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Dynamic characterization of hysteresis elements in mechanical systems. I. Theoretical analysis.

The pre-sliding-pre-rolling phase of friction behavior is dominated by rate-independent hysteresis. Many machine elements in common engineering use exhibit, therefore, the characteristic of "hysteresis springs," for small displacements at least. Plain and rolling element bearings that are widely used in motion guidance of machine tools are typical examples. While the presence of a hysteresis element may mark the character of the resulting dynamics, little is to be found about this topic in the literature. The study of the nonlinear dynamics caused by such elements becomes imperative if we wish to achieve accurate control of such machines. In this Part I of the investigation, we examine a single-degree-of-freedom mass-hysteresis-spring system and show that, while the free response case is amenable to an exact solution, the more important case of forced response has no closed form solution and requires other methods of treatment. We consider harmonic-balance analysis methods (which are common analysis tools in engineering) suitable for frequency-domain treatment, in particular the approximate describing function (DF) method, and compare those results with "exact" numerical simulations. The DF method yields basically a linear equation with amplitude-dependent modal parameters. We find that agreement in the frequency response function, between DF and exact solution, is good for small excitation amplitudes and for very large amplitudes. Intermediate values, however, show high sensitivity to amplitude variations and, consequently, no regular solution is obtainable by either approach. This appears to be an inherent property of the system pointing to the need for developing further analysis methods. Experimental verification of the analysis outlined in this Part I is given in Part II of the paper.

Fourier Analysis↗

Time scale dependence of human brain dynamics.

It is shown that variation of Kolmogorov entropy is significantly sensitive to the choice of time lag during epileptic conditions. This clearly indicates that the human brain dynamics consist of different time scales and they are very crucial in the study of various disorders of brain from a nonlinear dynamical point of view.

Brain↗

Oscillations and oscillatory behavior in small neural circuits.

In order to determine the dynamical properties of central pattern generators (CPGs), we have examined the lobster stomatogastric ganglion using the tools of nonlinear dynamics. The lobster pyloric and gastric mill central pattern generators can be analyzed at both the cellular and network levels because they are small, i.e., contain only 25 neurons between them and each neuron and synapse are repeatedly identifiable from animal to animal. We discuss how the biophysical properties of each neuron and synapse in the two circuits act cooperatively to generate two different patterns of sequential activity, how these patterns are altered by neuromodulators and perturbed by noise and sensory inputs. Finally, we show how simplified Hindmarsh-Rose models can be made into analog electronic neurons that mimic the lobster neurons and in addition be incorporated into artificial CPGs with robotic applications.

Animals↗

Prediction of long-term dynamics from transients.

Existing methods of time series analysis of nonlinear dynamical systems deal with the dynamics on single (strange) attractors. We extend this method for systems that can be externally manipulated. We interact with a previously unknown system by perturbing it randomly and recording the responses. By following transients for a short time and approximating the global flow, we can predict the final long-term dynamics, including coexisting multistable, chaotic, or periodic dynamics. Also the localization of basins of attraction is possible, enabling the selection or control of the type of final dynamics. Numerical and experimental examples of driven nonlinear oscillators are given.

Journal Article↗

Determination of largest Lyapunov exponents of vocal signal: application to unilateral laryngeal paralysis.

It has been shown that voice signal abnormalities, particularly in unilateral laryngeal paralysis (ULP), are not always randomly distributed and that "statistical" indexes of regularity, such as jitter and shimmer, may be unreliable in these cases. The techniques of nonlinear dynamics, particularly phase portraits, have been used to demonstrate that some of the abnormalities observed were the consequence of nonlinearity of glottic function. From a theoretical point of view, determination of Lyapunov exponents allows quantification of the complexity of the phase portraits. The authors studied vocal signals recorded in 12 normal subjects and 26 patients with ULP and calculated the largest Lyapunov exponent (LLE). In normal subjects, LLE mean value was 0.380 (SD = 0.182). In patients with ULP, LLE mean value was 0.570 (SD = 0.337). The difference is significant at P = 0.031. Determination of LLE was compared to more "classical" indexes such as jitter and oral airflow during phonation. A principal component analysis showed that information contained in LLE was not redundant but complementary to the other parameters.

Adult↗

Prediction of the oculocardiac reflex from pre-operative linear and nonlinear heart rate dynamics in children.

This study was aimed to determine whether pre-operatively measured linear and nonlinear analysis of heart rate variability might predict the occurrence of the oculocardiac reflex (OCR) or other arrhythmia during strabismus surgery in children (n = 185, mean (SD) age = 8.09 (3.31) years). We compared time- and frequency-domain, and nonlinear dynamic indices of pre-operatively measured RR interval data between the OCR-positive group (maximum heart rate decrement = 20 beat.min-1 during the traction of extraocular muscle, n = 54), OCR-negative group (< 20 beat x min(-1), n = 78) and arrhythmia-positive group (all other arrhythmias; n = 53). pNN50, rMSSD, high-frequency power and nonlinear prediction error were significantly lower in the OCR-positive and arrhythmia-positive groups than in the OCR-negative group. Discriminant analysis using these indices could correctly identify 39/54 (72.2%) OCR-positive patients. Some pre-operatively measured indices of linear and nonlinear heart rate variability, especially when used in combination, are valuable for predicting significant bradycardia during strabismus surgery in children.

Adolescent↗

Dynamics of large-scale brain activity in normal arousal states and epileptic seizures.

Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms-mainly slow wave, delta, theta, alpha, and sleep spindle-with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

Adult↗

A coupled oscillator model of disordered interlimb coordination in patients with Parkinson's disease.

Coordination between the left and right limbs during cyclic movements, which can be characterized by the amplitude of each limb's oscillatory movement and relative phase, is impaired in patients with Parkinson's disease (PD). A pedaling exercise on an ergometer in a recent clinical study revealed several types of coordination disorder in PD patients. These include an irregular and burst-like amplitude modulation with intermittent changes in its relative phase, a typical sign of chaotic behavior in nonlinear dynamical systems. This clinical observation leads us to hypothesize that emergence of the rhythmic motor behaviors might be concerned with nonlinearity of an underlying dynamical system. In order to gain insight into this hypothesis, we consider a simple hard-wired central pattern generator model consisting of two identical oscillators connected by reciprocal inhibition. In the model, each oscillator acts as a neural half-center controlling movement of a single limb, either left or right, and receives a control input modeling a flow of descending signals from higher motor centers. When these two control inputs are tonic-constant and identical, the model has left-right symmetry and basically exhibits ordered coordination with an alternating periodic oscillation. We show that, depending on the intensities of these two control inputs and on the difference between them that introduces asymmetry into the model, the model can reproduce several behaviors observed in the clinical study. Bifurcation analysis of the model clarifies two possible mechanisms for the generation of disordered coordination in the model: one is the spontaneous symmetry-breaking bifurcation in the model with the left-right symmetry. The other is related to the degree of asymmetry reflecting the difference between the two control inputs. Finally, clinical implications by the model's dynamics are briefly discussed.

Ataxia↗

Cardiac interbeat interval dynamics from childhood to senescence : comparison of conventional and new measures based on fractals and chaos theory.

BACKGROUND: New methods of R-R interval variability based on fractal scaling and nonlinear dynamics ("chaos theory") may give new insights into heart rate dynamics. The aims of this study were to (1) systematically characterize and quantify the effects of aging from early childhood to advanced age on 24-hour heart rate dynamics in healthy subjects; (2) compare age-related changes in conventional time- and frequency-domain measures with changes in newly derived measures based on fractal scaling and complexity (chaos) theory; and (3) further test the hypothesis that there is loss of complexity and altered fractal scaling of heart rate dynamics with advanced age. METHODS AND RESULTS: The relationship between age and cardiac interbeat (R-R) interval dynamics from childhood to senescence was studied in 114 healthy subjects (age range, 1 to 82 years) by measurement of the slope, beta, of the power-law regression line (log power-log frequency) of R-R interval variability (10(-4) to 10(-2) Hz), approximate entropy (ApEn), short-term (alpha(1)) and intermediate-term (alpha(2)) fractal scaling exponents obtained by detrended fluctuation analysis, and traditional time- and frequency-domain measures from 24-hour ECG recordings. Compared with young adults (<40 years old, n=29), children (<15 years old, n=27) showed similar complexity (ApEn) and fractal correlation properties (alpha(1), alpha(2), beta) of R-R interval dynamics despite lower spectral and time-domain measures. Progressive loss of complexity (decreased ApEn, r=-0.69, P<0.001) and alterations of long-term fractal-like heart rate behavior (increased alpha(2), r=0.63, decreased beta, r=-0.60, P<0.001 for both) were observed thereafter from middle age (40 to 60 years, n=29) to old age (>60 years, n=29). CONCLUSIONS: Cardiac interbeat interval dynamics change markedly from childhood to old age in healthy subjects. Children show complexity and fractal correlation properties of R-R interval time series comparable to those of young adults, despite lower overall heart rate variability. Healthy aging is associated with R-R interval dynamics showing higher regularity and altered fractal scaling consistent with a loss of complex variability.

Adolescent↗

Genetic influences on dynamic complexity of brain oscillations.

Human electroencephalogram (EEG) consists of complex aperiodic oscillations that are assumed to indicate underlying neural dynamics such as the number and degree of independence of oscillating neuronal networks. EEG complexity can be estimated using measures derived from nonlinear dynamic systems theory. Variations in such measures have been shown to be associated with normal individual differences in cognition and some neuropsychiatric disorders. Despite the increasing use of EEG complexity measures for the study of normal and abnormal brain functioning, little is known about genetic and environmental influences on these measures. Using the pointwise dimension (PD2) algorithm, this study assessed heritability of EEG complexity at rest in a sample of 214 young female twins consisting of 51 monozygotic (MZ) and 56 dizygotic (DZ) pairs. In MZ twins, intrapair correlations were high and statistically significant; in DZ twins, correlations were substantially smaller. Genetic analyses using linear structural equation modeling revealed high and significant heritability of EEG complexity: 62-68% in the eyes-closed condition, and 46-60% in the eyes-open condition. Results suggest that individual differences in the complexity of resting electrocortical dynamics are largely determined by genetic factors. Neurophysiological mechanisms mediating genetic variation in EEG complexity may include the degree of structural connectivity and functional differentiation among cortical neuronal assemblies.

Adolescent↗

Schizophrenia as a dynamical disease.

It is demonstrated that schizophrenia is a dynamical disease, i. e. that important aspects of schizophrenia can be understood on the basis of concepts of the theory of nonlinear dynamical systems. In particular, the gradual shift of a single parameter may result in completely different kinds of behavior of the entire system just like water can be in three qualitatively different states depending on the single parameter temperature. Transitions between these different states are called bifurcations or phase transitions. In case of schizophrenia the parameter may be the neurotransmitter dopamine (or serotonin or glutamate). With a high level of dopamine transmission the symptoms of schizophrenia appear, and with rather low levels those of Morbus Parkinson. In between a healthy state prevails. With respect to neuronal activity the effects of dopamine are demonstrated by a mathematical model which can be interpreted in two ways: on the one hand as a model of typical excitatory-inhibitory circuits in the cortex, on the other hand of a negative feedback loop between thalamus, prefrontal cortex and striatum. The model exhibits different types of firing patterns and their bifurcation, from various kinds of periodicities up to erratic or chaotic behavior, corresponding to different levels of dopamine concentration.

Animals↗

Understanding dynamic state changes in temporal lobe epilepsy.

The authors review their work in applying nonlinear dynamics to predict onset of seizures in patients with medically refractory temporal lobe epilepsy. The underlying mathematical methodology is presented in some detail. To illustrate their approach, they present an extensive discussion of the analysis of preictal data from two seizures of one patient, and from one disease-free subject. They find similar behavior in some nonlinear measures across seizures, which suggests the possibility of forming a robust method of seizure prediction. However, despite clinical and electrographic preictal and ictal similarity, they have also found marked heterogeneity in other nonlinear measures of preictal activity across seizures arising out of stage 2 nonrapid eye movement sleep. The underlying basis for this variation remains uncertain and needs to be the subject of further intense study to gain a better understanding of the dynamic basis of epilepsy. The origin of these heterogeneities may or may not be related to the much larger differences in nonlinear measures between patients and disease-free subjects. To understand these differences, the authors think it is crucial to pay close attention to potentially confounding factors such as behavioral and other state changes, and to study and report in detail the ways in which relevant nonlinear measures behave in the presence of such changes, independent of seizure onset.

Adolescent↗

Chaos in neuronal networks with balanced excitatory and inhibitory activity.

Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically. Such a balance emerges naturally in large networks of excitatory and inhibitory neuronal populations that are sparsely connected by relatively strong synapses. The resulting state is characterized by strongly chaotic dynamics, even when the external inputs to the network are constant in time. Such a network exhibits a linear response, despite the highly nonlinear dynamics of single neurons, and reacts to changing external stimuli on time scales much smaller than the integration time constant of a single neuron.

Animals↗

Stable adaptive neurocontrol for nonlinear discrete-time systems.

This paper presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network weight adaptation law is derived from Lyapunov stability analysis and the connection between convergence of the network weight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed-loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach.

Neural Networks, Computer↗

Current oscillation and chaotic dynamics in superlattices driven by crossed electric and magnetic fields.

We have theoretically studied current oscillation and chaotic dynamics in doped GaAsAlAs superlattices driven by crossed electric and magnetic fields. When the superlattice system is driven by a dc voltage, a stationary or dynamic electric-field domain can be obtained. We carefully studied the electric-field-domain dynamics and current self-oscillation which both display different modes with the change of magnetic field. When an ac electric field is also applied to the superlattice, a typical nonlinear dynamic system is constructed with the ac amplitude, ac frequency, and magnetic field as the control parameters. Different nonlinear behaviors show up when we tune the control parameters.

Electromagnetic Fields↗

Pathological analysis of myocardial cell under ventricular tachycardia and fibrillation based on symbolic dynamics.

Ventricular fibrillation (VF) is one of the most serious malignant arrhythmias usually resulting from immediate degeneration of ventricular tachycardia (VT). In order to analyse the nonlinear dynamics of the cardiac micro-mechanism under VT and VT rhythm, at the cellular level, myocardial cell action potentials are investigated under different rhythm, normal sinus rhythm, VT and VT. On the basis of nonlinear chaotic theory and symbolic dynamics, we put forward new definitions, complexity rate, etc, and obtained some useful properties for cellular electrophysiological analysis. The results of the experiments and computation show that the myocardial cellular signals under VT and VF rhythm are different kinds of chaotic signals in that the cardiac chaos attractor under VF is higher than that under VT. The analytical complexity theory has been promising in the clinical application.

Action Potentials↗

Unnatural selection: talent identification and development in sport.

The early identification of talented individuals has become increasingly important across many performance domains. Current talent identification (TI) schemes in sport typically select on the basis of discrete, unidimensional measures at unstable periods in the athlete's development. In this article, the concept of talent is revised as a complex, dynamical system in which future behaviors emerge from an interaction of key performance determinants such as psychological behaviors, motor abilities, and physical characteristics. Key nonlinear dynamics concepts are related to TI approaches such as sensitivity to initial conditions, transitions, and exponential behavioral distributions. It is concluded that many TI models place an overemphasis on early identification rather than the development of potentially talented performers. A generic model of talent identification and development is proposed that addresses these issues and provides direction for future research.

Achievement↗

[Recent advances in multi-variate and multi-dimensional analysis of heart rate variability and blood pressure variability].

Heart rate variability (HRV) and blood pressure variability (BPV) reflect the modulation activity of autonomic nervous system on cardiovascular function. There have been important advances in multi-variate, multi-dimensional and dynamic analysis of HRV and BPV during recent years. Modern signal processing methods, such as multi-variate system identification, time-frequency analysis and nonlinear dynamics analysis, could be adopted in the risk prediction, severity evaluation and therapeutic treatment assessment of cardiovascular diseases. These new methods might also be useful in special environmental medicine.

Aerospace Medicine↗