling. Previous analyses of the vibrations, often neglecting sag, have generally dealt with direct and parametric excitation separately or have reverted to numerical solutions of the responses. Here a nonlinear cable model is adopted, applicable to taut cables such as on cable-stayed bridges, that allows for cable inclination, small sag (such that the vibration modes are similar to those of a taut string), multiple modes in both planes and end motion and/or external forcing close to any natural frequency. Based on the method of scaling and averaging it is found that, for sinusoidal inputs and positive damping, non-zero steady state responses can only occur in the modes in each plane with natural frequencies close to the excitation frequency and those with natural frequencies close to half this frequency. Analytical solutions, in the form of non-dimensional polynomial equations, are derived for the steady state vibration amplitudes in up to three modes simultaneously: the directly excited mode, the corresponding nonlinearly coupled mode in the orthogonal plane and a parametrically excited mode with half the natural frequency. The stability of the solutions is also identified. The outputs of the equations are consistent with previous results, where available. Example results from the analytical solutions are presented for a typical inclined bridge cable subject to vertical excitation of the lower end, and they are validated by numerical integration of the equations of motion and against some previous experimental

Nonlinear surface elastic modes in crystals

Science.gov (United States)

Gorentsveig, V. I.; Kivshar, Yu. S.; Kosevich, A. M.; Syrkin, E. S.

1990-03-01

The influence of nonlinearity on shear horizontal surface elastic waves in crystals is described on the basis of the effective nonlinear Schrödinger equation. It is shown that the corresponding solutions form a set of surface modes and the simplest mode coincides with the solution proposed by Mozhaev. The higher order modes have internal frequencies caused by the nonlinearity. All these modes decay in the crystal as uoexp(- z/ zo) atz≫ zo- u o-1 ( z is the distance from the crystal surface, uo the wave amplitude at the surface). The creation of the modes from a localized surface excitation has a threshold. The stability of the modes is discussed.

Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Barhen, J.; Cogswell, R.; Protopopescu, V.

1999-07-31

A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

Excitable particles in an optical torque wrench

Science.gov (United States)

Pedaci, Francesco; Huang, Zhuangxiong; van Oene, Maarten; Barland, Stephane; Dekker, Nynke H.

2011-03-01

The optical torque wrench is a laser trapping technique capable of applying and directly measuring torque on microscopic birefringent particles using spin momentum transfer, and has found application in the measurement of static torsional properties of biological molecules such as single DNAs. Motivated by the potential of the optical torque wrench to access the fast rotational dynamics of biological systems, a result of its all-optical manipulation and detection, we focus on the angular dynamics of the trapped birefringent particle, demonstrating its excitability in the vicinity of a critical point. This links the optical torque wrench to nonlinear dynamical systems such as neuronal and cardiovascular tissues, nonlinear optics and chemical reactions, all of which display an excitable binary (`all-or-none') response to input perturbations. On the basis of this dynamical feature, we devise and implement a conceptually new sensing technique capable of detecting single perturbation events with high signal-to-noise ratio and continuously adjustable sensitivity.

Dynamics of Microbeams under Multi-Frequency Excitations

KAUST Repository

Ibrahim, Alwathiqbellah

2017-01-24

This paper presents an investigation of the dynamics of microbeams under multiple harmonic electrostatic excitation frequencies. First, the response of a cantilever microbeam to two alternating current (AC) source excitation is examined. We show by simulations the response of the microbeam at primary resonance (near the fundamental natural frequency) and at secondary resonances (near half, superharmonic, and twice, subharmonic, the fundamental natural frequency). A multimode Galerkin method combined with the Euler-Bernoulli beam equation, accounting for the nonlinear electrostatic force, has been used to develop a reduced order model. The response of the cantilever microbeam to three AC source excitation is also investigated and shown as a promising technique to enhance the bandwidth of resonators. Finally, an experimental study of a clamped-clamped microbeam is conducted, demonstrating the multi-frequency excitation resonances using two, three, and four AC sources.

Dynamics of Microbeams under Multi-Frequency Excitations

KAUST Repository

Ibrahim, Alwathiqbellah; Jaber, Nizar; Chandran, Akhil; Thirupathi, Maloth; Younis, Mohammad I.

2017-01-01

This paper presents an investigation of the dynamics of microbeams under multiple harmonic electrostatic excitation frequencies. First, the response of a cantilever microbeam to two alternating current (AC) source excitation is examined. We show by simulations the response of the microbeam at primary resonance (near the fundamental natural frequency) and at secondary resonances (near half, superharmonic, and twice, subharmonic, the fundamental natural frequency). A multimode Galerkin method combined with the Euler-Bernoulli beam equation, accounting for the nonlinear electrostatic force, has been used to develop a reduced order model. The response of the cantilever microbeam to three AC source excitation is also investigated and shown as a promising technique to enhance the bandwidth of resonators. Finally, an experimental study of a clamped-clamped microbeam is conducted, demonstrating the multi-frequency excitation resonances using two, three, and four AC sources.

Distributed Extreme Learning Machine for Nonlinear Learning over Network

Directory of Open Access Journals (Sweden)

Songyan Huang

2015-02-01

Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

Damage detection in composites using nonlinear ultrasonically modulated thermography

Science.gov (United States)

Malfense Fierro, G.-P.; Dionysopoulos, D.; Meo, M.; Ciampa, F.

2018-03-01

This paper proposes a novel nonlinear ultrasonically stimulated thermography technique for a quick and reliable assessment of material damage in carbon fibre reinforced plastic (CFRP) composite materials. The proposed nondestructive evaluation (NDE) method requires narrow sweep ultrasonic excitation using contact piezoelectric transducers in order to identify dual excitation frequencies associated with the damage resonance. High-amplitude signals and higher harmonic generation are necessary conditions for an accurate identification of these two input frequencies. Dual periodic excitation using high- and low-frequency input signals was then performed in order to generate frictional heating at the crack location that was measured by an infrared (IR) camera. To validate this concept, an impact damaged CFRP composite panel was tested and the experimental results were compared with traditional flash thermography. A laser vibrometer was used to investigate the response of the material with dual frequency excitation. The proposed nonlinear ultrasonically modulated thermography successfully detected barely visible impact damage in CFRP composites. Hence, it can be considered as an alternative to traditional flash thermography and thermosonics by allowing repeatable detection of damage in composites.

Efficient second-harmonic imaging of collagen in histological slides using Bessel beam excitation

Science.gov (United States)

Vuillemin, Nelly; Mahou, Pierre; Débarre, Delphine; Gacoin, Thierry; Tharaux, Pierre-Louis; Schanne-Klein, Marie-Claire; Supatto, Willy; Beaurepaire, Emmanuel

2016-07-01

Second-harmonic generation (SHG) is the most specific label-free indicator of collagen accumulation in widespread pathologies such as fibrosis, and SHG-based measurements hold important potential for biomedical analyses. However, efficient collagen SHG scoring in histological slides is hampered by the limited depth-of-field of usual nonlinear microscopes relying on focused Gaussian beam excitation. In this work we analyze theoretically and experimentally the use of Bessel beam excitation to address this issue. Focused Bessel beams can provide an axially extended excitation volume for nonlinear microscopy while preserving lateral resolution. We show that shaping the focal volume has consequences on signal level and scattering directionality in the case of coherent signals (such as SHG) which significantly differ from the case of incoherent signals (two-photon excited fluorescence, 2PEF). We demonstrate extended-depth SHG-2PEF imaging of fibrotic mouse kidney histological slides. Finally, we show that Bessel beam excitation combined with spatial filtering of the harmonic light in wave vector space can be used to probe collagen accumulation more efficiently than the usual Gaussian excitation scheme. These results open the way to SHG-based histological diagnoses.

MgB_{2} nonlinear properties investigated under localized high rf magnetic field excitation

Directory of Open Access Journals (Sweden)

Tamin Tai

2012-12-01

Full Text Available The high transition temperature and low surface resistance of MgB_{2} attracts interest in its potential application in superconducting radio frequency accelerating cavities. However, compared to traditional Nb cavities, the viability of MgB_{2} at high rf fields is still open to question. Our approach is to study the nonlinear electrodynamics of the material under localized rf magnetic fields. Because of the presence of the small superconducting gap in the π band, the nonlinear response of MgB_{2} at low temperature is potentially complicated compared to a single-gap s-wave superconductor such as Nb. Understanding the mechanisms of nonlinearity coming from the two-band structure of MgB_{2}, as well as extrinsic sources of nonlinearity, is an urgent requirement. A localized and strong rf magnetic field, created by a magnetic write head, is integrated into our nonlinear-Meissner-effect scanning microwave microscope [T. Tai et al., IEEE Trans. Appl. Supercond. 21, 2615 (2011ITASE91051-822310.1109/TASC.2010.2096531]. MgB_{2} films with thickness 50 nm, fabricated by a hybrid physical-chemical vapor deposition technique on dielectric substrates, are measured at a fixed location and show a strongly temperature-dependent third harmonic response. We propose that several possible mechanisms are responsible for this nonlinear response.

A study of anharmonic al and nonlinear behaviours of vibrations of atomic nuclei

International Nuclear Information System (INIS)

Volpe, M.C.

1997-01-01

Double Giant Resonances, vibrational states in which a Giant Resonance is excited on top of another Giant Resonance, have been in the last years the object of many theories and studies. Whereas the measured energies and widths of these states agree with a theoretical predictions, the measured excitation cross sections on the other hand are almost always larger than the calculated ones. The standard theoretical approaches are based both on a harmonic approximation for the collective motion on the nucleus and on its linear response to an external field. In this work the influence of anharmonicities and non-linearities in the external field on the excitation of Double Giant Resonances are studied. First, an oscillator model and an extension of the Lipkin-Meshkow-Glick model are used to study the effects of anharmonicities and non-linearities on the excitation probabilities. The results show that these terms can influence the excitation probability of the second excited state in a significant way. Secondly, these exactly soluble schematic models are used to study some of the approximations made in microscopic calculations based on boson expansion methods and also some aspects on the time-dependent mean field approach. Finally, a microscopic calculation of the Coulomb excitation cross sections of Double Giant Resonances is presented for several nuclei. It is found that, for 208 Pb, the inclusion of anharmonicities and non-linearities and the consideration of many states that play a role in the excitation process give a satisfactory agreement between calculated and observed cross sections. (author)

Response of sliding structures to seismic excitation: bibliographical study

International Nuclear Information System (INIS)

Sarh, K.; Duval, C.

1992-11-01

Calculation of the seismic response of structures on sliding supports involves the dual problem of ''non-linear'' and ''random'' dynamic behaviour. After a review of the non-linearities common in dynamics, slipping is compared with a hysteresis phenomenon. Simple examples are then used to present the Fokker-Planck equation and the equivalent linearization method. Finally, the methods for modification of the excitation spectrum intended for the engineering calculations are recalled. (authors). 21 figs., 23 refs

Nonlinear photoacoustic spectroscopy of hemoglobin

Energy Technology Data Exchange (ETDEWEB)

Danielli, Amos; Maslov, Konstantin; Favazza, Christopher P.; Xia, Jun; Wang, Lihong V., E-mail: LHWANG@WUSTL.EDU [Optical Imaging Laboratory, Department of Biomedical Engineering, Washington University in St. Louis, One Brookings Drive, St. Louis, Missouri 63130 (United States)

2015-05-18

As light intensity increases in photoacoustic imaging, the saturation of optical absorption and the temperature dependence of the thermal expansion coefficient result in a measurable nonlinear dependence of the photoacoustic (PA) signal on the excitation pulse fluence. Here, under controlled conditions, we investigate the intensity-dependent photoacoustic signals from oxygenated and deoxygenated hemoglobin at varied optical wavelengths and molecular concentrations. The wavelength and concentration dependencies of the nonlinear PA spectrum are found to be significantly greater in oxygenated hemoglobin than in deoxygenated hemoglobin. These effects are further influenced by the hemoglobin concentration. These nonlinear phenomena provide insights into applications of photoacoustics, such as measurements of average inter-molecular distances on a nm scale or with a tuned selection of wavelengths, a more accurate quantitative PA tomography.

Nonlinear photoacoustic spectroscopy of hemoglobin.

Science.gov (United States)

Danielli, Amos; Maslov, Konstantin; Favazza, Christopher P; Xia, Jun; Wang, Lihong V

2015-05-18

As light intensity increases in photoacoustic imaging, the saturation of optical absorption and the temperature dependence of the thermal expansion coefficient result in a measurable nonlinear dependence of the photoacoustic (PA) signal on the excitation pulse fluence. Here, under controlled conditions, we investigate the intensity-dependent photoacoustic signals from oxygenated and deoxygenated hemoglobin at varied optical wavelengths and molecular concentrations. The wavelength and concentration dependencies of the nonlinear PA spectrum are found to be significantly greater in oxygenated hemoglobin than in deoxygenated hemoglobin. These effects are further influenced by the hemoglobin concentration. These nonlinear phenomena provide insights into applications of photoacoustics, such as measurements of average inter-molecular distances on a nm scale or with a tuned selection of wavelengths, a more accurate quantitative PA tomography.

Nonlinear photoacoustic spectroscopy of hemoglobin

International Nuclear Information System (INIS)

Danielli, Amos; Maslov, Konstantin; Favazza, Christopher P.; Xia, Jun; Wang, Lihong V.

2015-01-01

As light intensity increases in photoacoustic imaging, the saturation of optical absorption and the temperature dependence of the thermal expansion coefficient result in a measurable nonlinear dependence of the photoacoustic (PA) signal on the excitation pulse fluence. Here, under controlled conditions, we investigate the intensity-dependent photoacoustic signals from oxygenated and deoxygenated hemoglobin at varied optical wavelengths and molecular concentrations. The wavelength and concentration dependencies of the nonlinear PA spectrum are found to be significantly greater in oxygenated hemoglobin than in deoxygenated hemoglobin. These effects are further influenced by the hemoglobin concentration. These nonlinear phenomena provide insights into applications of photoacoustics, such as measurements of average inter-molecular distances on a nm scale or with a tuned selection of wavelengths, a more accurate quantitative PA tomography

An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

Science.gov (United States)

Matsubara, Takashi; Torikai, Hiroyuki

2016-04-01

Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

Targeted energy transfers and passive acoustic wave redirection in a two-dimensional granular network under periodic excitation

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yijing, E-mail: yzhng123@illinois.edu; Moore, Keegan J.; Vakakis, Alexander F. [Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); McFarland, D. Michael [Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States)

2015-12-21

We study passive pulse redirection and nonlinear targeted energy transfer in a granular network composed of two semi-infinite, ordered homogeneous granular chains mounted on linear elastic foundations and coupled by weak linear stiffnesses. Periodic excitation in the form of repetitive half-sine pulses is applied to one of the chains, designated as the “excited chain,” whereas the other chain is initially at rest and is regarded as the “absorbing chain.” We show that passive pulse redirection and targeted energy transfer from the excited to the absorbing chain can be achieved by macro-scale realization of the spatial analog of the Landau-Zener quantum tunneling effect. This is realized by finite stratification of the elastic foundation of the excited chain and depends on the system parameters (e.g., the percentage of stratification) and on the parameters of the periodic excitation. Utilizing empirical mode decomposition and numerical Hilbert transforms, we detect the existence of two distinct nonlinear phenomena in the periodically forced network; namely, (i) energy localization in the absorbing chain due to sustained 1:1 resonance capture leading to irreversible pulse redirection from the excited chain, and (ii) continuous energy exchanges in the form of nonlinear beats between the two chains in the absence of resonance capture. Our results extend previous findings of transient passive energy redirection in impulsively excited granular networks and demonstrate that steady state passive pulse redirection in these networks can be robustly achieved under periodic excitation.

A dynamic load estimation method for nonlinear structures with unscented Kalman filter

Science.gov (United States)

Guo, L. N.; Ding, Y.; Wang, Z.; Xu, G. S.; Wu, B.

2018-02-01

A force estimation method is proposed for hysteretic nonlinear structures. The equation of motion for the nonlinear structure is represented in state space and the state variable is augmented by the unknown the time history of external force. Unscented Kalman filter (UKF) is improved for the force identification in state space considering the ill-condition characteristic in the computation of square roots for the covariance matrix. The proposed method is firstly validated by a numerical simulation study of a 3-storey nonlinear hysteretic frame excited by periodic force. Each storey is supposed to follow a nonlinear hysteretic model. The external force is identified and the measurement noise is considered in this case. Then a case of a seismically isolated building subjected to earthquake excitation and impact force is studied. The isolation layer performs nonlinearly during the earthquake excitation. Impact force between the seismically isolated structure and the retaining wall is estimated with the proposed method. Uncertainties such as measurement noise, model error in storey stiffness and unexpected environmental disturbances are considered. A real-time substructure testing of an isolated structure is conducted to verify the proposed method. In the experimental study, the linear main structure is taken as numerical substructure while the one of the isolations with additional mass is taken as the nonlinear physical substructure. The force applied by the actuator on the physical substructure is identified and compared with the measured value from the force transducer. The method proposed in this paper is also validated by shaking table test of a seismically isolated steel frame. The acceleration of the ground motion as the unknowns is identified by the proposed method. Results from both numerical simulation and experimental studies indicate that the UKF based force identification method can be used to identify external excitations effectively for the nonlinear

Suppression and nonlinear excitation of parasitic modes in second harmonic gyrotrons operating in a very high order mode

International Nuclear Information System (INIS)

Nusinovich, Gregory S.; Pu, Ruifeng; Granatstein, Victor L.

2015-01-01

In recent years, there was an active development of high-power, sub-terahertz (sub-THz) gyrotrons for numerous applications. For example, a 0.67 THz gyrotron delivering more than 200 kW with about 20% efficiency was developed. This record high efficiency was achieved because the gyrotron operated in a high-order TE 31,8 -mode with the power of ohmic losses less than 10% of the power of outgoing radiation. That gyrotron operated at the fundamental cyclotron resonance, and a high magnetic field of about 27 T was created by a pulse solenoid. For numerous applications, it is beneficial to use gyrotrons at cyclotron harmonics which can operate in available cryomagnets with fields not exceeding 15 T. However, typically, the gyrotron operation at harmonics faces severe competition from parasitic modes at the fundamental resonance. In the present paper, we consider a similar 0.67 THz gyrotron designed for operation in the same TE 31,8 -mode, but at the second harmonic. We focus on two nonlinear effects typical for interaction between the fundamental and second harmonic modes, viz., the mode suppression and the nonlinear excitation of the mode at the fundamental harmonic by the second harmonic oscillations. Our study includes both the analytical theory and numerical simulations performed with the self-consistent code MAGY. The simulations show that stable second harmonic operation in the TE 31,8 mode is possible with only modest sacrifice of efficiency and power

Nonlinear optics principles and applications

CERN Document Server

Li, Chunfei

2017-01-01

This book reflects the latest advances in nonlinear optics. Besides the simple, strict mathematical deduction, it also discusses the experimental verification and possible future applications, such as the all-optical switches. It consistently uses the practical unit system throughout. It employs simple physical images, such as "light waves" and "photons" to systematically explain the main principles of nonlinear optical effects. It uses the first-order nonlinear wave equation in frequency domain under the condition of “slowly varying amplitude approximation" and the classical model of the interaction between the light and electric dipole. At the same time, it also uses the rate equations based on the energy-level transition of particle systems excited by photons and the energy and momentum conservation principles to explain the nonlinear optical phenomenon. The book is intended for researchers, engineers and graduate students in the field of the optics, optoelectronics, fiber communication, information tech...

Nonlinear analysis for dual-frequency concurrent energy harvesting

Science.gov (United States)

Yan, Zhimiao; Lei, Hong; Tan, Ting; Sun, Weipeng; Huang, Wenhu

2018-05-01

The dual-frequency responses of the hybrid energy harvester undergoing the base excitation and galloping were analyzed numerically. In this work, an approximate dual-frequency analytical method is proposed for the nonlinear analysis of such a system. To obtain the approximate analytical solutions of the full coupled distributed-parameter model, the forcing interactions is first neglected. Then, the electromechanical decoupled governing equation is developed using the equivalent structure method. The hybrid mechanical response is finally separated to be the self-excited and forced responses for deriving the analytical solutions, which are confirmed by the numerical simulations of the full coupled model. The forced response has great impacts on the self-excited response. The boundary of Hopf bifurcation is analytically determined by the onset wind speed to galloping, which is linearly increased by the electrical damping. Quenching phenomenon appears when the increasing base excitation suppresses the galloping. The theoretical quenching boundary depends on the forced mode velocity. The quenching region increases with the base acceleration and electrical damping, but decreases with the wind speed. Superior to the base-excitation-alone case, the existence of the aerodynamic force protects the hybrid energy harvester at resonance from damages caused by the excessive large displacement. From the view of the harvested power, the hybrid system surpasses the base-excitation-alone system or the galloping-alone system. This study advances our knowledge on intrinsic nonlinear dynamics of the dual-frequency energy harvesting system by taking advantage of the analytical solutions.

Controllable excitation of higher-order rogue waves in nonautonomous systems with both varying linear and harmonic external potentials

Science.gov (United States)

Jia, Heping; Yang, Rongcao; Tian, Jinping; Zhang, Wenmei

2018-05-01

The nonautonomous nonlinear Schrödinger (NLS) equation with both varying linear and harmonic external potentials is investigated and the semirational rogue wave (RW) solution is presented by similarity transformation. Based on the solution, the interactions between Peregrine soliton and breathers, and the controllability of the semirational RWs in periodic distribution and exponential decreasing nonautonomous systems with both linear and harmonic potentials are studied. It is found that the harmonic potential only influences the constraint condition of the semirational solution, the linear potential is related to the trajectory of the semirational RWs, while dispersion and nonlinearity determine the excitation position of the higher-order RWs. The higher-order RWs can be partly, completely and biperiodically excited in periodic distribution system and the diverse excited patterns can be generated for different parameter relations in exponential decreasing system. The results reveal that the excitation of the higher-order RWs can be controlled in the nonautonomous system by choosing dispersion, nonlinearity and external potentials.

Decentralized neural control application to robotics

CERN Document Server

Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

2017-01-01

This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

Oscillating solitons in nonlinear optics

Indian Academy of Sciences (India)

The study of solitons in those physical systems reveals some exciting .... With the following power series expansions for g(z,t) and f(z,t): g(z,t) = εg1(z,t) + ... If nonlinearity γ (z) is also taken as a function in figure 1b, the periodic and oscillation.

The Challenges of Neural Mind-reading Paradigms

Directory of Open Access Journals (Sweden)

Oscar eVilarroya

2013-06-01

Full Text Available Neural mind-reading studies, based on multivariate pattern analysis (MVPA methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: a the BOLD signal is a marker of neural activity; b the BOLD pattern identified by a MVPA is a neurally sound pattern; c the MVPA’s feature space is a good mapping of the neural representation of a stimulus, and d the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

The challenges of neural mind-reading paradigms.

Science.gov (United States)

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Nonlinear excitation fluorescence microscopy: source considerations for biological applications

Science.gov (United States)

Wokosin, David L.

2008-02-01

Ultra-short-pulse solid-state laser sources have improved contrast within fluorescence imaging and also opened new windows of investigation in biological imaging applications. Additionally, the pulsed illumination enables harmonic scattering microscopy which yields intrinsic structure, symmetry and contrast from viable embryos, cells and tissues. Numerous human diseases are being investigated by the combination of (more) intact dynamic tissue imaging of cellular function with gene-targeted specificity and electrophysiology context. The major limitation to more widespread use of multi-photon microscopy has been the complete system cost and added complexity above and beyond commercial camera and confocal systems. The current status of all-solid-state ultrafast lasers as excitation sources will be reviewed since these lasers offer tremendous potential for affordable, reliable, "turnkey" multiphoton imaging systems. This effort highlights the single box laser systems currently commercially available, with defined suggestions for the ranges for individual laser parameters as derived from a biological and fluorophore limited perspective. The standard two-photon dose is defined by 800nm, 10mW, 200fs, and 80Mhz - at the sample plane for tissue culture cells, i.e. after the full scanning microscope system. Selected application-derived excitation wavelengths are well represented by 700nm, 780nm, ~830nm, ~960nm, 1050nm, and 1250nm. Many of the one-box lasers have fixed or very limited excitation wavelengths available, so the lasers will be lumped near 780nm, 800nm, 900nm, 1050nm, and 1250nm. The following laser parameter ranges are discussed: average power from 200mW to 2W, pulse duration from 70fs to 700fs, pulse repetition rate from 20MHz to 200MHz, with the laser output linearly polarized with an extinction ratio at least 100:1.

Nonlinear Dynamics of Electrostatically Actuated MEMS Arches

KAUST Repository

Al Hennawi, Qais M.

2015-01-01

In this thesis, we present theoretical and experimental investigation into the nonlinear statics and dynamics of clamped-clamped in-plane MEMS arches when excited by an electrostatic force. Theoretically, we first solve the equation of motion using

Nonlinear absorption dynamics using field-induced surface hopping: zinc porphyrin in water.

Science.gov (United States)

Röhr, Merle I S; Petersen, Jens; Wohlgemuth, Matthias; Bonačić-Koutecký, Vlasta; Mitrić, Roland

2013-05-10

We wish to present the application of our field-induced surface-hopping (FISH) method to simulate nonlinear absorption dynamics induced by strong nonresonant laser fields. We provide a systematic comparison of the FISH approach with exact quantum dynamics simulations on a multistate model system and demonstrate that FISH allows for accurate simulations of nonlinear excitation processes including multiphoton electronic transitions. In particular, two different approaches for simulating two-photon transitions are compared. The first approach is essentially exact and involves the solution of the time-dependent Schrödinger equation in an extended manifold of excited states, while in the second one only transiently populated nonessential states are replaced by an effective quadratic coupling term, and dynamics is performed in a considerably smaller manifold of states. We illustrate the applicability of our method to complex molecular systems by simulating the linear and nonlinear laser-driven dynamics in zinc (Zn) porphyrin in the gas phase and in water. For this purpose, the FISH approach is connected with the quantum mechanical-molecular mechanical approach (QM/MM) which is generally applicable to large classes of complex systems. Our findings that multiphoton absorption and dynamics increase the population of higher excited states of Zn porphyrin in the nonlinear regime, in particular in solution, provides a means for manipulating excited-state properties, such as transient absorption dynamics and electronic relaxation. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

Science.gov (United States)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Application of General Regression Neural Network to the Prediction of LOD Change

Science.gov (United States)

Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao

2012-01-01

Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.

Non-Linear Back-propagation: Doing Back-Propagation withoutDerivatives of the Activation Function

DEFF Research Database (Denmark)

Hertz, John; Krogh, Anders Stærmose; Lautrup, Benny

1997-01-01

The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the non-linear back...

Adaptive Dynamic Surface Control for Generator Excitation Control System

Directory of Open Access Journals (Sweden)

Zhang Xiu-yu

2014-01-01

Full Text Available For the generator excitation control system which is equipped with static var compensator (SVC and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1 the transformation of the excitation generator model to the linear systems is omitted; (2 the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3 the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4 the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme.

Nonlinear dynamics of a coherent polariton-biexciton system

International Nuclear Information System (INIS)

Nguyen Trung Dan; Vo Tinh

1994-08-01

The nonlinear dynamics of a coherent interacting polariton-biexciton system in optically excited semiconductors is investigated. We consider the case when two macroscopically coherent modes - a lower branch polariton and a biexciton existing simultaneously in a direct-gap semiconductor. The conditions for exhibiting optical bistability in stationary regime are obtained. Numerical simulation for the nonlinear dynamics equations of the system is also carried out. (author). 16 refs, 4 figs

Nonlinearly coupled dynamics of irregularities in the equatorial electrojet

Energy Technology Data Exchange (ETDEWEB)

Atul, J.K., E-mail: jkatulphysics@gmail.com [Department of Physics, College of Commerce under Magadh University, Patna 800020 (India); Sarkar, S. [FCIPT, Institute for Plasma Research, Gandhinagar 382428 (India); Singh, S.K. [Department of Physics, College of Commerce under Magadh University, Patna 800020 (India)

2016-04-01

Kinetic wave description is used to study the nonlinear influence of background Farley Buneman (FB) modes on the Gradient Drift (GD) modes in the equatorial electrojet ionosphere. The dominant nonlinearity is mediated through the electron flux term in the governing fluid equation which further invokes a turbulent current into the system. Electron dynamics reveals the modification in electron collision frequency and inhomogeneity scale length. It is seen that the propagation and growth rate of GD modes get modified by the background FB modes. Also, a new quasimode gets excited through the quadratic dispersion relation. Physical significance of coupled dynamics between the participating modes is also discussed. - Highlights: • Nonlinear influence of Farley Buneman mode on the Gradient drift mode, is investigated. • Electron collision frequency and density gradient scale length get modified. • A new quasimode gets excited due to the competition between these modes. • It seems to be important for modelling of Equatorial Electrojet current system.

Nonlinearly coupled dynamics of irregularities in the equatorial electrojet

International Nuclear Information System (INIS)

Atul, J.K.; Sarkar, S.; Singh, S.K.

2016-01-01

Kinetic wave description is used to study the nonlinear influence of background Farley Buneman (FB) modes on the Gradient Drift (GD) modes in the equatorial electrojet ionosphere. The dominant nonlinearity is mediated through the electron flux term in the governing fluid equation which further invokes a turbulent current into the system. Electron dynamics reveals the modification in electron collision frequency and inhomogeneity scale length. It is seen that the propagation and growth rate of GD modes get modified by the background FB modes. Also, a new quasimode gets excited through the quadratic dispersion relation. Physical significance of coupled dynamics between the participating modes is also discussed. - Highlights: • Nonlinear influence of Farley Buneman mode on the Gradient drift mode, is investigated. • Electron collision frequency and density gradient scale length get modified. • A new quasimode gets excited due to the competition between these modes. • It seems to be important for modelling of Equatorial Electrojet current system.

Ultracompact all-optical logic gates based on nonlinear plasmonic nanocavities

Directory of Open Access Journals (Sweden)

Yang Xiaoyu

2016-09-01

Full Text Available In this study, nanoscale integrated all-optical XNOR, XOR, and NAND logic gates were realized based on all-optical tunable on-chip plasmon-induced transparency in plasmonic circuits. A large nonlinear enhancement was achieved with an organic composite cover layer based on the resonant excitation-enhancing nonlinearity effect, slow light effect, and field confinement effect provided by the plasmonic nanocavity mode, which ensured a low excitation power of 200 μW that is three orders of magnitude lower than the values in previous reports. A feature size below 600 nm was achieved, which is a one order of magnitude lower compared to previous reports. The contrast ratio between the output logic states “1” and “0” reached 29 dB, which is among the highest values reported to date. Our results not only provide an on-chip platform for the study of nonlinear and quantum optics but also open up the possibility for the realization of nanophotonic processing chips based on nonlinear plasmonics.

Synaptic E-I Balance Underlies Efficient Neural Coding.

Science.gov (United States)

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

Science.gov (United States)

Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bispectral analysis of nonlinear compressional waves in a two-dimensional dusty plasma crystal

International Nuclear Information System (INIS)

Nosenko, V.; Goree, J.; Skiff, F.

2006-01-01

Bispectral analysis was used to study the nonlinear interaction of compressional waves in a two-dimensional strongly coupled dusty plasma. A monolayer of highly charged polymer microspheres was suspended in a plasma sheath. The microspheres interacted with a Yukawa potential and formed a triangular lattice. Two sinusoidal pump waves with different frequencies were excited in the lattice by pushing the particles with modulated Ar + laser beams. Coherent nonlinear interaction of the pump waves was shown to be the mechanism of generating waves at the sum, difference, and other combination frequencies. However, coherent nonlinear interaction was ruled out for certain combination frequencies, in particular, for the difference frequency below an excitation-power threshold, as predicted by theory

Determining the magnetically nonlinear characteristics of a three phase core-type power transformer

International Nuclear Information System (INIS)

Dolinar, Matjaz; Stumberger, Gorazd; Polajzer, Bostjan; Dolinar, Drago

2006-01-01

This paper presents nonlinear iron core model of a three-phase, three-limb power transformer which is given by the current-dependant characteristics of flux linkages. The magnetically nonlinear characteristics are determined by controlled magnetic excitation of all three limbs which allows to take into account the variable magnetic-cross couplings between different coils placed on limbs, caused by saturation. The corresponding partial derivatives of measured flux linkage characteristics are used in the transformer circuit model as a magnetically nonlinear iron core model in order to analyze the behaviour of a nonsymmetrically excited transformer. Numerical results using transformer model with the determined iron core model agree very well with the measured results

Evolving RBF neural networks for adaptive soft-sensor design.

Science.gov (United States)

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Nonlinear response and avalanche behavior in metallic glasses

Science.gov (United States)

Riechers, B.; Samwer, K.

2017-08-01

The response to different stress amplitudes at temperatures below the glass transition temperature is analyzed by mechanical oscillatory excitation of Pd40Ni40P20 metallic glass samples in single cantilever bending geometry. While low amplitude oscillatory excitations are commonly used in mechanical spectroscopy to probe the relaxation spectrum, in this work the response to comparably high amplitudes is investigated. The strain response of the material is well below the critical yield stress even for highest stress amplitudes, implying the expectation of a linear relation between stress and strain according to Hooke's Law. However, a deviation from the linear behavior is evident, which is analyzed in terms of temperature dependence and influence of the applied stress amplitude by two different approaches of evaluation. The nonlinear approach is based on a nonlinear expansion of the stress-strain-relation, assuming an intrinsic nonlinear character of the shear or elastic modulus. The degree of nonlinearity is extracted by a period-by-period Fourier-analysis and connected to nonlinear coefficients, describing the intensity of nonlinearity at the fundamental and higher harmonic frequencies. The characteristic timescale to adapt to a significant change in stress amplitude in terms of a recovery timescale to a steady state value is connected to the structural relaxation time of the material, suggesting a connection between the observed nonlinearity and primary relaxation processes. The second approach of evaluation is termed the incremental analysis and relates the observed response behavior to avalanches, which occur due to the activation and correlation of local microstructural rearrangements. These rearrangements are connected with shear transformation zones and correspond to localized plastic events, which are superimposed on the linear response behavior of the material.

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation.

Science.gov (United States)

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-06-17

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc. , it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

NeuroMEMS: Neural Probe Microtechnologies

Directory of Open Access Journals (Sweden)

Sam Musallam

2008-10-01

Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

The effect of excitation intensity variation and silver nanoparticle codoping on nonlinear optical properties of mixed tellurite and zinc oxide glass doped with Nd2O3 studied through ultrafast z-scan spectroscopy

Science.gov (United States)

Moreira, L.; Falci, R. F.; Darabian, H.; Anjos, V.; Bell, M. J. V.; Kassab, L. R. P.; Bordon, C. D. S.; Doualan, J. L.; Camy, P.; Moncorgé, R.

2018-05-01

The research on Nd3+ doped new solid-state laser hosts with specific thermo-mechanical and optical properties is very active. Nd3+ doped tellurite glasses are suitable for these applications. They have high linear and nonlinear refraction index, wide transmittance range. The TeO2-ZnO (TZO) glass considered in the present work combines all those features and the nonlinear optical properties can be used for the development of Kerr-lens mode-locked sub picosecond lasers. Recently the laser performance of Nd3+ doped TZO glass and was reported and laser slope efficiency of 21% was observed. We investigate how the intensity variation and the silver nanoparticles codoping affects the nonlinear optical properties of Nd3+ doped TZO glasses. Intensity dependent nonlinear refraction indices coefficients at 750, 800 and 850 nm were observed. The nonlinear optical features were obtained through ultrafast single beam z-scan technique with excitations at 750, 800 and 850 nm and are up to two orders of magnitude higher than those reported in the literature.

Laboratory Observations of Self-Excited Dust Acoustic Shocks

Science.gov (United States)

Heinrich, J.; Kim, S.-H.; Merlino, R. L.

2009-09-01

Repeated, self-excited dust acoustic shock waves (DASWs) have been observed in a dc glow discharge dusty plasma using high-speed video imaging. Two major observations are reported: (1) The self-steepening of a nonlinear dust acoustic wave (DAW) into a saw-tooth wave with sharp gradient in dust density, very similar to those found in numerical solutions of the fully nonlinear fluid equations for a nondispersive DAW [B. Eliasson and P. K. Shukla, Phys. Rev. E 69, 067401 (2004)], and (2) the collision and confluence of two DASWs.

Control of beam halo-chaos using neural network self-adaptation method

International Nuclear Information System (INIS)

Fang Jinqing; Huang Guoxian; Luo Xiaoshu

2004-11-01

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

Applying neural networks to optimize instrumentation performance

Energy Technology Data Exchange (ETDEWEB)

Start, S.E.; Peters, G.G.

1995-06-01

Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate.

Applying neural networks to optimize instrumentation performance

International Nuclear Information System (INIS)

Start, S.E.; Peters, G.G.

1995-01-01

Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate

Nonlinear analysis of piezoelectric nanocomposite energy harvesting plates

International Nuclear Information System (INIS)

Rafiee, M; He, X Q; Liew, K M

2014-01-01

This paper investigates the nonlinear analysis of energy harvesting from piezoelectric functionally graded carbon nanotube reinforced composite plates under combined thermal and mechanical loadings. The excitation, which derives from harmonically varying mechanical in-plane loading, results in parametric excitation. The governing equations of the piezoelectric functionally graded carbon nanotube reinforced composite plates are derived based on classical plate theory and von Kármán geometric nonlinearity. The material properties of the nanocomposite plate are assumed to be graded in the thickness direction. The single-walled carbon nanotubes (SWCNTs) are assumed to be aligned, straight and have a uniform layout. The linear buckling and vibration behavior of the nanocomposite plates is obtained in the first step. Then, Galerkin’s method is employed to derive the nonlinear governing equations of the problem with cubic nonlinearities associated with mid-plane stretching. Periodic solutions are determined by using the Poincaré–Lindstedt perturbation scheme with movable simply supported boundary conditions. The effects of temperature change, the volume fraction and the distribution pattern of the SWCNTs on the parametric resonance, in particular the amplitude of vibration and the average harvested power of the smart functionally graded carbon nanotube reinforced composite plates, are investigated through a detailed parametric study. (paper)

Nonlinear Characterization of Half and Full Wavelength Power Ultrasonic Devices

Science.gov (United States)

Mathieson, Andrew; Cerisola, Niccolò; Cardoni, Andrea

It is well known that power ultrasonic devices whilst driven under elevated excitation levels exhibit nonlinear behaviors. If no attempt is made to understand and subsequently control these behaviors, these devices can exhibit poor performance or even suffer premature failure. This paper presents an experimental method for the dynamic characterization of a commercial ultrasonic transducer for bone cutting applications (Piezosurgery® Device) operated together with a variety of rod horns that are tuned to operate in a longitudinal mode of vibration. Near resonance responses, excited via a burst sine sweep method were used to identify nonlinear responses exhibited by the devices, while experimental modal analysis was performed to identify the modal parameters of the longitudinal modes of vibration of the assemblies between 0-80 kHz. This study tries to provide an understanding of the effects that geometry and material choices may have on the nonlinear behavior of a tuned device.

Identification of the nonlinear excitation force acting on a bowed string using the dynamical responses at remote locations