101,150 research outputs found
Complex Dynamics and Synchronization of Delayed-Feedback Nonlinear Oscillators
We describe a flexible and modular delayed-feedback nonlinear oscillator that
is capable of generating a wide range of dynamical behaviours, from periodic
oscillations to high-dimensional chaos. The oscillator uses electrooptic
modulation and fibre-optic transmission, with feedback and filtering
implemented through real-time digital-signal processing. We consider two such
oscillators that are coupled to one another, and we identify the conditions
under which they will synchronize. By examining the rates of divergence or
convergence between two coupled oscillators, we quantify the maximum Lyapunov
exponents or transverse Lyapunov exponents of the system, and we present an
experimental method to determine these rates that does not require a
mathematical model of the system. Finally, we demonstrate a new adaptive
control method that keeps two oscillators synchronized even when the coupling
between them is changing unpredictably.Comment: 24 pages, 13 figures. To appear in Phil. Trans. R. Soc. A (special
theme issue to accompany 2009 International Workshop on Delayed Complex
Systems
A new class of wavelet networks for nonlinear system identification
A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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