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Sample records for nonlinear excitable neural

  1. Nonlinear excitations in biomolecules

    International Nuclear Information System (INIS)

    Peyrard, M.

    1995-01-01

    The aim of the workshop entitled ''Nonlinear Excitations in Biomolecules'' is to attempt to bridge the gap between the physicists and biologists communities which is mainly due to language and cultural barriers. The progress of nonlinear science in the last few decades which have shown that the combination of nonlinearity, which characterize most biological phenomena, and cooperative effects in a system having a large number of degrees of freedom, can give rise to coherent excitations with remarkable properties. New concepts, such as solitons nd nonlinear energy localisation have become familiar to physicists and applied mathematicians. It is thus tempting to make an analogy between these coherent excitations and the exceptional stability of some biological processes, such as for instance DNA transcription, which require the coordination of many events in the ever changing environment of a cell. Physicists are now invoking nonlinear excitations to describe and explain many bio-molecular processes while biologists often doubt that the seemingly infinite variety of phenomena that they are attempting to classify can be reduced to such simple concepts. A large part of the meeting is devoted to tutorial lectures rather than to latest research results. The book provides a pedagogical introduction to the two topics forming the backbone of the meeting: the theory of nonlinear excitations and solitons, and their application in biology; and the structure and function of biomolecules, as well as energy and charge transport in biophysics. In order to emphasize the link between physics and biology, the volume is not divided along these two topics but according to biological subjects. Each chapter starts with a short introduction attempting to help the reader to find his way among the contributions and point out the connection between them. 23 lectures over the 32 presented have been selected and refers to quantum properties of macro-molecules. (J.S.)

  2. Stabilization of nonlinear excitations by disorder

    DEFF Research Database (Denmark)

    Christiansen, Peter Leth; Gaididei, Yuri Borisovich; Johansson, M.

    1998-01-01

    Using analytical and numerical techniques we analyze the static and dynamical properties of solitonlike excitations in the presence of parametric disorder in the one-dimensional nonlinear Schrodinger equation with a homogeneous power nonlinearity. Both the continuum and the discrete problem...... are investigated. We find that otherwise unstable excitations can be stabilized by the presence of disorder in the continuum problem. For the very narrow excitations of the discrete problem we find that the disorder has no effect on the averaged behavior. Finally, we show that the disorder can be applied to induce...... a high degree of controllability of the spatial extent of the stable excitations in the continuum system....

  3. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  4. 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...

  5. Nonlinear stability of spin-flip excitations

    International Nuclear Information System (INIS)

    Arunasalam, V.

    1975-01-01

    A rather complete discussion of the nonlinear electrodynamic behavior of a negative-temperature spin system is presented. The method presented here is based on a coupled set of master equations, one describing the time evolution of the photon (i.e., the spin-flip excitation) distribution function and the other describing the time evolution of the particle distribution function. It is found that the initially unstable (i.e., growing) spin-flip excitations grow to such a large amplitude that their nonlinear reaction on the particle distribution function becomes important. It is then shown that the initially totally inverted two-level spin system evolves rapidly (through this nonlinear photon-particle coupling) towards a quasilinear steady state where the populations of the spin-up and the spin-down states are equal to each other. Explicit expressions for the time taken to reach this quasilinear steady state and the energy in the spin-flip excitations at this state are also presented

  6. 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

  7. Breatherlike excitations in discrete lattices with noise and nonlinear damping

    DEFF Research Database (Denmark)

    Christiansen, Peter Leth; Gaididei, Yuri B.; Johansson, Magnus

    1997-01-01

    We discuss the stability of highly localized, ''breatherlike,'' excitations in discrete nonlinear lattices under the influence of thermal fluctuations. The particular model considered is the discrete nonlinear Schrodinger equation in the regime of high nonlinearity, where temperature effects...

  8. Feedforward Nonlinear Control Using Neural Gas Network

    OpenAIRE

    Machón-González, Iván; López-García, Hilario

    2017-01-01

    Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the ...

  9. 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...

  10. 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.

  11. Feedforward Nonlinear Control Using Neural Gas Network

    Directory of Open Access Journals (Sweden)

    Iván Machón-González

    2017-01-01

    Full Text Available Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.

  12. 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

  13. 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

  14. Non-Linear Excitation of Ion Acoustic Waves

    DEFF Research Database (Denmark)

    Michelsen, Poul; Hirsfield, J. L.

    1974-01-01

    The excitation of ion acoustic waves by nonlinear coupling of two transverse magnetic waves generated in a microwave cavity was investigated. Measurements of the wave amplitude showed good agreement with calculations based on the Vlasov equation.......The excitation of ion acoustic waves by nonlinear coupling of two transverse magnetic waves generated in a microwave cavity was investigated. Measurements of the wave amplitude showed good agreement with calculations based on the Vlasov equation....

  15. Non-linear excitation of gravitational radiation antennae

    International Nuclear Information System (INIS)

    Blair, D.G.

    1982-01-01

    A mechanism of non-linear excitation is proposed to explain observed excess noise in gravitational radiation antennae, driven by low frequency vibration. The mechanism is analogous to the excitation of a violin string by low frequency bowing. Numerical estimates for Weber bars suspended by cables are in good agreement with observations. (Auth.)

  16. Nonlinear excitations in two-dimensional molecular structures with impurities

    DEFF Research Database (Denmark)

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

    1995-01-01

    We study the nonlinear dynamics of electronic excitations interacting with acoustic phonons in two-dimensional molecular structures with impurities. We show that the problem is reduced to the nonlinear Schrodinger equation with a varying coefficient. The latter represents the influence...... of the impurity. Transforming the equation to the noninertial frame of reference coupled with the center of mass we investigate the soliton behavior in the close vicinity of the impurity. With the help of the lens transformation we show that the soliton width is governed by an Ermakov-Pinney equation. We also...... excitations. Analytical results are in good agreement with numerical simulations of the nonlinear Schrodinger equation....

  17. Design and development of a parametrically excited nonlinear energy harvester

    International Nuclear Information System (INIS)

    Yildirim, Tanju; Ghayesh, Mergen H.; Li, Weihua; Alici, Gursel

    2016-01-01

    Highlights: • A parametrically broadband energy harvester was fabricated. • Strong softening-type nonlinear behaviour was observed. • Experiments were conducted showing the large bandwidth of the device. - Abstract: An energy harvester has been designed, fabricated and tested based on the nonlinear dynamical response of a parametrically excited clamped-clamped beam with a central point-mass; magnets have been used as the central point-mass which pass through a coil when parametrically excited. Experiments have been conducted for the energy harvester when the system is excited (i) harmonically near the primary resonance; (ii) harmonically near the principal parametric resonance; (iii) by means of a non-smooth periodic excitation. An electrodynamic shaker was used to parametrically excite the system and the corresponding displacement of the magnet and output voltages of the coil were measured. It has been shown that the system displays linear behaviour at the primary resonance; however, at the principal parametric resonance, the motion characteristic of the magnet substantially changed displaying a strong softening-type nonlinearity. Theoretical simulations have also been conducted in order to verify the experimental results; the comparison between theory and experiment were within very good agreement of each other. The energy harvester developed in this paper is capable of harvesting energy close to the primary resonance as well as the principal parametric resonance; the frequency-band has been broadened significantly mainly due to the nonlinear effects as well as the parametric excitation.

  18. 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.

  19. 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.

  20. Excitation power quantities in phase resonance testing of nonlinear systems with phase-locked-loop excitation

    Science.gov (United States)

    Peter, Simon; Leine, Remco I.

    2017-11-01

    Phase resonance testing is one method for the experimental extraction of nonlinear normal modes. This paper proposes a novel method for nonlinear phase resonance testing. Firstly, the issue of appropriate excitation is approached on the basis of excitation power considerations. Therefore, power quantities known from nonlinear systems theory in electrical engineering are transferred to nonlinear structural dynamics applications. A new power-based nonlinear mode indicator function is derived, which is generally applicable, reliable and easy to implement in experiments. Secondly, the tuning of the excitation phase is automated by the use of a Phase-Locked-Loop controller. This method provides a very user-friendly and fast way for obtaining the backbone curve. Furthermore, the method allows to exploit specific advantages of phase control such as the robustness for lightly damped systems and the stabilization of unstable branches of the frequency response. The reduced tuning time for the excitation makes the commonly used free-decay measurements for the extraction of backbone curves unnecessary. Instead, steady-state measurements for every point of the curve are obtained. In conjunction with the new mode indicator function, the correlation of every measured point with the associated nonlinear normal mode of the underlying conservative system can be evaluated. Moreover, it is shown that the analysis of the excitation power helps to locate sources of inaccuracies in the force appropriation process. The method is illustrated by a numerical example and its functionality in experiments is demonstrated on a benchmark beam structure.

  1. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  2. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  3. 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.

  4. 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

  5. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  6. 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.)

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. Adaptive nonlinear control using input normalized neural networks

    International Nuclear Information System (INIS)

    Leeghim, Henzeh; Seo, In Ho; Bang, Hyo Choong

    2008-01-01

    An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small

  15. 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

  16. 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.

  17. 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.

  18. 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....

  19. 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.

  20. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

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

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...

  1. 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.

  2. Gamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure

    Directory of Open Access Journals (Sweden)

    Dimitris Pinotsis

    2014-05-01

    Full Text Available This paper shows how gamma oscillations can be combined with neural population models and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical excitability and microstructure. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. Neural field models are used to evaluate model evidence and obtain parameter estimates using invasive and non-invasive gamma recordings. Our overview comprises two parts: in the first part, we use neural fields to simulate neural activity and distinguish the effects of post synaptic filtering on predicted responses in terms of synaptic rate constants that correspond to different timescales and distinct neurotransmitters. We focus on model predictions of conductance and convolution based field models and show that these can yield spectral responses that are sensitive to biophysical properties of local cortical circuits like synaptic kinetics and filtering; we also consider two different mechanisms for this filtering: a nonlinear mechanism involving specific conductances and a linear convolution of afferent firing rates producing post synaptic potentials. In the second part of this paper, we use neural fields quantitatively—to fit empirical data recorded during visual stimulation. We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception, while in the second study, we exploited high density electrocorticographic (ECoG data to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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....

  8. 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

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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

  14. 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).

  15. 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...

  16. 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

  17. 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.

  18. 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.

  19. Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States); Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

    1995-12-01

    This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Soliton excitations in a class of nonlinear field theory models

    International Nuclear Information System (INIS)

    Makhan'kov, V.G.; Fedyanin, V.K.

    1985-01-01

    Investigation results of nonlinear models of the field theory with a lagrangian are described. The theory includes models both with zero stable vacuum epsilon=1 and with condensate epsilon=-1 (of disturbed symmetry). Conditions of existence of particle-like solutions (PLS), stability of these solutions are investigated. Soliton dynamics is studied. PLS formfactors are calculated. Statistical mechanics of solitons is built and their dynamic structure factors are calculated

  5. 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)

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. Interpretation of the nonlinear mode excitation in the ITER gyrotron

    International Nuclear Information System (INIS)

    Nusinovich, G. S.; Sinitsyn, O. V.

    2007-01-01

    This study was motivated by an interesting physical effect observed in experiments with a 1 MW, 170 GHz, continuous-wave gyrotron developed at the Japan Atomic Energy Agency for plasma heating and current drive in ITER [see, e.g., Fusion Eng. Des. 55, issues 2-3 (2001)]. In these experiments, the gyrotron switching from a parasitic mode to the operating one was observed with the increase in external magnetic field in the region of hard self-excitation of the operating mode where it cannot be excited from the noise level in the absence of other modes. Below, the theory describing this effect is developed. The switching mechanism caused by merging and disappearance of two (one stable and another unstable) equilibrium states with nonzero amplitudes of both modes is proposed. It is found that the present theory can correctly interpret experimental results qualitatively, but the lack of experimental data does not let the authors carry out some simulations more adequate to experimental conditions

  12. Nonlinear beat excitation of low frequency wave in degenerate plasmas

    Science.gov (United States)

    Mir, Zahid; Shahid, M.; Jamil, M.; Rasheed, A.; Shahbaz, A.

    2018-03-01

    The beat phenomenon due to the coupling of two signals at slightly different frequencies that generates the low frequency signal is studied. The linear dispersive properties of the pump and sideband are analyzed. The modified nonlinear dispersion relation through the field coupling of linear modes against the beat frequency is derived in the homogeneous quantum dusty magnetoplasmas. The dispersion relation is used to derive the modified growth rate of three wave parametric instability. Moreover, significant quantum effects of electrons through the exchange-correlation potential, the Bohm potential, and the Fermi pressure evolved in macroscopic three wave interaction are presented. The analytical results are interpreted graphically describing the significance of the work. The applications of this study are pointed out at the end of introduction.

  13. 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.

  14. 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)

  15. 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.

  16. On nonlinear excitation of voids in dusty plasmas

    International Nuclear Information System (INIS)

    Nebbat, E.; Annou, R.; Bharuthram, R.

    2007-01-01

    The void, which is a dust-free region inside the dust cloud in the plasma, results from a balance of the electrostatic force and the ion-drag force on a dust particulate that has numerous forms, some of which are based on models whereas others are driven from first principles. To explain the generation of voids, K. Avinash, A. Bhattacharjee, and S. Hu [Phys. Rev. Lett. 90, 075001 (2003)] proposed a time-dependent nonlinear model that describes the void as a result of an instability. We augment this model by incorporating the grain drift and reintroducing the velocity convective term as well as by replacing the modeled ion-drag force by a more accurate one. The analysis is conducted in a spherical configuration. It is revealed that the void formation is a threshold phenomenon, i.e., it depends on the grain size. Furthermore, the void possesses a sharp boundary beyond which the dust density decreases and may present a corrugated aspect. For big size grains, the use of both ion-drag forces leads to voids of the same dimension, though for grains of small sizes, the Avinash force drives voids of a higher dimension. The model shows good agreement with the experiment

  17. 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.

  18. 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

  19. Nonlinear control strategy based on using a shape-tunable neural controller

    Energy Technology Data Exchange (ETDEWEB)

    Chen, C.; Peng, S. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of chemical Engineering; Chang, W. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of Automatic Control

    1997-08-01

    In this paper, a nonlinear control strategy based on using a shape-tunable neural network is developed for adaptive control of nonlinear processes. Based on the steepest descent method, a learning algorithm that enables the neural controller to possess the ability of automatic controller output range adjustment is derived. The novel feature of automatic output range adjustment provides the neural controller more flexibility and capability, and therefore the scaling procedure, which is usually unavoidable for the conventional fixed-shape neural controllers, becomes unnecessary. The advantages and effectiveness of the proposed nonlinear control strategy are demonstrated through the challenge problem of controlling an open-loop unstable nonlinear continuous stirred tank reactor (CSTR). 14 refs., 11 figs.

  20. 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....

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

    Directory of Open Access Journals (Sweden)

    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.

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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)

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. 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

  20. 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.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  3. 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

  4. 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.

  5. Identification of Nonlinear Oscillatory Activity Embedded in Broadband Neural Signals

    Czech Academy of Sciences Publication Activity Database

    Vejmelka, Martin; Paluš, Milan; Šušmáková, K.

    2010-01-01

    Roč. 20, č. 2 (2010), s. 117-128 ISSN 0129-0657 R&D Projects: GA MŠk 7E08027 EU Projects: European Commission(XE) 200728 - BRAINSYNC Institutional research plan: CEZ:AV0Z10300504 Keywords : nonlinear dynamical systems * oscillations * random processes * time series analysis * EEG Subject RIV: FH - Neurology Impact factor: 4.237, year: 2010

  6. 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...

  7. 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.

  8. 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.

  9. 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 ...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

    Directory of Open Access Journals (Sweden)

    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.

  19. 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.

  20. 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.

  1. 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.

  2. 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)

  3. 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....

  4. 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.

  5. 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

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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)

  15. Nonlinear rolling of a biased ship in a regular beam wave under external and parametric excitations

    Energy Technology Data Exchange (ETDEWEB)

    El-Bassiouny, A.F. [Mathematics Dept., Benha Univ., Benha (Egypt)

    2007-10-15

    We consider a nonlinear oscillator simultaneously excited by external and parametric functions. The oscillator has a bias parameter that breaks the symmetry of the motion. The example that we use to illustrate the problem is the rolling oscillation of a biased ship in longitudinal waves, but many mechanical systems display similar features. The analysis took into consideration linear, quadratic, cubic, quintic, and seven terms in the polynomial expansion of the relative roll angle. The damping moment consists of the linear term associated with radiation and viscous damping and a cubic term due to frictional resistance and eddies behind bilge keels and hard bilge corners. Two methods (the averaging and the multiple time scales) are used to investigate the first-order approximate analytical solution. The modulation equations of the amplitudes and phases are obtained. These equations are used to obtain the stationary state. The stability of the proposed solution is determined applying Liapunov's first method. Effects of different parameters on the system behaviour are investigated numerically. Results are presented graphically and discussed. The results obtained by two methods are in excellent agreement. (orig.)

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  9. 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

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. 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...

  15. 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

  16. 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.

  17. 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.

  18. 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...

  19. 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.

  20. Nonlinear vibrations of thin arbitrarily laminated composite plates subjected to harmonic excitations using DKT elements

    Science.gov (United States)

    Chiang, C. K.; Xue, David Y.; Mei, Chuh

    1993-04-01

    A finite element formulation is presented for determining the large-amplitude free and steady-state forced vibration response of arbitrarily laminated anisotropic composite thin plates using the Discrete Kirchhoff Theory (DKT) triangular elements. The nonlinear stiffness and harmonic force matrices of an arbitrarily laminated composite triangular plate element are developed for nonlinear free and forced vibration analyses. The linearized updated-mode method with nonlinear time function approximation is employed for the solution of the system nonlinear eigenvalue equations. The amplitude-frequency relations for convergence with gridwork refinement, triangular plates, different boundary conditions, lamination angles, number of plies, and uniform versus concentrated loads are presented.

  1. Nonlinear localized excitations in magnets with a weak exchange interaction as a soliton problem

    International Nuclear Information System (INIS)

    Gvozdikova, M.V.; Kovalev, A.S.

    1998-01-01

    The spin dynamics of soliton-like localized excitations in a discrete ferromagnet chain with an easy axis anisotropy and a weak exchange interaction is studied. The connection of these excitations with longwave magnetic solitons is discussed. The localized excitation frequency dependence on exchange interaction is found for a fixed number of spin deviation. It is shown that this dependence modifies essentially when the exchange interaction becomes comparable with an anisotropy value

  2. 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.

  3. 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.

  4. 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....

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. The method of varying amplitudes for solving (non)linear problems involving strong parametric excitation

    DEFF Research Database (Denmark)

    Sorokin, Vladislav; Thomsen, Jon Juel

    2015-01-01

    Parametrically excited systems appear in many fields of science and technology, intrinsically or imposed purposefully; e.g. spatially periodic structures represent an important class of such systems [4]. When the parametric excitation can be considered weak, classical asymptotic methods like...... the method of averaging [2] or multiple scales [6] can be applied. However, with many practically important applications this simplification is inadequate, e.g. with spatially periodic structures it restricts the possibility to affect their effective dynamic properties by a structural parameter modulation...... of considerable magnitude. Approximate methods based on Floquet theory [4] for analyzing problems involving parametric excitation, e.g. the classical Hill’s method of infinite determinants [3,4], can be employed also in cases of strong excitation; however, with Floquet theory being applicable only for linear...

  14. 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

  15. 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...

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. Nonlinear Response of Vibrational Conveyers with Nonideal Vibration Exciter: Superharmonic and Subharmonic Resonance

    Directory of Open Access Journals (Sweden)

    H. Bayıroğlu

    2012-01-01

    Full Text Available Vibrational conveyers with a centrifugal vibration exciter transmit their load based on the jumping method. Common unbalanced-mass driver oscillates the trough. The motion is strictly related to the vibrational parameters. The transition over resonance of a vibratory system, excited by rotating unbalances, is important in terms of the maximum vibrational amplitude produced and the power demand on the drive for the crossover. The mechanical system is driven by the DC motor. In this study, the working ranges of oscillating shaking conveyers with nonideal vibration exciter have been analyzed analytically for superharmonic and subharmonic resonances by the method of multiple scales and numerically. The analytical results obtained in this study agree well with the numerical results.

  5. Dynamic Response of Non-Linear Inelsatic Systems to Poisson-Driven Stochastic Excitations

    DEFF Research Database (Denmark)

    Nielsen, Søren R. K.; Iwankiewicz, R.

    of an equivalent linearization techni que and substituting the non-analytical non-linearity in the original system by the cubic form in the pertinent state variables. The response moments are evaluated for the equivalent systems with the help of a generalized Ito's differential rule. The analytical results...

  6. Solitary excitations in discrete two-dimensional nonlinear Schrodinger models with dispersive dipole-dipole interactions

    DEFF Research Database (Denmark)

    Christiansen, Peter Leth; Gaididei, Yuri Borisovich; Johansson, M.

    1998-01-01

    The dynamics of discrete two-dimensional nonlinear Schrodinger models with long-range dispersive interactions is investigated. In particular, we focus on the cases where the dispersion arises from a dipole-dipole interaction, assuming the dipole moments at each lattice site to be aligned either...

  7. Non-linear Springing Excitation Due to a Bidirectional Wave Field

    DEFF Research Database (Denmark)

    Vidic-Perunovic, Jelena; Jensen, Jørgen Juncher

    2005-01-01

    Significant springing vibrations in ships have recently been measured in a large ocean-going bulk carrier. So far calculations using various linear and non-linear hydrodynamic procedures have not been able to predict the measured responses. In the present paper it is shown that the springing...

  8. Localized excitations in discrete nonlinear Schrodinger systems: Effects of nonlocal dispersive interactions and noise

    DEFF Research Database (Denmark)

    Rasmussen, Kim; Christiansen, Peter Leth; Johansson, Magnus

    1998-01-01

    A one-dimensional discrete nonlinear Schrodinger (DNLS) model with the power dependence, r(-s) on the distance r, of dispersive interactions is proposed. The stationary states of the system are studied both analytically and numerically. Two kinds of trial functions, exp-like and sech-like are exp...

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Dynamic stability of a vertically excited non-linear continuous system

    Czech Academy of Sciences Publication Activity Database

    Náprstek, Jiří; Fischer, Cyril

    2015-01-01

    Roč. 155, July (2015), s. 106-114 ISSN 0045-7949 R&D Projects: GA ČR(CZ) GA15-01035S Institutional support: RVO:68378297 Keywords : non-linear systems * auto-parametric systems * semi-trivial solution * dynamic stability * system recovery * post- critical response Subject RIV: JM - Building Engineering Impact factor: 2.425, year: 2015 http://www.sciencedirect.com/science/article/pii/S0045794915000024

  15. 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.

  16. 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...

  17. 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.

  18. On One Means of Hard Excitation of Oscillations in Nonlinear Flutter Systems

    Directory of Open Access Journals (Sweden)

    S. D. Glyzin

    2014-01-01

    Full Text Available Considered are so-called finite-dimensional flutter systems, i.e. systems of ordinary differential equations, arising from Galerkin approximations of certain boundary value problems of aeroelasticity theory as well as from a number of radiophysics applications. We study small oscillations of these equations in case of 1 : 3 resonance. By combining analytical and numerical methods, it is concluded that the mentioned resonance can cause a hard excitation of oscillations. Namely, for flutter systems shown is the possibility of coexistence, along with the stable zero state, of stable invariant tori of arbitrary finite dimension as well as chaotic attractors.

  19. Nonlinear response to the multiple sine wave excitation of a softening--hardening system

    International Nuclear Information System (INIS)

    Koplik, B.; Subudhi, M.; Curreri, J.

    1979-01-01

    In studying the earthquake response of the HTGR core, it was observed that the system can display softening--hardening characteristics. This is of great consequence in evaluating the structural safety aspects of the core. In order to obtain a better understanding of the governing parameters, an investigation was undertaken with a single-degree-of-freedom system having a softening--hardening spring characteristic and excited by multiple sine waves. A parametric study varying the input amplitudes and the spring characteristic was performed. Transients were introduced into the system, and the jump phenomena between the lower softening characteristics to the higher hardening curve was studied

  20. Nonlinear electrostatic excitations in magnetized dense plasmas with nonrelativistic and ultra-relativistic degenerate electrons

    International Nuclear Information System (INIS)

    Mahmood, S.; Sadiq, Safeer; Haque, Q.

    2013-01-01

    Linear and nonlinear electrostatic waves in magnetized dense electron-ion plasmas are studied with nonrelativistic and ultra-relativistic degenerate and singly, doubly charged helium (He + , He ++ ) and hydrogen (H + ) ions, respectively. The dispersion relation of electrostatic waves in magnetized dense plasmas is obtained under both the energy limits of degenerate electrons. Using reductive perturbation method, the Zakharov-Kuznetsov equation for nonlinear propagation of electrostatic solitons in magnetized dense plasmas is derived for both nonrelativistic and ultra-relativistic degenerate electrons. It is found that variations in plasma density, magnetic field intensity, different mass, and charge number of ions play significant role in the formation of electrostatic solitons in magnetized dense plasmas. The numerical plots are also presented for illustration using the parameters of dense astrophysical plasma situations such as white dwarfs and neutron stars exist in the literature. The present investigation is important for understanding the electrostatic waves propagation in the outer periphery of compact stars which mostly consists of hydrogen and helium ions with degenerate electrons in dense magnetized plasmas

  1. Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2018-01-01

    In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  2. 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.

  3. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  4. 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

  5. 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.

  6. Bifurcation of Safe Basins and Chaos in Nonlinear Vibroimpact Oscillator under Harmonic and Bounded Noise Excitations

    Directory of Open Access Journals (Sweden)

    Rong Haiwu

    2014-01-01

    Full Text Available The erosion of the safe basins and chaotic motions of a nonlinear vibroimpact oscillator under both harmonic and bounded random noise is studied. Using the Melnikov method, the system’s Melnikov integral is computed and the parametric threshold for chaotic motions is obtained. Using the Monte-Carlo and Runge-Kutta methods, the erosion of the safe basins is also discussed. The sudden change in the character of the stochastic safe basins when the bifurcation parameter of the system passes through a critical value may be defined as an alternative stochastic bifurcation. It is founded that random noise may destroy the integrity of the safe basins, bring forward the occurrence of the stochastic bifurcation, and make the parametric threshold for motions vary in a larger region, hence making the system become more unsafely and chaotic motions may occur more easily.

  7. Anharmonic longitudinal motion of bases and dynamics of nonlinear excitation in DNA.

    Science.gov (United States)

    Di Garbo, Angelo

    2016-01-01

    The dynamics of the transcription bubble in DNA is studied by using a nonlinear model in which torsional and longitudinal conformations of the biomolecule are coupled. In the absence of forcing and dissipation the torsional dynamics is described by a perturbed kink of the Sine-Gordon DNA model, while the longitudinal conformational energy propagate as phonons. It was found that for random initial conditions of the longitudinal conformational field the presence of the kink promotes the creation of phonons propagating along the chain axis. Moreover, the presence of forcing, describing the active role of RNA polymerase, determines in agreement to the experimental data a modulation of the velocity of the transcription bubble. Lastly, it was shown that the presence of dissipation impacts the dynamic of the phonon by reducing the amplitude of the corresponding conformational field. On the contrary, dissipation and forcing modulate the velocity of the transcription bubble alone.

  8. 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

  9. A Phenomenological Study on Inelastic Torsion Caused By Nonlinear Behavior Changes during Earthquake Excitations

    Directory of Open Access Journals (Sweden)

    Seyed Bahram Beheshti-Aval

    2014-12-01

    Full Text Available Torsion of many symmetric structures, which were designed based on the seismic codes, is due to their asymmetricity induced during inelastic behavior. Although the structure was designed symmetrically assuming elastic based criteria, different factors such as material inconsistency in structure, construction details discrepancy and construction errors may result in asymmetric behavior in inelastic deformation range. So far, these considerations have been rarely contemplated in previous published investigations and should be studied regarding the importance of irregularity in increase of seismic demand of structures in the inelastic range. In this paper, as the first step, the asymmetry and irregularity in plan due to non-similar inelastic characteristics with respect to axis passing through center of gravity as well as the effect and importance of each irregularity factors are studied by changing the excitation properties applying to one-storey one-bay steel structures. This simplified structure is chosen due to studying and illustrating the absolute effect of this kind of irregularity in which higher mode effect is eliminated. The results show that the behavior of a structure with inelastic asymmetry is completely different from the structure with elastic asymmetry. As for inelastic asymmetry structure, although the translational and rotational oscillations before yielding were uncouple, these DOFs after yielding become coupled until reaching the terminal rotation point (rotation reaches a constant value and then become uncoupled, i.e., again oscillated symmetrically. This behavior is different from the structures with elastic asymmetricity, in which the translational and rotational movements being coupled during all the excitation time. This effect has not been recognized in previews studies on inelastic behavior of initially elastic symmetry buildings. The study of these behaviors aids the designer to choose the appropriate rehabilitation

  10. 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.

  11. Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders.

    Science.gov (United States)

    O'Donnell, Cian; Gonçalves, J Tiago; Portera-Cailliau, Carlos; Sejnowski, Terrence J

    2017-10-11

    A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca 2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits.

  12. 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

  13. 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

  14. 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

  15. Nonlinear Vibration Analysis for a Jeffcott Rotor with Seal and Air-Film Bearing Excitations

    Directory of Open Access Journals (Sweden)

    Yuefang Wang

    2010-01-01

    Full Text Available The nonlinear coupling vibration and bifurcation of a high-speed centrifugal compressor with a labyrinth seal and two air-film journal bearings are presented in this paper. The rotary shaft and disk are modeled as a rigid Jeffcott rotor. Muszynska's model is used to express the seal force with multiple parameters. For air-film journal bearings, the model proposed by Zhang et al. is adopted to express unsteady bearing forces. The Runge-Kutta method is used to numerically determine the vibration responses of the disk center and the bearings. Bifurcation diagrams for transverse motion of the rotor are presented with parameters of rotation speed and pressure drop of the seal. Multiple subharmonic, periodic, and quasiperiodic motions are presented with two seal-pressure drops. The bifurcation characteristics show inherent interactions between forces of the air-film bearings and the seal, presenting more complicated rotor dynamics than the one with either of the forces alone. Bifurcation diagrams are obtained with parameters of pressure drop and seal length determined for the sake of operation safety.

  16. 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.

  17. Nonlinear plasma waves excitation by intense ion beams in background plasma

    International Nuclear Information System (INIS)

    Kaganovich, Igor D.; Startsev, Edward A.; Davidson, Ronald C.

    2004-01-01

    Plasma neutralization of an intense ion pulse is of interest for many applications, including plasma lenses, heavy ion fusion, cosmic ray propagation, etc. An analytical electron fluid model has been developed to describe the plasma response to a propagating ion beam. The model predicts very good charge neutralization during quasi-steady-state propagation, provided the beam pulse duration τ b is much longer than the electron plasma period 2π/ω p , where ω p =(4πe 2 n p /m) 1/2 is the electron plasma frequency, and n p is the background plasma density. In the opposite limit, the beam pulse excites large-amplitude plasma waves. If the beam density is larger than the background plasma density, the plasma waves break. Theoretical predictions are compared with the results of calculations utilizing a particle-in-cell (PIC) code. The cold electron fluid results agree well with the PIC simulations for ion beam propagation through a background plasma. The reduced fluid description derived in this paper can provide an important benchmark for numerical codes and yield scaling relations for different beam and plasma parameters. The visualization of numerical simulation data shows complex collective phenomena during beam entry and exit from the plasma

  18. Nonlinear Plasma Waves Excitation by Intense Ion Beams in Background Plasma

    International Nuclear Information System (INIS)

    Kaganovich, Igor D.; Startsev, Edward A.; Davidson, Ronald C.

    2004-01-01

    Plasma neutralization of an intense ion pulse is of interest for many applications, including plasma lenses, heavy ion fusion, cosmic ray propagation, etc. An analytical electron fluid model has been developed to describe the plasma response to a propagating ion beam. The model predicts very good charge neutralization during quasi-steady-state propagation, provided the beam pulse duration τ b is much longer than the electron plasma period 2π/ω p , where ω p = (4πe 2 n p /m) 1/2 is the electron plasma frequency and n p is the background plasma density. In the opposite limit, the beam pulse excites large-amplitude plasma waves. If the beam density is larger than the background plasma density, the plasma waves break. Theoretical predictions are compared with the results of calculations utilizing a particle-in-cell (PIC) code. The cold electron fluid results agree well with the PIC simulations for ion beam propagation through a background plasma. The reduced fluid description derived in this paper can provide an important benchmark for numerical codes and yield scaling relations for different beam and plasma parameters. The visualization of numerical simulation data shows complex collective phenomena during beam entry and exit from the plasma

  19. Stability, diffusion and interactions of nonlinear excitations in a many body system

    Science.gov (United States)

    Coste, Christophe; Jean, Michel Saint; Dessup, Tommy

    2017-04-01

    When repelling particles are confined in a quasi-one-dimensional trap by a transverse potential, a configurational phase transition happens. All particles are aligned along the trap axis at large confinement, but below a critical transverse confinement they adopt a staggered row configuration (zigzag phase). This zigzag transition is a subcritical pitchfork bifurcation in extended systems and in systems with cyclic boundary conditions in the longitudinal direction. Among many evidences, phase coexistence is exhibited by localized nonlinear patterns made of a zigzag phase embedded in otherwise aligned particles. We give the normal form at the bifurcation and we show that these patterns can be described as solitary wave envelopes that we call bubbles. They are stable in a large temperature range and can diffuse as quasi-particles, with a diffusion coefficient that may be deduced from the normal form. The potential energy of a bubble is found to be lower than that of the homogeneous bifurcated phase, which explains their stability. We observe also metastable states, that are pairs of solitary wave envelopes which spontaneously evolve toward a stable single bubble. We evidence a strong effect of the discreteness of the underlying particles system and introduce the concept of topological frustration of a bubble pair. A configuration is frustrated when the particles between the two bubbles are not organized in a modulated staggered row. For a nonfrustrated (NF) bubble pair configuration, the bubbles interaction is attractive so that the bubbles come closer and eventually merge as a single bubble. In contrast, the bubbles interaction is found to be repulsive for a frustrated (F) configuration. We describe a model of interacting solitary wave that provides all qualitative characteristics of the interaction force: it is attractive for NF-systems, repulsive for F-systems, and decreases exponentially with the bubbles distance.

  20. Nonlinear saturation of wave packets excited by low-energy electron horseshoe distributions.

    Science.gov (United States)

    Krafft, C; Volokitin, A

    2013-05-01

    Horseshoe distributions are shell-like particle distributions that can arise in space and laboratory plasmas when particle beams propagate into increasing magnetic fields. The present paper studies the stability and the dynamics of wave packets interacting resonantly with electrons presenting low-energy horseshoe or shell-type velocity distributions in a magnetized plasma. The linear instability growth rates are determined as a function of the ratio of the plasma to the cyclotron frequencies, of the velocity and the opening angle of the horseshoe, and of the relative thickness of the shell. The nonlinear stage of the instability is investigated numerically using a symplectic code based on a three-dimensional Hamiltonian model. Simulation results show that the dynamics of the system is mainly governed by wave-particle interactions at Landau and normal cyclotron resonances and that the high-order normal cyclotron resonances play an essential role. Specific features of the dynamics of particles interacting simultaneously with two or more waves at resonances of different natures and orders are discussed, showing that such complex processes determine the main characteristics of the wave spectrum's evolution. Simulations with wave packets presenting quasicontinuous spectra provide a full picture of the relaxation of the horseshoe distribution, revealing two main phases of the evolution: an initial stage of wave energy growth, characterized by a fast filling of the shell, and a second phase of slow damping of the wave energy, accompanied by final adjustments of the electron distribution. The influence of the density inhomogeneity along the horseshoe on the wave-particle dynamics is also discussed.

  1. 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

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. Identification of the nonlinear excitation force acting on a bowed string using the dynamical responses at remote locations

    International Nuclear Information System (INIS)

    Debut, V.; Antunes, J.; Delaune, X.

    2010-01-01

    For achieving realistic numerical simulations of bowed string instruments, based on physical modeling, a good understanding of the actual friction interaction phenomena is of great importance. Most work published in the field including our own has assumed that bow/string frictional forces behave according to the classical Coulomb stick-slip model, with an empirical velocity-dependent sliding friction coefficient. Indeed, the basic self-excited string motions (such as the Helmholtz regime) are well captured using such friction model. However, recent work has shown that the tribological behavior of the bow/string rosin interface is rather complex, therefore the basic velocity-dependent Coulomb model may be an over-simplistic representation of the friction force. More specifically, it was suggested that a more accurate model of the interaction force can be achieved by coupling the system dynamical equations with a thermal model which encapsulates the complex interface phenomena. In spite of the interesting work performed by Askenfelt, a direct measurement of the actual dynamical friction forces without disturbing the string motion is quite difficult. Therefore, in this work we develop a modal-based identification technique making use of inverse methods and optimization techniques, which enables the identification of the interface force, as well as the string self-excited motion, from the dynamical reactions measured at the string end supports. The method gives convincing results using simulated data originated from nonlinear computations of a bowed string. Furthermore, in cases where the force identifications are very sensitive to errors in the transfer function modal parameters, we suggest a method to improve the modal frequencies used for the identifications. Preliminary experimental results obtained using a basic bowing device, by which the string is excited with the stick of the bow, are then presented. Our identifications, from the two dynamical string reactions

  7. 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.

  8. 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.

  9. 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 %.

  10. 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).

  11. 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.

  12. Contributions to muscle force and EMG by combined neural excitation and electrical stimulation

    Science.gov (United States)

    Crago, Patrick E.; Makowski, Nathaniel S.; Cole, Natalie M.

    2014-10-01

    Objective. Stimulation of muscle for research or clinical interventions is often superimposed on ongoing physiological activity without a quantitative understanding of the impact of the stimulation on the net muscle activity and the physiological response. Experimental studies show that total force during stimulation is less than the sum of the isolated voluntary and stimulated forces, but the occlusion mechanism is not understood. Approach. We develop a model of efferent motor activity elicited by superimposing stimulation during a physiologically activated contraction. The model combines action potential interactions due to collision block, source resetting, and refractory periods with previously published models of physiological motor unit recruitment, rate modulation, force production, and EMG generation in human first dorsal interosseous muscle to investigate the mechanisms and effectiveness of stimulation on the net muscle force and EMG. Main results. Stimulation during a physiological contraction demonstrates partial occlusion of force and the neural component of the EMG, due to action potential interactions in motor units activated by both sources. Depending on neural and stimulation firing rates as well as on force-frequency properties, individual motor unit forces can be greater, smaller, or unchanged by the stimulation. In contrast, voluntary motor unit EMG potentials in simultaneously stimulated motor units show progressive occlusion with increasing stimulus rate. The simulations predict that occlusion would be decreased by a reverse stimulation recruitment order. Significance. The results are consistent with and provide a mechanistic interpretation of previously published experimental evidence of force occlusion. The models also predict two effects that have not been reported previously—voluntary EMG occlusion and the advantages of a proximal stimulation site. This study provides a basis for the rational design of both future experiments and clinical

  13. 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.

  14. 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.)

  15. Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller.

    Science.gov (United States)

    Gong, Shuqing; Yang, Shaofu; Guo, Zhenyuan; Huang, Tingwen

    2018-06-01

    The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronization of the derive and response neural networks are derived based on Lyapunov stability theory and some inequality techniques. Finally, several numerical simulations are provided to substantiate the effectiveness of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. 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.

  17. 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.

  18. Nonlinear Dynamics and Chaos in Fractional-Order Hopfield Neural Networks with Delay

    Directory of Open Access Journals (Sweden)

    Xia Huang

    2013-01-01

    Full Text Available A fractional-order two-neuron Hopfield neural network with delay is proposed based on the classic well-known Hopfield neural networks, and further, the complex dynamical behaviors of such a network are investigated. A great variety of interesting dynamical phenomena, including single-periodic, multiple-periodic, and chaotic motions, are found to exist. The existence of chaotic attractors is verified by the bifurcation diagram and phase portraits as well.

  19. 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.

  20. Nonlinear transfer function encodes synchronization in a neural network from the mammalian brain.

    Science.gov (United States)

    Menendez de la Prida, L; Sanchez-Andres, J V

    1999-09-01

    Synchronization is one of the mechanisms by which the brain encodes information. The observed synchronization of neuronal activity has, however, several levels of fluctuations, which presumably regulate local features of specific areas. This means that biological neural networks should have an intrinsic mechanism able to synchronize the neuronal activity but also to preserve the firing capability of individual cells. Here, we investigate the input-output relationship of a biological neural network from developing mammalian brain, i.e., the hippocampus. We show that the probability of occurrence of synchronous output activity (which consists in stereotyped population bursts recorded throughout the hippocampus) is encoded by a sigmoidal transfer function of the input frequency. Under this scope, low-frequency inputs will not produce any coherent output while high-frequency inputs will determine a synchronous pattern of output activity (population bursts). We analyze the effect of the network size (N) on the parameters of the transfer function (threshold and slope). We found that sigmoidal functions realistically simulate the synchronous output activity of hippocampal neural networks. This outcome is particularly important in the application of results from neural network models to neurobiology.

  1. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J

    1998-01-01

    kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained...

  2. 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

  3. Nonlinear forecasting of stream flows using a chaotic approach and artificial neural networks

    Directory of Open Access Journals (Sweden)

    Hakan Tongal

    2013-07-01

    Full Text Available This paper evaluates the forecasting performance of two nonlinear models, k-nearest neighbor (kNN and feed-forward neural networks (FFNN, using stream flow data of the Kızılırmak River, the longest river in Turkey. For the kNN model, the required parameters are delay time, number of nearest neigh- bors and embedding dimension. The optimal delay time was obtained with the mutual information function; the number of nearest neighbors was obtained with the optimization process that minimi- zes RMSE as a function of the neighbor number and the embedding dimension was obtained with the correlation dimension method. The correlation dimension of the Kızılırmak River was d = 2.702, which was used in forming the input structure of the FFNN. The nearest integer above the correlation dimension (i.e., 3 provided the minimal number of required variables to characterize the system, and the maximum number of required variables was obtained with the nearest integer above the value 2d + 1 (Takens, 1981 (i.e., 7. Two FFNN models were developed that incorporate 3 and 7 lagged discharge values and the predicted performance compared to that of the kNN model. The results showed that the kNN model was superior to the FFNN model in stream flow forecasting. However, as a result from the kNN model structure, the model failed in the prediction of peak values. Additionally, it was found that the correlation dimension (if it existed could successfully be used in time series where the determina- tion of the input structure is difficult because of high inter-dependency, as in stream flow time series.  Resumen Este trabajo evalúa el desempeño de pronóstico de dos modelos no lineares, de método de clasificación no paramétrico kNN y de redes neuronales con alimentación avanzada (FNNN, usando datos de flujo del río Kizilirmak, el mayor de Turquía. Para el modelo kNN, los parámetros requeridos son tiempo de retraso, número de vecindarios cercanos y dimensión de

  4. Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.

    Science.gov (United States)

    Mohammadzadeh, Ardashir; Ghaemi, Sehraneh

    2015-09-01

    This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  5. 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.

  6. 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

  7. 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.

  8. Supervised local error estimation for nonlinear image registration using convolutional neural networks

    NARCIS (Netherlands)

    Eppenhof, Koen A.J.; Pluim, Josien P.W.; Styner, M.A.; Angelini, E.D.

    2017-01-01

    Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.)

  16. 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.

  17. Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

    Science.gov (United States)

    Gilra, Aditya; Gerstner, Wulfram

    2017-11-27

    The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

  18. Role of dynamical screening in excitation kinetics of biased quantum wells: Nonlinear absorption and ultrabroadband terahertz emission

    DEFF Research Database (Denmark)

    Turchinovich, Dmitry; Monozon, B. S.; Jepsen, Peter Uhd

    2006-01-01

    In this work we describe the ultrafast excitation kinetics of biased quantum well, arising from the optically induced dynamical screening of a bias electric field. The initial bia electric field inside the quantum well is screened by the optically excited polarized electron-hole pairs. This leads...... wells are in good agreement with our experimental observations [Turchinovich et al., Phys. Rev. B 68, 241307(R) (2003)], as well as in perfect compliance with qualitative considerations. ©2006 American Institute of Physics...

  19. 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

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

    International Nuclear Information System (INIS)

    Huang Zhenkun; Xia Yonghui

    2008-01-01

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

  1. Nonlinear characteristics of the rotating exciter system of power plant generators in case of electricity accidents; Transientes Verhalten des rotierenden Erregersystems von Kraftwerksgeneratoren bei elektrischen Stoerfaellen

    Energy Technology Data Exchange (ETDEWEB)

    Ataei, Nader

    2006-05-09

    Different types of exciter are used for voltage supply to the synchronous generators of power stations depending on the required power and design. The exciter system of the generator, which as a rule consists syncronous motors and commutators, is commonly modeled in conventional models by control units with nonlinear characteristics which do not give an accurate picture of the dynamic processes inside the exciter motor. It was not possible to assess the component loads of the exciter components and the physical characteristics within the exciter system. In this study, a brushless exciter for the grid-connected synchronous generator was investigated which consists of two synchronous motors as primary and secondary exciter and two commutator bridges. A dynamic simulation model was developed for calculating the interactions between the grid, generator and exciter unit in consideration of electromagnetic and galvanic coupling. For this, the normal control units were replaced by physical components of the exciter system, i.e. electric exciter motors and commutators. The study was carried out using an enhanced version of the Siemens NETOMAC software, which provided information on the loads on the exciter components in case of internal and external failures. In particular, loads in coils and commutators were calculated that could not be measured before. The findings enable more accurate dimensioning of the exciter unit making it more fail-safe, and the protective systems can be adjusted more accurately. One important result of the investigation was the identification of all dynamic processes going on between the exciter motors, commutators, generator and grid induced by external and internal failures. (orig.) [German] Zur Spannungsversorgung der Synchrongeneratoren in Kraftwerken werden je nach Leistungsanforderung und Baukonzept unterschiedliche Erregereinrichtungen verwendet. Das Erregersystem des Generators, das in der Regel aus Erregersynchronmaschinen und

  2. Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter.

    Science.gov (United States)

    Rigatos, Gerasimos

    2014-12-01

    A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.

  3. Nonlinear dynamics analysis of a self-organizing recurrent neural network: chaos waning.

    Science.gov (United States)

    Eser, Jürgen; Zheng, Pengsheng; Triesch, Jochen

    2014-01-01

    Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.

  4. Partially non-linear stimulation intensity-dependent effects of direct current stimulation on motor cortex excitability in humans.

    Science.gov (United States)

    Batsikadze, G; Moliadze, V; Paulus, W; Kuo, M-F; Nitsche, M A

    2013-04-01

    Transcranial direct current stimulation (tDCS) of the human motor cortex at an intensity of 1 mA with an electrode size of 35 cm(2) has been shown to induce shifts of cortical excitability during and after stimulation. These shifts are polarity-specific with cathodal tDCS resulting in a decrease and anodal stimulation in an increase of cortical excitability. In clinical and cognitive studies, stronger stimulation intensities are used frequently, but their physiological effects on cortical excitability have not yet been explored. Therefore, here we aimed to explore the effects of 2 mA tDCS on cortical excitability. We applied 2 mA anodal or cathodal tDCS for 20 min on the left primary motor cortex of 14 healthy subjects. Cathodal tDCS at 1 mA and sham tDCS for 20 min was administered as control session in nine and eight healthy subjects, respectively. Motor cortical excitability was monitored by transcranial magnetic stimulation (TMS)-elicited motor-evoked potentials (MEPs) from the right first dorsal interosseous muscle. Global corticospinal excitability was explored via single TMS pulse-elicited MEP amplitudes, and motor thresholds. Intracortical effects of stimulation were obtained by cortical silent period (CSP), short latency intracortical inhibition (SICI) and facilitation (ICF), and I wave facilitation. The above-mentioned protocols were recorded both before and immediately after tDCS in randomized order. Additionally, single-pulse MEPs, motor thresholds, SICI and ICF were recorded every 30 min up to 2 h after stimulation end, evening of the same day, next morning, next noon and next evening. Anodal as well as cathodal tDCS at 2 mA resulted in a significant increase of MEP amplitudes, whereas 1 mA cathodal tDCS decreased corticospinal excitability. A significant shift of SICI and ICF towards excitability enhancement after both 2 mA cathodal and anodal tDCS was observed. At 1 mA, cathodal tDCS reduced single-pulse TMS-elicited MEP amplitudes and shifted SICI

  5. 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.

  6. Dual frequency parametric excitation of a nonlinear, multi degree of freedom mechanical amplifier with electronically modified topology

    Science.gov (United States)

    Dolev, A.; Bucher, I.

    2018-04-01

    Mechanical or electromechanical amplifiers can exploit the high-Q and low noise features of mechanical resonance, in particular when parametric excitation is employed. Multi-frequency parametric excitation introduces tunability and is able to project weak input signals on a selected resonance. The present paper addresses multi degree of freedom mechanical amplifiers or resonators whose analysis and features require treatment of the spatial as well as temporal behavior. In some cases, virtual electronic coupling can alter the given topology of the resonator to better amplify specific inputs. An analytical development is followed by a numerical and experimental sensitivity and performance verifications, illustrating the advantages and disadvantages of such topologies.

  7. Adaptive control of nonlinear system using online error minimum neural networks.

    Science.gov (United States)

    Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

    2016-11-01

    In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. 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...

  9. 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.

  10. Investigating Nonlinear Shoreline Multiperiod Change from Orthophoto Map Information by Using a Neural Network Model

    Directory of Open Access Journals (Sweden)

    Tienfuan Kerh

    2014-01-01

    Full Text Available The effects of extreme weather and overdevelopment may cause some coastal areas to exhibit erosion problems, which in turn may contribute to creating disasters of varying scale, particularly in regions comprising islands. This study used aerial survey information from three periods (1990, 2001, and 2010 and used graphical software to establish the spatial data of six beaches surrounding the island of Taiwan. An overlaying technique was then implemented to compare the sandy area of each beach in the aforementioned study periods. In addition, an artificial neural network model was developed based on available digitised coordinates for predicting coastline variation for 2015 and 2020. An onsite investigation was performed using a global positioning system for comparing the beaches. The results revealed that two beaches from this study may have experienced significant changes in total sandy areas under a statistical 95% confidence interval. The proposed method and the result of this study may provide a valuable reference in follow-up research and applications.

  11. Stability of bumps in piecewise smooth neural fields with nonlinear adaptation

    KAUST Repository

    Kilpatrick, Zachary P.

    2010-06-01

    We study the linear stability of stationary bumps in piecewise smooth neural fields with local negative feedback in the form of synaptic depression or spike frequency adaptation. The continuum dynamics is described in terms of a nonlocal integrodifferential equation, in which the integral kernel represents the spatial distribution of synaptic weights between populations of neurons whose mean firing rate is taken to be a Heaviside function of local activity. Discontinuities in the adaptation variable associated with a bump solution means that bump stability cannot be analyzed by constructing the Evans function for a network with a sigmoidal gain function and then taking the high-gain limit. In the case of synaptic depression, we show that linear stability can be formulated in terms of solutions to a system of pseudo-linear equations. We thus establish that sufficiently strong synaptic depression can destabilize a bump that is stable in the absence of depression. These instabilities are dominated by shift perturbations that evolve into traveling pulses. In the case of spike frequency adaptation, we show that for a wide class of perturbations the activity and adaptation variables decouple in the linear regime, thus allowing us to explicitly determine stability in terms of the spectrum of a smooth linear operator. We find that bumps are always unstable with respect to this class of perturbations, and destabilization of a bump can result in either a traveling pulse or a spatially localized breather. © 2010 Elsevier B.V. All rights reserved.

  12. Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

    Directory of Open Access Journals (Sweden)

    Zhi-Ren Tsai

    2013-01-01

    Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

  13. Kinetics of non-equilibrium processes in non-linear crystals of lithium borates excited with synchrotron radiation

    CERN Document Server

    Ogorodnikov, I N; Isaenko, L I; Zinin, E I; Kruzhalov, A V

    2000-01-01

    The paper presents the results of a study of the LiB sub 3 O sub 5 and Li sub 2 B sub 4 O sub 7 crystals by the use of the luminescent spectroscopy with the sub-nanosecond time resolution under excitation of the high-power synchrotron radiation. The commonness in the origin of the non-equilibrium processes in these crystals as well as the observed differences in the luminescence manifestations is discussed.

  14. Kinetics of non-equilibrium processes in non-linear crystals of lithium borates excited with synchrotron radiation

    Energy Technology Data Exchange (ETDEWEB)

    Ogorodnikov, I.N. E-mail: ogo@dpt.ustu.ru; Pustovarov, V.A.; Isaenko, L.I.; Zinin, E.I.; Kruzhalov, A.V

    2000-06-21

    The paper presents the results of a study of the LiB{sub 3}O{sub 5} and Li{sub 2}B{sub 4}O{sub 7} crystals by the use of the luminescent spectroscopy with the sub-nanosecond time resolution under excitation of the high-power synchrotron radiation. The commonness in the origin of the non-equilibrium processes in these crystals as well as the observed differences in the luminescence manifestations is discussed.

  15. Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems.

    Science.gov (United States)

    Oparaji, Uchenna; Sheu, Rong-Jiun; Bankhead, Mark; Austin, Jonathan; Patelli, Edoardo

    2017-12-01

    Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the ANN with the highest R 2 value can lead to biassing in the prediction. This is as a result of the fact that the use of R 2 cannot determine if the prediction made by ANN is biased. Additionally, R 2 does not indicate if a model is adequate, as it is possible to have a low R 2 for a good model and a high R 2 for a bad model. Hence, in this paper, we propose an approach to improve the robustness of a prediction made by ANN. The approach is based on a systematic combination of identical trained ANNs, by coupling the Bayesian framework and model averaging. Additionally, the uncertainties of the robust prediction derived from the approach are quantified in terms of confidence intervals. To demonstrate the applicability of the proposed approach, two synthetic numerical examples are presented. Finally, the proposed approach is used to perform a reliability and sensitivity analyses on a process simulation model of a UK nuclear effluent treatment plant developed by National Nuclear Laboratory (NNL) and treated in this study as a black-box employing a set of training data as a test case. This model has been extensively validated against plant and experimental data and used to support the UK effluent discharge strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Evapotranspiration Modeling by Linear, Nonlinear Regression and Artificial Neural Network in Greenhouse (Case study Reference Crop, Cucumber and Tomato

    Directory of Open Access Journals (Sweden)

    vahid Rezaverdinejad

    2017-01-01

    important models to estimate ETc in greenhouse. The inputs of these models are net radiation, temperature, day after planting and air vapour pressure deficit (or relative humidity. Materials and Methods: In this study, daily ETc of reference crop, greenhouse tomato and cucumber crops were measured using lysimeter method in Urmia region. Several linear, nonlinear regressions and artificial neural networks were considered for ETc modelling in greenhouse. For this purpose, the effective meteorological parameters on ETc process includes: air temperature (T, air humidity (RH, air pressure (P, air vapour pressure deficit (VPD, day after planting (N and greenhouse net radiation (SR were considered and measured. According to the goodness of fit, different models of artificial neural networks and regression were compared and evaluated. Furthermore, based on partial derivatives of regression models, sensitivity analysis was conducted. The accuracy and performance of the employed models was judged by ten statistical indices namely root mean square error (RMSE, normalized root mean square error (NRMSE and coefficient of determination (R2. Results and Discussion: Based on the results, the most accurate regression model to reference ETc prediction was obtained three variables exponential function of VPD, RH and SR with RMSE=0.378 mm day-1. The RMSE of optimal artificial neural network to reference ET prediction for train and test data sets were obtained 0.089 and 0.365 mm day-1, respectively. The performance of logarithmic and exponential functions to prediction of cucumber ETc were proper, with high dependent variables especially, and the most accurate regression model to cucumber ET prediction was obtained for exponential function of five variables: VPD, N, T, RH and SR with RMSE=0.353 mm day-1. In addition, for tomato ET prediction, the most accurate regression model was obtained for exponential function of four variables: VPD, N, RH and SR with RMSE= 0.329 mm day-1. The best

  17. Nonlinear spectral imaging of human normal skin, basal cell carcinoma and squamous cell carcinoma based on two-photon excited fluorescence and second-harmonic generation

    Science.gov (United States)

    Xiong, S. Y.; Yang, J. G.; Zhuang, J.

    2011-10-01

    In this work, we use nonlinear spectral imaging based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) for analyzing the morphology of collagen and elastin and their biochemical variations in basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and normal skin tissue. It was found in this work that there existed apparent differences among BCC, SCC and normal skin in terms of their thickness of the keratin and epithelial layers, their size of elastic fibers, as well as their distribution and spectral characteristics of collagen. These differences can potentially be used to distinguish BCC and SCC from normal skin, and to discriminate between BCC and SCC, as well as to evaluate treatment responses.

  18. 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.

  19. 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.

  20. Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

    Science.gov (United States)

    Bhaduri, Aritra; Banerjee, Amitava; Roy, Subhrajit; Kar, Sougata; Basu, Arindam

    2018-03-01

    We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

  1. The Effect of Material Property on the Critical Velocity of Randomly Excited Nonlinear Axially Travelling Functionally Graded Plates

    Directory of Open Access Journals (Sweden)

    M. Abedi

    Full Text Available Abstract In this paper, the critical axial speeds of three types of sigmoid, power law and exponential law functionally graded plates for both isotropic and orthotropic cases are obtained via a completely analytic method. The plates are subjected to lateral white noise excitation and show evidence of large deformations. Due to randomness, the conventional deterministic methods fail and a statistical approach must be selected. Here, the probability density function is evaluated analytically for prescribed plates and used to investigate the critical axial velocity of them. Specifically the effect of in-plane forces, mean value of lateral load and the material property on the critical axial speed are studied and discussed for both isotropic and orthotropic functionally graded plates. Since the governing equation is transformed to a non dimensional format, the results can be used for a wide range of plate dimensions. It is shown that the material heterogeneity palys an essential and significant role in increasing or decreasing the critical speed of both isotropic and orthotropic functionally graded plates.

  2. Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

    Directory of Open Access Journals (Sweden)

    Paul eChorley

    2011-05-01

    Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.

  3. 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.

  4. Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

    International Nuclear Information System (INIS)

    Wang Rubin; Yu Wei

    2005-01-01

    In this paper, we investigate how the population of neuronal oscillators deals with information and the dynamic evolution of neural coding when the external stimulation acts on it. Numerically computing method is used to describe the evolution process of neural coding in three-dimensioned space. The numerical result proves that only the suitable stimulation can change the coupling structure and plasticity of neurons

  5. Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models

    Science.gov (United States)

    Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals...

  6. Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

    Science.gov (United States)

    DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

    2016-03-29

    We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

  7. 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.

  8. 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

  9. Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

    DEFF Research Database (Denmark)

    Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

    2004-01-01

    This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

  10. 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.

  11. Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets

    International Nuclear Information System (INIS)

    Canizes, Bruno; Soares, João; Faria, Pedro; Vale, Zita

    2013-01-01

    Highlights: • Ancillary services market management. • Ancillary services requirements forecast based on Artificial Neural Network. • Ancillary services clearing mechanisms without complex bids and with complex bids. - Abstract: Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids

  12. Fouling resistance prediction using artificial neural network nonlinear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations

    Energy Technology Data Exchange (ETDEWEB)

    Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)

    2016-06-03

    Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.

  13. Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

    International Nuclear Information System (INIS)

    Guerra, Fabio A.; Coelho, Leandro dos S.

    2008-01-01

    An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

  14. Optical study of interactions among propagation waves of neural excitation in the rat somatosensory cortex evoked by forelimb and hindlimb stimuli.

    Science.gov (United States)

    Hama, Noriyuki; Kawai, Minako; Ito, Shin-Ichi; Hirota, Akihiko

    2018-02-14

    Multisite optical recording has revealed that the neural excitation wave induced by a sensory stimulation begins at a focus and propagates on the cortex. This wave is considered to be important for computation in the sensory cortex, particularly the integration of sensory information; however, the nature of this wave remains largely unknown. In the present study, we examined the interaction between two waves in the rat sensory cortex induced by hindlimb and forelimb stimuli with different inter-stimulus intervals. We classified the resultant patterns as follows: 1) the collision of two waves; 2) the hindlimb response being evoked while the forelimb-induced wave is passing the hindlimb focus; and 3) the hindlimb response being evoked after the forelimb-induced wave has passed the hindlimb focus. In pattern 1, the two waves fused into a single wave, but the propagation pattern differed from that predicted by the superimposition of two solely induced propagation courses. In pattern 2, the state of the interaction between the two waves varied depending on the phase of optical signals constituting the forelimb-induced wave around the hindlimb focus. Although no hindlimb-induced wave was observed in the rising phase, the propagating velocity of the forelimb-induced wave increased. At the peak, neither the hindlimb-induced response nor a modulatory effect on the forelimb-induced wave was detected. In pattern 3, the hindlimb-induced wave showed a reduced amplitude and spatial extent. These results indicate that the state of the interaction between waves was strongly influenced by the relative timing of sensory inputs.

  15. 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

  16. Stability switches, oscillatory multistability, and spatio-temporal patterns of nonlinear oscillations in recurrently delay coupled neural networks.

    Science.gov (United States)

    Song, Yongli; Makarov, Valeri A; Velarde, Manuel G

    2009-08-01

    A model of time-delay recurrently coupled spatially segregated neural assemblies is here proposed. We show that it operates like some of the hierarchical architectures of the brain. Each assembly is a neural network with no delay in the local couplings between the units. The delay appears in the long range feedforward and feedback inter-assemblies communications. Bifurcation analysis of a simple four-units system in the autonomous case shows the richness of the dynamical behaviors in a biophysically plausible parameter region. We find oscillatory multistability, hysteresis, and stability switches of the rest state provoked by the time delay. Then we investigate the spatio-temporal patterns of bifurcating periodic solutions by using the symmetric local Hopf bifurcation theory of delay differential equations and derive the equation describing the flow on the center manifold that enables us determining the direction of Hopf bifurcations and stability of the bifurcating periodic orbits. We also discuss computational properties of the system due to the delay when an external drive of the network mimicks external sensory input.

  17. From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

    Science.gov (United States)

    Truccolo, Wilson

    2016-11-01

    This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

  18. 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

  19. 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.

  20. Advances and challenges in label-free nonlinear optical imaging using two-photon excitation fluorescence and second harmonic generation for cancer research

    NARCIS (Netherlands)

    Thomas, Giju; van Voskuilen, Johan; Gerritsen, Hans C.; Sterenborg, H. J. C. M.

    2014-01-01

    Nonlinear optical imaging (NLOI) has emerged to be a promising tool for bio-medical imaging in recent times. Among the various applications of NLOI, its utility is the most significant in the field of pre-clinical and clinical cancer research. This review begins by briefly covering the core

  1. Nonlinear electronic excitations in crystalline solids using meta-generalized gradient approximation and hybrid functional in time-dependent density functional theory

    Energy Technology Data Exchange (ETDEWEB)

    Sato, Shunsuke A. [Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8571 (Japan); Taniguchi, Yasutaka [Center for Computational Science, University of Tsukuba, Tsukuba 305-8571 (Japan); Department of Medical and General Sciences, Nihon Institute of Medical Science, 1276 Shimogawara, Moroyama-Machi, Iruma-Gun, Saitama 350-0435 (Japan); Shinohara, Yasushi [Max Planck Institute of Microstructure Physics, 06120 Halle (Germany); Yabana, Kazuhiro [Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8571 (Japan); Center for Computational Science, University of Tsukuba, Tsukuba 305-8571 (Japan)

    2015-12-14

    We develop methods to calculate electron dynamics in crystalline solids in real-time time-dependent density functional theory employing exchange-correlation potentials which reproduce band gap energies of dielectrics; a meta-generalized gradient approximation was proposed by Tran and Blaha [Phys. Rev. Lett. 102, 226401 (2009)] (TBm-BJ) and a hybrid functional was proposed by Heyd, Scuseria, and Ernzerhof [J. Chem. Phys. 118, 8207 (2003)] (HSE). In time evolution calculations employing the TB-mBJ potential, we have found it necessary to adopt the predictor-corrector step for a stable time evolution. We have developed a method to evaluate electronic excitation energy without referring to the energy functional which is unknown for the TB-mBJ potential. For the HSE functional, we have developed a method for the operation of the Fock-like term in Fourier space to facilitate efficient use of massive parallel computers equipped with graphic processing units. We compare electronic excitations in silicon and germanium induced by femtosecond laser pulses using the TB-mBJ, HSE, and a simple local density approximation (LDA). At low laser intensities, electronic excitations are found to be sensitive to the band gap energy: they are close to each other using TB-mBJ and HSE and are much smaller in LDA. At high laser intensities close to the damage threshold, electronic excitation energies do not differ much among the three cases.

  2. 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.

  3. 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.

  4. Some Aspects of Nonlinearity and Self-Organization In Biosystems on Examples of Localized Excitations in the DNA Molecule and Generalized Fisher–KPP Model

    Directory of Open Access Journals (Sweden)

    A. V. Shapovalov

    2018-02-01

    Full Text Available This review deals with ideas and approaches to nonlinear phenomena, based on different branches of physics and related to biological systems, that focus on how small impacts can significantly change the state of the system at large spatial scales. This problem is very extensive, and it cannot be fully resolved in this paper. Instead, some selected physical effects are briefly reviewed. We consider sine-Gordon solitons and nonlinear Schrodinger solitons in some models of DNA as examples of self-organization at the molecular level, as well as examine features of their formation and dynamics under the influence of external influences. In addition, the formation of patterns in the generalized Fisher–KPP model is viewed as a simple example of self-organization in a system with nonlocal interaction at the cellular level. Symmetries of model equations are employed to analyze the considered nonlinear phenomena. In this context the possible relations between phenomena considered and released activity effect, which is assessed differently in the literature, are discussed.

  5. Observation of non-linear effects in a quasi-one-dimensional antiferromagnet: magnetic excitations in CsVCl sub 3

    CERN Document Server

    Inami, T; Tanaka, H

    1997-01-01

    The spin dynamics of the hexagonal ABX sub 3 -type quasi-one-dimensional antiferromagnet CsVCl sub 3 is investigated by means of an inelastic neutron scattering technique. In good qualitative agreement with a recent spin-wave calculation including higher-order terms, a large scattering cross-section arising from two-magnon excitations is observed at the one-dimensional antiferromagnetic zone centre. In addition, we measured spin-wave excitations between the chains precisely and revealed that the spin-wave dispersion curves are modified in energy and in intensity on account of the anticrossing between the one-magnon branches and two-magnon continuum. These results demonstrate that anharmonic terms are important in the spin dynamics of CsVCl sub 3 even at low temperatures. We also measured the temperature dependence of the magnetic excitations and found that far above the Neel temperature the two-magnon process gives a considerable contribution to the inelastic spectrum. (author)

  6. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

    In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

  7. Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

    Directory of Open Access Journals (Sweden)

    Zheng Lu

    2017-06-01

    Full Text Available A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn to retrieve time series soil moisture (SM that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2, Global Land Satellite product (GLASS Leaf Area Index (LAI, precipitation from the Tropical Rainfall Measuring Mission (TRMM and the Global Precipitation Measurement (GPM, and a global 30 arc-second elevation (GTOPO-30. The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA and the Land Surface Parameter Model (LPRM. The reprocessed fused SM from two years (2013 and 2014 was inputted into the NARXnn for training; subsequently, SM during a third year (2015 was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS, as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015, lower Bias values (absolute value of Bias ≤ 0.02 and root mean square error values (RMSE ≤ 0.06, and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

  8. Excitation energies and properties of open-shell singlet molecules applications to a new class of molecules for nonlinear optics and singlet fission

    CERN Document Server

    Nakano, Masayoshi

    2014-01-01

    This brief investigates the diradical character, which is one of the ground-state chemical indices for 'bond weakness' or 'electron correlation' and which allows researchers to explore the origins of the electron-correlation-driven physico-chemical phenomena concerned with electronic, optical and magnetic properties as well as to control them in the broad fields of physics and chemistry. It then provides the theoretical fundamentals of ground and excited electronic structures of symmetric and asymmetric open-shell molecular systems by using model molecular systems. Moreover, it presents the th

  9. 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....

  10. 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...

  11. Cryogenic exciter

    Science.gov (United States)

    Bray, James William [Niskayuna, NY; Garces, Luis Jose [Niskayuna, NY

    2012-03-13

    The disclosed technology is a cryogenic static exciter. The cryogenic static exciter is connected to a synchronous electric machine that has a field winding. The synchronous electric machine is cooled via a refrigerator or cryogen like liquid nitrogen. The static exciter is in communication with the field winding and is operating at ambient temperature. The static exciter receives cooling from a refrigerator or cryogen source, which may also service the synchronous machine, to selected areas of the static exciter and the cooling selectively reduces the operating temperature of the selected areas of the static exciter.

  12. 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

  13. Coulomb excitation

    International Nuclear Information System (INIS)

    McGowan, F.K.; Stelson, P.H.

    1974-01-01

    The theory of Coulomb excitation and a brief review of pertinent treatments of the Coulomb excitation process that are useful for the analysis of experiments are given. Examples demonstrating the scope of nuclear structure information obtainable from gamma spectroscopy are presented. Direct Elambda excitation of 232 Th is discussed in terms of the one phonon octupole vibrational spectrum. B(MI) reduced transition probabilities resulting from Coulomb excitation of odd-A deformed nuclei with heavy ions are presented as a test of the rotational model. The use of gamma ray coincidence and particle-gamma coincidence as tools for investigating Coulomb excitation is discussed. (U.S.)

  14. 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.

  15. 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...

  16. 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 ...

  17. 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.

  18. Exciter switch

    Science.gov (United States)

    Mcpeak, W. L.

    1975-01-01

    A new exciter switch assembly has been installed at the three DSN 64-m deep space stations. This assembly provides for switching Block III and Block IV exciters to either the high-power or 20-kW transmitters in either dual-carrier or single-carrier mode. In the dual-carrier mode, it provides for balancing the two drive signals from a single control panel located in the transmitter local control and remote control consoles. In addition to the improved switching capabilities, extensive monitoring of both the exciter switch assembly and Transmitter Subsystem is provided by the exciter switch monitor and display assemblies.

  19. Voiced Excitations

    National Research Council Canada - National Science Library

    Holzricher, John

    2004-01-01

    To more easily obtain a voiced excitation function for speech characterization, measurements of skin motion, tracheal tube, and vocal fold, motions were made and compared to EM sensor-glottal derived...

  20. Exciting Pools

    Science.gov (United States)

    Wright, Bradford L.

    1975-01-01

    Advocates the creation of swimming pool oscillations as part of a general investigation of mechanical oscillations. Presents the equations, procedure for deriving the slosh modes, and methods of period estimation for exciting swimming pool oscillations. (GS)

  1. Excited states

    CERN Document Server

    Lim, Edward C

    1974-01-01

    Excited States, Volume I reviews radiationless transitions, phosphorescence microwave double resonance through optical spectra in molecular solids, dipole moments in excited states, luminescence of polar molecules, and the problem of interstate interaction in aromatic carbonyl compounds. The book discusses the molecular electronic radiationless transitions; the double resonance techniques and the relaxation mechanisms involving the lowest triplet state of aromatic compounds; as well as the optical spectra and relaxation in molecular solids. The text also describes dipole moments and polarizab

  2. 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.

  3. 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...

  4. Excited fermions

    International Nuclear Information System (INIS)

    Boudjema, F.; Djouadi, A.; Kneur, J.L.

    1992-01-01

    The production of excited fermions with mass above 100 GeV is considered. f→Vf (1) decay widths are calculated where V=γ, Z or W. Excited fermion pair production in e + e - annihilation and in γγ collisions, and single production in e + e - annihilation, eγ and γγ collisions is also discussed. Cross sections are calculated for all these cases. The discovery potential of the NLC at 500 GeV is compared with that of other colliders. (K.A.) 15 refs., 5 figs., 2 tabs

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. Excited baryons

    International Nuclear Information System (INIS)

    Mukhopadhyay, N.C.

    1986-01-01

    The status of the theory of the low-energy approach to hadron structure is reviewed briefly by surveying a few relevant models. A few examples of tests needed to sort out the predictions of different models pertaining to the quark-gluon structure of hadrons are discussed, and given the resulting physics objectives, a few experimental options for excited baryon research at CFBAF are suggested

  10. Excited baryons

    Energy Technology Data Exchange (ETDEWEB)

    Mukhopadhyay, N.C.

    1986-01-01

    The status of the theory of the low-energy approach to hadron structure is reviewed briefly by surveying a few relevant models. A few examples of tests needed to sort out the predictions of different models pertaining to the quark-gluon structure of hadrons are discussed, and given the resulting physics objectives, a few experimental options for excited baryon research at CFBAF are suggested. (LEW)

  11. 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.

  12. 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...

  13. 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...

  14. 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

  15. 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...

  16. Nonlinear optics

    International Nuclear Information System (INIS)

    Boyd, R.W.

    1992-01-01

    Nonlinear optics is the study of the interaction of intense laser light with matter. This book is a textbook on nonlinear optics at the level of a beginning graduate student. The intent of the book is to provide an introduction to the field of nonlinear optics that stresses fundamental concepts and that enables the student to go on to perform independent research in this field. This book covers the areas of nonlinear optics, quantum optics, quantum electronics, laser physics, electrooptics, and modern optics

  17. 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

  18. 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

  19. 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.

  20. 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

  1. Nonlinear optics

    CERN Document Server

    Bloembergen, Nicolaas

    1996-01-01

    Nicolaas Bloembergen, recipient of the Nobel Prize for Physics (1981), wrote Nonlinear Optics in 1964, when the field of nonlinear optics was only three years old. The available literature has since grown by at least three orders of magnitude.The vitality of Nonlinear Optics is evident from the still-growing number of scientists and engineers engaged in the study of new nonlinear phenomena and in the development of new nonlinear devices in the field of opto-electronics. This monograph should be helpful in providing a historical introduction and a general background of basic ideas both for expe

  2. 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)

  3. 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.

  4. 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...

  5. A novel method combining cellular neural networks and the coupled nonlinear oscillators' paradigm involving a related bifurcation analysis for robust image contrast enhancement in dynamically changing difficult visual environments

    International Nuclear Information System (INIS)

    Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere

    2010-01-01

    It is well known that a machine vision-based analysis of a dynamic scene, for example in the context of advanced driver assistance systems (ADAS), does require real-time processing capabilities. Therefore, the system used must be capable of performing both robust and ultrafast analyses. Machine vision in ADAS must fulfil the above requirements when dealing with a dynamically changing visual context (i.e. driving in darkness or in a foggy environment, etc). Among the various challenges related to the analysis of a dynamic scene, this paper focuses on contrast enhancement, which is a well-known basic operation to improve the visual quality of an image (dynamic or static) suffering from poor illumination. The key objective is to develop a systematic and fundamental concept for image contrast enhancement that should be robust despite a dynamic environment and that should fulfil the real-time constraints by ensuring an ultrafast analysis. It is demonstrated that the new approach developed in this paper is capable of fulfilling the expected requirements. The proposed approach combines the good features of the 'coupled oscillators'-based signal processing paradigm with the good features of the 'cellular neural network (CNN)'-based one. The first paradigm in this combination is the 'master system' and consists of a set of coupled nonlinear ordinary differential equations (ODEs) that are (a) the so-called 'van der Pol oscillator' and (b) the so-called 'Duffing oscillator'. It is then implemented or realized on top of a 'slave system' platform consisting of a CNN-processors platform. An offline bifurcation analysis is used to find out, a priori, the windows of parameter settings in which the coupled oscillator system exhibits the best and most appropriate behaviours of interest for an optimal resulting image processing quality. In the frame of the extensive bifurcation analysis carried out, analytical formulae have been derived, which are capable of determining the various

  6. 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.

  7. Nonlinear Science

    CERN Document Server

    Yoshida, Zensho

    2010-01-01

    This book gives a general, basic understanding of the mathematical structure "nonlinearity" that lies in the depths of complex systems. Analyzing the heterogeneity that the prefix "non" represents with respect to notions such as the linear space, integrability and scale hierarchy, "nonlinear science" is explained as a challenge of deconstruction of the modern sciences. This book is not a technical guide to teach mathematical tools of nonlinear analysis, nor a zoology of so-called nonlinear phenomena. By critically analyzing the structure of linear theories, and cl

  8. Nonlinear oscillations

    CERN Document Server

    Nayfeh, Ali Hasan

    1995-01-01

    Nonlinear Oscillations is a self-contained and thorough treatment of the vigorous research that has occurred in nonlinear mechanics since 1970. The book begins with fundamental concepts and techniques of analysis and progresses through recent developments and provides an overview that abstracts and introduces main nonlinear phenomena. It treats systems having a single degree of freedom, introducing basic concepts and analytical methods, and extends concepts and methods to systems having degrees of freedom. Most of this material cannot be found in any other text. Nonlinear Oscillations uses sim

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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...

  18. 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.

  19. Laser amplification in excited dielectrics

    Science.gov (United States)

    Winkler, Thomas; Haahr-Lillevang, Lasse; Sarpe, Cristian; Zielinski, Bastian; Götte, Nadine; Senftleben, Arne; Balling, Peter; Baumert, Thomas

    2018-01-01

    Wide-bandgap dielectrics such as glasses or water are transparent at visible and infrared wavelengths. This changes when they are exposed to ultrashort and highly intense laser pulses. Different interaction mechanisms lead to the appearance of various transient nonlinear optical phenomena. Using these, the optical properties of dielectrics can be controlled from the transparent to the metal-like state. Here we expand this range by a yet unexplored mechanism in excited dielectrics: amplification. In a two-colour pump-probe experiment, we show that a 400 nm femtosecond laser pulse is coherently amplified inside an excited sapphire sample on a scale of a few micrometres. Simulations strongly support the proposed two-photon stimulated emission process, which is temporally and spatially controllable. Consequently, we expect applications in all fields that demand strongly localized amplification.

  20. Laser amplification in excited dielectrics

    DEFF Research Database (Denmark)

    Winkler, Thomas; Haahr-Lillevang, Lasse; Sarpe, Cristian

    2018-01-01

    Wide-bandgap dielectrics such as glasses or water are transparent at visible and infrared wavelengths. This changes when they are exposed to ultrashort and highly intense laser pulses. Different interaction mechanisms lead to the appearance of various transient nonlinear optical phenomena. Using...... these, the optical properties of dielectrics can be controlled from the transparent to the metal-like state. Here we expand this range by a yet unexplored mechanism in excited dielectrics: amplification. In a two-colour pump-probe experiment, we show that a 400nm femtosecond laser pulse is coherently...

  1. 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

  2. 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...

  3. Nonlinear optics

    CERN Document Server

    Boyd, Robert W

    2013-01-01

    Nonlinear Optics is an advanced textbook for courses dealing with nonlinear optics, quantum electronics, laser physics, contemporary and quantum optics, and electrooptics. Its pedagogical emphasis is on fundamentals rather than particular, transitory applications. As a result, this textbook will have lasting appeal to a wide audience of electrical engineering, physics, and optics students, as well as those in related fields such as materials science and chemistry.Key Features* The origin of optical nonlinearities, including dependence on the polarization of light* A detailed treatment of the q

  4. Nonlinear systems

    National Research Council Canada - National Science Library

    Drazin, P. G

    1992-01-01

    This book is an introduction to the theories of bifurcation and chaos. It treats the solution of nonlinear equations, especially difference and ordinary differential equations, as a parameter varies...

  5. Nonlinear analysis

    CERN Document Server

    Gasinski, Leszek

    2005-01-01

    Hausdorff Measures and Capacity. Lebesgue-Bochner and Sobolev Spaces. Nonlinear Operators and Young Measures. Smooth and Nonsmooth Analysis and Variational Principles. Critical Point Theory. Eigenvalue Problems and Maximum Principles. Fixed Point Theory.

  6. 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

  7. 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.

  8. 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...

  9. 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...

  10. 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...

  11. EDITORIAL: Why we need a new journal in neural engineering

    Science.gov (United States)

    Durand, Dominique M.

    2004-03-01

    laboratory but also in the publication of scientific papers. We do, therefore, need a new journal that provides a platform for this emerging interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that spans the disciplines. Journal of Neural Engineering will provide this platform. The new journal will publish full-length articles of the highest quality and importance in the field of neural engineering at the molecular, cellular and systems levels. The scope of Journal of Neural Engineering encompasses experimental, computational and theoretical aspects of neural interfacing, neuroelectronics, neuromechanical systems, neuroinformatics, neuroimaging, neural prostheses, artificial and biological neural circuits, neural control, neural tissue regeneration, neural signal processing, neural modeling and neuro-computation. The scope of the journal has both depth and breadth in areas relevant to the interface between neuroscience and engineering. There will be two Editors-in-Chief, with expertise covering both engineering and neuroscience. Experts in the areas encompassed by the journal's scope have been identified for the Editorial Board and the composition of the board will be continually updated to address the developments in this new and exciting field. The first issue of this new journal covers a variety of topics that combine neuroscience and engineering: mental state recognition from EEG signals, analysis of body motion in Parkinson's patients, non-linear dynamics of the respiratory system, automatic identification of saccade-related visual evoked potentials, multiple electrode stimulators, algorithms to estimate the causal relationship between brain sources, diffusion tensor imaging in the brain and phase synchronization of neural activity in vitro. This broad array of manuscripts focusing on neural imaging, neurophysiology, neural signal processing, neuroelectronics and neuro-dynamics can

  12. 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...

  13. 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

  14. 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.

  15. Nonlinear optimization

    CERN Document Server

    Ruszczynski, Andrzej

    2011-01-01

    Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates the theory and the methods of nonlinear optimization in a unified, clear, and mathematically rigorous fashion, with detailed and easy-to-follow proofs illustrated by numerous examples and figures. The book covers convex analysis, the theory of optimality conditions, duality theory, and numerical methods for solving unconstrained and constrained optimization problems. It addresses not only classical material but also modern top...

  16. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  17. Excited charmed mesons

    International Nuclear Information System (INIS)

    Butler, J.N.; Shukla, S.

    1995-05-01

    The experimental status of excited charmed mesons is reviewed and is compared to theoretical expectations. Six states have been observed and their properties are consistent with those predicted for excited charmed states with orbital angular momentum equal to one

  18. Portable vibration exciter

    Science.gov (United States)

    Beecher, L. C.; Williams, F. T.

    1970-01-01

    Gas-driven vibration exciter produces a sinusoidal excitation function controllable in frequency and in amplitude. It allows direct vibration testing of components under normal loads, removing the possibility of component damage due to high static pressure.

  19. Multi-frequency excitation

    KAUST Repository

    Younis, Mohammad I.

    2016-01-01

    Embodiments of multi-frequency excitation are described. In various embodiments, a natural frequency of a device may be determined. In turn, a first voltage amplitude and first fixed frequency of a first source of excitation can be selected

  20. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  1. 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.

  2. 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

  3. 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.

  4. 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)

  5. 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.

  6. Linear and nonlinear optical properties of borate crystals as ...

    Indian Academy of Sciences (India)

    Unknown

    crystal series, with an accuracy acceptable for materials development/design, and answer the questions often ... Optical property; nonlinear optical crystals; first principles calculation. 1. ..... system, and is not in concept suitable to excitation pro-.

  7. Non-linear optical imaging – Introduction and pharmaceutical applications

    NARCIS (Netherlands)

    Fussell, A.L.; Isomaki, Antti; Strachan, Clare J.

    2013-01-01

    Nonlinear optical imaging is an emerging technology with much potential in pharmaceutical analysis. The technique encompasses a range of optical phenomena, including coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG), and twophoton excited fluorescence (TPEF). The

  8. Nonlinear Dynamics of Carbon Nanotubes Under Large Electrostatic Force

    KAUST Repository

    Xu, Tiantian; Younis, Mohammad I.

    2015-01-01

    Because of the inherent nonlinearities involving the behavior of CNTs when excited by electrostatic forces, modeling and simulating their behavior is challenging. The complicated form of the electrostatic force describing the interaction

  9. 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...

  10. 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.

  11. Dynamical analysis of highly excited molecular spectra

    Energy Technology Data Exchange (ETDEWEB)

    Kellman, M.E. [Univ. of Oregon, Eugene (United States)

    1993-12-01

    The goal of this program is new methods for analysis of spectra and dynamics of highly excited vibrational states of molecules. In these systems, strong mode coupling and anharmonicity give rise to complicated classical dynamics, and make the simple normal modes analysis unsatisfactory. New methods of spectral analysis, pattern recognition, and assignment are sought using techniques of nonlinear dynamics including bifurcation theory, phase space classification, and quantization of phase space structures. The emphasis is chaotic systems and systems with many degrees of freedom.

  12. Elementary excitations in nuclei

    International Nuclear Information System (INIS)

    Lemmer, R.H.

    1987-01-01

    The role of elementary quasi-particle and quasi-hole excitations is reviewed in connection with the analysis of data involving high-lying nuclear states. This article includes discussions on: (i) single quasi-hole excitations in pick-up reactions, (ii) the formation of single quasi-hole and quasi-particle excitations (in different nuclei) during transfer reactions, followed by (iii) quasi-particle quasi-hole excitations in the same nucleus that are produced by photon absorption. Finally, the question of photon absorption in the vicinity of the elementary Δ resonance is discussed, where nucleonic as well as nuclear degrees of freedom can be excited

  13. Multi-frequency excitation

    KAUST Repository

    Younis, Mohammad I.

    2016-03-10

    Embodiments of multi-frequency excitation are described. In various embodiments, a natural frequency of a device may be determined. In turn, a first voltage amplitude and first fixed frequency of a first source of excitation can be selected for the device based on the natural frequency. Additionally, a second voltage amplitude of a second source of excitation can be selected for the device, and the first and second sources of excitation can be applied to the device. After applying the first and second sources of excitation, a frequency of the second source of excitation can be swept. Using the methods of multi- frequency excitation described herein, new operating frequencies, operating frequency ranges, resonance frequencies, resonance frequency ranges, and/or resonance responses can be achieved for devices and systems.

  14. 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.

  15. 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.

  16. a simple a simple excitation control excitation control excitation

    African Journals Online (AJOL)

    eobe

    field voltages determined follow a simple quadratic relationship that offer a very simple control scheme, dependent on only the stator current. Keywords: saturated reactances, no-load field voltage, excitation control, synchronous generators. 1. Introduction. Introduction. Introduction. The commonest generator in use today is ...

  17. Adaptive transition rates in excitable membranes

    Directory of Open Access Journals (Sweden)

    Shimon Marom

    2009-02-01

    Full Text Available Adaptation of activity in excitable membranes occurs over a wide range of timescales. Standard computational approaches handle this wide temporal range in terms of multiple states and related reaction rates emanating from the complexity of ionic channels. The study described here takes a different (perhaps complementary approach, by interpreting ion channel kinetics in terms of population dynamics. I show that adaptation in excitable membranes is reducible to a simple Logistic-like equation in which the essential non-linearity is replaced by a feedback loop between the history of activation and an adaptive transition rate that is sensitive to a single dimension of the space of inactive states. This physiologically measurable dimension contributes to the stability of the system and serves as a powerful modulator of input-output relations that depends on the patterns of prior activity; an intrinsic scale free mechanism for cellular adaptation that emerges from the microscopic biophysical properties of ion channels of excitable membranes.

  18. Triple Giant Resonance Excitations: A Microscopic Approach

    International Nuclear Information System (INIS)

    Lanza, E.G.; Andres, M.V.; Catara, F.; Chomaz, Ph.; Fallot, M.; Scarpaci, J.A.

    2007-01-01

    We present, for the first time, microscopic calculations of inelastic cross sections of the triple excitation of giant resonances induced by heavy ion probes. We start from a microscopic approach based on RPA. The mixing of three-phonon states among themselves and with two- and one-phonon states is considered within a boson expansion with Pauli corrections. In this way we go beyond the standard harmonic approximations and get anharmonic excitation spectra. At the same time we also introduce non-linearities in the external field. The calculations are done by solving semiclassical coupled channel equations, the channels being superpositions of one-, two- and three-phonon states. Previous calculations for the Double Giant Resonance excitation show good agreement with experimental cross sections. The inclusion of the three phonon components confirms the previous results for the DGR and produces a strong increase in the Triple GR energy region

  19. 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.

  20. 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.

  1. Nonlinear Elliptic Differential Equations with Multivalued Nonlinearities

    Indian Academy of Sciences (India)

    In this paper we study nonlinear elliptic boundary value problems with monotone and nonmonotone multivalued nonlinearities. First we consider the case of monotone nonlinearities. In the first result we assume that the multivalued nonlinearity is defined on all R R . Assuming the existence of an upper and of a lower ...

  2. Neural networks and orbit control in accelerators

    International Nuclear Information System (INIS)

    Bozoki, E.; Friedman, A.

    1994-01-01

    An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to 'kicks' and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given

  3. 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...

  4. 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 ...

  5. 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...

  6. 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.

  7. 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

  8. Nonlinear dynamical phenomena in liquid crystals

    International Nuclear Information System (INIS)

    Wang, X.Y.; Sun, Z.M.

    1988-09-01

    Because of the existence of the orientational order and anisotropy in liquid crystals, strong nonlinear phenomena and singular behaviors, such as solitary wave, transient periodic structure, chaos, fractal and viscous fingering, can be excited by a very small disturbance. These phenomena and behaviors are in connection with physics, biology and mathematics. 12 refs, 6 figs

  9. Modeling vector nonlinear time series using POLYMARS

    NARCIS (Netherlands)

    de Gooijer, J.G.; Ray, B.K.

    2003-01-01

    A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector

  10. On isospin excitation energy

    International Nuclear Information System (INIS)

    Li Wenfei; Zhang Fengshou; Chen Liewen

    2001-01-01

    Within the framework of Hartree-Fock theory using the extended Skyrme effective interaction, the isospin excitation energy as a function of relative neutron excess δ was investigated at different temperatures and densities. It was found that the isospin excitation energy decreased with the increment of temperature and/or the decrement of density. The authors pointed out that the decrement of isospin excitation energy was resulted from the weakening of quantum effect with increment of temperature and/or decrement of density. Meanwhile, the relationship between the isospin excitation energy and the symmetry energy was discussed and found that the symmetry energy was just a part of the isospin excitation energy. With increasing temperature and decreasing density, the contribution of the symmetry energy to the isospin excitation energy becomes more and more important. The isospin excitation energy as a function of relative neutron excess was also investigated using different potential parameters. The results shows that the isospin excitation energy is almost independent of the incompressibility and the effective mass, but strongly depends on the symmetry energy strength coefficient, which indicates that it is possible to extract the symmetry energy of the nuclear equation of state by investigating the isospin excitation energy in experiments

  11. Excited states 2

    CERN Document Server

    Lim, Edward C

    2013-01-01

    Excited States, Volume 2 is a collection of papers that deals with molecules in the excited states. The book describes the geometries of molecules in the excited electronic states. One paper describes the geometries of a diatomic molecule and of polyatomic molecules; it also discusses the determination of the many excited state geometries of molecules with two, three, or four atoms by techniques similar to diatomic spectroscopy. Another paper introduces an ordered theory related to excitons in pure and mixed molecular crystals. This paper also presents some experimental data such as those invo

  12. Excited states v.6

    CERN Document Server

    Lim, Edward C

    1982-01-01

    Excited States, Volume 6 is a collection of papers that discusses the excited states of molecules. The first paper discusses the linear polyene electronic structure and potential surfaces, considering both the theoretical and experimental approaches in such electronic states. This paper also reviews the theory of electronic structure and cites some experimental techniques on polyene excitations, polyene spectroscopic phenomenology, and those involving higher states of polyenes and their triplet states. Examples of these experimental studies of excited states involve the high-resolution one-pho

  13. 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

  14. 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.

  15. 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.

  16. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  17. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  18. Nonlinear Ion Harmonics in the Paul Trap with Added Octopole Field: Theoretical Characterization and New Insight into Nonlinear Resonance Effect.

    Science.gov (United States)

    Xiong, Caiqiao; Zhou, Xiaoyu; Zhang, Ning; Zhan, Lingpeng; Chen, Yongtai; Nie, Zongxiu

    2016-02-01

    The nonlinear harmonics within the ion motion are the fingerprint of the nonlinear fields. They are exclusively introduced by these nonlinear fields and are responsible to some specific nonlinear effects such as nonlinear resonance effect. In this article, the ion motion in the quadrupole field with a weak superimposed octopole component, described by the nonlinear Mathieu equation (NME), was studied by using the analytical harmonic balance (HB) method. Good accuracy of the HB method, which was comparable with that of the numerical fourth-order Runge-Kutta (4th RK), was achieved in the entire first stability region, except for the points at the stability boundary (i.e., β = 1) and at the nonlinear resonance condition (i.e., β = 0.5). Using the HB method, the nonlinear 3β harmonic series introduced by the octopole component and the resultant nonlinear resonance effect were characterized. At nonlinear resonance, obvious resonant peaks were observed in the nonlinear 3β series of ion motion, but were not found in the natural harmonics. In addition, both resonant excitation and absorption peaks could be observed, simultaneously. These are two unique features of the nonlinear resonance, distinguishing it from the normal resonance. Finally, an approximation equation was given to describe the corresponding working parameter, q nr , at nonlinear resonance. This equation can help avoid the sensitivity degradation due to the operation of ion traps at the nonlinear resonance condition.

  19. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  20. 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.)

  1. 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...

  2. 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

  3. Nonlinear Elasticity

    Science.gov (United States)

    Fu, Y. B.; Ogden, R. W.

    2001-05-01

    This collection of papers by leading researchers in the field of finite, nonlinear elasticity concerns itself with the behavior of objects that deform when external forces or temperature gradients are applied. This process is extremely important in many industrial settings, such as aerospace and rubber industries. This book covers the various aspects of the subject comprehensively with careful explanations of the basic theories and individual chapters each covering a different research direction. The authors discuss the use of symbolic manipulation software as well as computer algorithm issues. The emphasis is placed firmly on covering modern, recent developments, rather than the very theoretical approach often found. The book will be an excellent reference for both beginners and specialists in engineering, applied mathematics and physics.

  4. Nonlinear resonances

    CERN Document Server

    Rajasekar, Shanmuganathan

    2016-01-01

    This introductory text presents the basic aspects and most important features of various types of resonances and anti-resonances in dynamical systems. In particular, for each resonance, it covers the theoretical concepts, illustrates them with case studies, and reviews the available information on mechanisms, characterization, numerical simulations, experimental realizations, possible quantum analogues, applications and significant advances made over the years. Resonances are one of the most fundamental phenomena exhibited by nonlinear systems and refer to specific realizations of maximum response of a system due to the ability of that system to store and transfer energy received from an external forcing source. Resonances are of particular importance in physical, engineering and biological systems - they can prove to be advantageous in many applications, while leading to instability and even disasters in others. The book is self-contained, providing the details of mathematical derivations and techniques invo...

  5. 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

  6. 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...

  7. 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

  8. Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.

    Science.gov (United States)

    Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N

    2007-07-01

    An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

  9. An efficient nonlinear Feshbach engine

    Science.gov (United States)

    Li, Jing; Fogarty, Thomás; Campbell, Steve; Chen, Xi; Busch, Thomas

    2018-01-01

    We investigate a thermodynamic cycle using a Bose-Einstein condensate (BEC) with nonlinear interactions as the working medium. Exploiting Feshbach resonances to change the interaction strength of the BEC allows us to produce work by expanding and compressing the gas. To ensure a large power output from this engine these strokes must be performed on a short timescale, however such non-adiabatic strokes can create irreversible work which degrades the engine’s efficiency. To combat this, we design a shortcut to adiabaticity which can achieve an adiabatic-like evolution within a finite time, therefore significantly reducing the out-of-equilibrium excitations in the BEC. We investigate the effect of the shortcut to adiabaticity on the efficiency and power output of the engine and show that the tunable nonlinearity strength, modulated by Feshbach resonances, serves as a useful tool to enhance the system’s performance.

  10. 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.

  11. 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...

  12. 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.

  13. 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...

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Utilization of excitation signal harmonics for control of nonlinear systems

    DEFF Research Database (Denmark)

    Vinther, Kasper; Rasmussen, Henrik; Izadi-Zamanabadi, Roozbeh

    2012-01-01

    signal together with Fourier analysis to generate a feedback signal and simulations have shown that different system gains and time constants does not change the global equilibrium/operating point. An evaporator in a refrigeration system was used as example in the simulations, however, it is anticipated...... that the method is applicable in a wide variety of systems satisfying the sigmoid function properties....

  20. Excitations and management of the nonlinear localized gap modes

    Indian Academy of Sciences (India)

    Finally, we discuss about the theory of Feshbach resonance management and dispersion ... Gap soliton; gap compacton; soliton management. ... Introduction ..... BD would like to thank DST and BCUD-PU for financial support through research.

  1. Application of inertia-induced excitation theory for nonlinear acoustic ...

    Indian Academy of Sciences (India)

    estimated. By these calculations, a generalized statement is reported that all possible sound ..... we carry out order-by-order analysis of eqs (9)–(13) in a stretched coordinate system ..... The financial support by the CSIR received through.

  2. 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....

  3. Visualization of neural networks using saliency maps

    DEFF Research Database (Denmark)

    Mørch, Niels J.S.; Kjems, Ulrik; Hansen, Lars Kai

    1995-01-01

    The saliency map is proposed as a new method for understanding and visualizing the nonlinearities embedded in feedforward neural networks, with emphasis on the ill-posed case, where the dimensionality of the input-field by far exceeds the number of examples. Several levels of approximations...

  4. Neural Chaos and Free Will Problem

    Czech Academy of Sciences Publication Activity Database

    Andrey, Ladislav

    1997-01-01

    Roč. 4, č. 1 (1997), s. 23 ISSN 1355-8250. [The Brain and Self Workshop: Toward a Science of Consciousness. Elsinore, 21.08.1997-24.08.1997] R&D Projects: GA ČR GA201/95/0992 Keywords : free will and agency * attention * emotion * neural networks and connectionism * nonlinear dynamics

  5. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  6. 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

  7. 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.

  8. [Nonlinear magnetohydrodynamics

    International Nuclear Information System (INIS)

    1994-01-01

    Resistive MHD equilibrium, even for small resistivity, differs greatly from ideal equilibrium, as do the dynamical consequences of its instabilities. The requirement, imposed by Faraday's law, that time independent magnetic fields imply curl-free electric fields, greatly restricts the electric fields allowed inside a finite-resistivity plasma. If there is no flow and the implications of the Ohm's law are taken into account (and they need not be, for ideal equilibria), the electric field must equal the resistivity times the current density. The vanishing of the divergence of the current density then provides a partial differential equation which, together with boundary conditions, uniquely determines the scalar potential, the electric field, and the current density, for any given resistivity profile. The situation parallels closely that of driven shear flows in hydrodynamics, in that while dissipative steady states are somewhat more complex than ideal ones, there are vastly fewer of them to consider. Seen in this light, the vast majority of ideal MHD equilibria are just irrelevant, incapable of being set up in the first place. The steady state whose stability thresholds and nonlinear behavior needs to be investigated ceases to be an arbitrary ad hoc exercise dependent upon the whim of the investigator, but is determined by boundary conditions and choice of resistivity profile

  9. Parametric excitation of drift waves in a sheared slab geometry

    International Nuclear Information System (INIS)

    Vranjes, J.; Weiland, J.

    1992-01-01

    The threshold for parametric excitation of drift waves in a sheared slab geometry is calculated for a pump wave that is a standing wave along the magnetic field, using the Hasegawa-Mima nonlinearity. The shear damping is counteracted by the parametric coupling and the eigenvalue problem is solved analytically using Taylor's strong coupling approximation. (au)

  10. Harmonic excitations in quasicrystals

    International Nuclear Information System (INIS)

    Luck, J.M.

    1986-03-01

    The harmonic excitations (phonons) of quasicrystals are studied in a simple one-dimensional model. The spectrum is a Cantor set, which exhibits selfsimilarity properties. The eigenstates are generically ''critical'', i.e. neither extended nor localized

  11. Spike Bursts from an Excitable Optical System

    Science.gov (United States)

    Rios Leite, Jose R.; Rosero, Edison J.; Barbosa, Wendson A. S.; Tredicce, Jorge R.

    Diode Lasers with double optical feedback are shown to present power drop spikes with statistical distribution controllable by the ratio of the two feedback times. The average time between spikes and the variance within long time series are studied. The system is shown to be excitable and present bursting of spikes created with specific feedback time ratios and strength. A rate equation model, extending the Lang-Kobayashi single feedback for semiconductor lasers proves to match the experimental observations. Potential applications to construct network to mimic neural systems having controlled bursting properties in each unit will be discussed. Brazilian Agency CNPQ.

  12. Radio frequency plasma excitation

    International Nuclear Information System (INIS)

    Burden, M.St.J.; Cross, K.B.

    1979-01-01

    An investigation into the use of rf sputtering for ion cleaning of insulating substrates before ion plating is reported. Initial experiments consisted of sputtering metals with rf power followed by the deposition of copper onto glass slides using rf plasma excitation and biasing supply. It was found that good quality films were obtained by rf ion plating onto plastics with excellent adhesion over a wide operating pressure range. A block schematic of the rf plasma excitation system is shown. (UK)

  13. High energy nuclear excitations

    International Nuclear Information System (INIS)

    Gogny, D.; Decharge, J.

    1983-09-01

    The main purpose of this talk is to see whether a simple description of the nuclear excitations permits one to characterize some of the high energy structures recently observed. The discussion is based on the linear response to different external fields calculated using the Random Phase Approximation. For those structure in heavy ion collisions at excitation energies above 50 MeV which cannot be explained with such a simple approach, we discuss a possible mechanism for this heavy ion scattering

  14. Dynamics of nonlinear feedback control.

    Science.gov (United States)

    Snippe, H P; van Hateren, J H

    2007-05-01

    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 steps, the dynamics of gain and attenuation can be very different, depending on the mathematical form of the nonlinearity and the ordering of the nonlinearity and the filtering in the feedback loop. Further, the dynamics of feedback control can be strongly asymmetrical for increment versus decrement steps of the input. Nevertheless, for each of the models studied, the nonlinearity in the feedback loop can be chosen such that immediately after an input step, the dynamics of feedback control is symmetric with respect to increments versus decrements. Finally, we study the dynamics of the output of the control loops and find conditions under which overshoots and undershoots of the output relative to the steady-state output occur when the models are stimulated with low-pass filtered steps. For small steps at the input, overshoots and undershoots of the output do not occur when the filtering in the control path is faster than the low-pass filtering at the input. For large steps at the input, however, results depend on the model, and for some of the models, multiple overshoots and undershoots can occur even with a fast control path.

  15. 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.

  16. 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

  17. Nonlinear dissipative devices in structural vibration control: A review

    Science.gov (United States)

    Lu, Zheng; Wang, Zixin; Zhou, Ying; Lu, Xilin

    2018-06-01

    Structural vibration is a common phenomenon existing in various engineering fields such as machinery, aerospace, and civil engineering. It should be noted that the effective suppression of structural vibration is conducive to enhancing machine performance, prolonging the service life of devices, and promoting the safety and comfort of structures. Conventional linear energy dissipative devices (linear dampers) are largely restricted for wider application owing to their low performance under certain conditions, such as the detuning effect of tuned mass dampers subjected to nonstationary excitations and the excessively large forces generated in linear viscous dampers at high velocities. Recently, nonlinear energy dissipative devices (nonlinear dampers) with broadband response and high robustness are being increasingly used in practical engineering. At the present stage, nonlinear dampers can be classified into three groups, namely nonlinear stiffness dampers, nonlinear-stiffness nonlinear-damping dampers, and nonlinear damping dampers. Corresponding to each nonlinear group, three types of nonlinear dampers that are widely utilized in practical engineering are reviewed in this paper: the nonlinear energy sink (NES), particle impact damper (PID), and nonlinear viscous damper (NVD), respectively. The basic concepts, research status, engineering applications, and design approaches of these three types of nonlinear dampers are summarized. A comparison between their advantages and disadvantages in practical engineering applications is also conducted, to provide a reference source for practical applications and new research.

  18. Numerical simulations of convectively excited gravity waves

    International Nuclear Information System (INIS)

    Glatzmaier, G.A.

    1983-01-01

    Magneto-convection and gravity waves are numerically simulated with a nonlinear, three-dimensional, time-dependent model of a stratified, rotating, spherical fluid shell heated from below. A Solar-like reference state is specified while global velocity, magnetic field, and thermodynamic perturbations are computed from the anelastic magnetohydrodynamic equations. Convective overshooting from the upper (superadiabatic) part of the shell excites gravity waves in the lower (subadiabatic) part. Due to differential rotation and Coriolis forces, convective cell patterns propagate eastward with a latitudinally dependent phase velocity. The structure of the excited wave motions in the stable region is more time-dependent than that of the convective motions above. The magnetic field tends to be concentrated over giant-cell downdrafts in the convective zone but is affected very little by the wave motion in the stable region

  19. 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)

  20. Nonlinear beam mechanics

    NARCIS (Netherlands)

    Westra, H.J.R.

    2012-01-01

    In this Thesis, nonlinear dynamics and nonlinear interactions are studied from a micromechanical point of view. Single and doubly clamped beams are used as model systems where nonlinearity plays an important role. The nonlinearity also gives rise to rich dynamic behavior with phenomena like

  1. 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.

  2. 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

  3. 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.

  4. 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...

  5. 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...

  6. 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.

  7. Workshop on Nonlinear Phenomena in Complex Systems

    CERN Document Server

    1989-01-01

    This book contains a thorough treatment of neural networks, cellular-automata and synergetics, in an attempt to provide three different approaches to nonlinear phenomena in complex systems. These topics are of major interest to physicists active in the fields of statistical mechanics and dynamical systems. They have been developed with a high degree of sophistication and include the refinements necessary to work with the complexity of real systems as well as the more recent research developments in these areas.

  8. Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

    Science.gov (United States)

    Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

    2015-05-01

    This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

  9. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

    Science.gov (United States)

    Khan, Muhammad Yousaf; Mittnik, Stefan

    2018-01-01

    In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

  10. Theory and design of nonlinear metamaterials

    Science.gov (United States)

    Rose, Alec Daniel

    and oscillators. By applying this set of tools and knowledge to microwave metamaterials, I experimentally confirm several novel nonlinear phenomena. Most notably, I construct a backward wave nonlinear medium from varactor-loaded split ring resonators loaded in a rectangular waveguide, capable of generating second-harmonic opposite to conventional nonlinear materials with a conversion efficiency as high as 1.5%. In addition, I confirm nonlinear magnetoelectric coupling in two dual gap varactor-loaded split ring resonator metamaterials through measurement of the amplitude and phase of the second-harmonic generated in the forward and backward directions from a thin slab. I then use the presence of simultaneous nonlinearities in such metamaterials to observe nonlinear interference, manifest as unidirectional difference frequency generation with contrasts of 6 and 12 dB in the forward and backward directions, respectively. Finally, I apply these principles and intuition to several plasmonic platforms with the goal of achieving similar enhancements and configurations at optical frequencies. Using the example of fluorescence enhancement in optical patch antennas, I develop a semi-classical numerical model for the calculation of field-induced enhancements to both excitation and spontaneous emission rates of an embedded fluorophore, showing qualitative agreement with experimental results, with enhancement factors of more than 30,000. Throughout these series of works, I emphasize the indispensability of effective design and retrieval tools in understanding and optimizing both metamaterials and plasmonic systems. Ultimately, when weighed against the disadvantages in fabrication and optical losses, the results presented here provide a context for the application of nonlinear metamaterials within three distinct areas where a competitive advantage over conventional materials might be obtained: fundamental science demonstrations, linear and nonlinear anisotropy engineering, and

  11. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  12. 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

  13. Giant resonances on excited states

    International Nuclear Information System (INIS)

    Besold, W.; Reinhard, P.G.; Toepffer, C.

    1984-01-01

    We derive modified RPA equations for small vibrations about excited states. The temperature dependence of collective excitations is examined. The formalism is applied to the ground state and the first excited state of 90 Zr in order to confirm a hypothesis which states that not only the ground state but every excited state of a nucleus has a giant resonance built upon it. (orig.)

  14. Local Dynamics in Trained Recurrent Neural Networks.

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-23

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  15. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  16. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

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

  17. Local Dynamics in Trained Recurrent Neural Networks

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-01

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  18. NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

    Directory of Open Access Journals (Sweden)

    Sergey A. Sannikov

    2017-03-01

    Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

  19. Excitation of Nucleon Resonances

    International Nuclear Information System (INIS)

    Burkert, Volker D.

    2001-01-01

    I discuss developments in the area of nucleon resonance excitation, both necessary and feasible, that would put our understanding of nucleon structure in the regime of strong QCD on a qualitatively new level. They involve the collection of high quality data in various channels, a more rigorous approach in the search for ''missing'' resonances, an effort to compute some critical quantities in nucleon resonance excitations from first principles, i.e. QCD, and a proposal focused to obtain an understanding of a fundamental quantity in nucleon structure

  20. Seismic response of cable stayed bridges under multi support excitation

    Directory of Open Access Journals (Sweden)

    Mahmoud Reza ُُShiravand

    2017-07-01

    Full Text Available In this Study, the seismic response of cable stayed bridges have been evaluated under multi-support excitations. There are three sources that cause the earthquake wave characteristics change during its propagation path. Local site effect, loss of coherency and wave passage effect are three sources of spatial variation of seismic ground motions. In long span structures, such as cable supported bridges, this phenomenon is more evident and traditional analyzing (uniform excitation may not be valid and be conservative. Thus, it is necessary to investigate the response of cable stayed bridges under non-uniform excitations. For this purpose, the non-uniform time histories were artificially generated using Kriging method based on a set of known time history in the west support of bridge. Nonlinear time history analysis was performed and cables axial force, deck moment, pylons moment and finally drift ratio of bridge have been examined in order to investigate how non-uniform excitation change the seismic response of bridge compared with uniform excitations. Results show non-uniform excitation in some bridge components increase responses and decreases in the others. In non-uniform excitation, although total time history energy is lesser than uniform excitation, it can significantly change the distribution of the forces and makes differential displacement between cables supports and increase the possibility of failure.