3,075 research outputs found
Synchronization and Noise: A Mechanism for Regularization in Neural Systems
To learn and reason in the presence of uncertainty, the brain must be capable
of imposing some form of regularization. Here we suggest, through theoretical
and computational arguments, that the combination of noise with synchronization
provides a plausible mechanism for regularization in the nervous system. The
functional role of regularization is considered in a general context in which
coupled computational systems receive inputs corrupted by correlated noise.
Noise on the inputs is shown to impose regularization, and when synchronization
upstream induces time-varying correlations across noise variables, the degree
of regularization can be calibrated over time. The proposed mechanism is
explored first in the context of a simple associative learning problem, and
then in the context of a hierarchical sensory coding task. The resulting
qualitative behavior coincides with experimental data from visual cortex.Comment: 32 pages, 7 figures. under revie
Audio Classification from Time-Frequency Texture
Time-frequency representations of audio signals often resemble texture
images. This paper derives a simple audio classification algorithm based on
treating sound spectrograms as texture images. The algorithm is inspired by an
earlier visual classification scheme particularly efficient at classifying
textures. While solely based on time-frequency texture features, the algorithm
achieves surprisingly good performance in musical instrument classification
experiments
Predictive Second Order Sliding Control of Constrained Linear Systems with Application to Automotive Control Systems
This paper presents a new predictive second order sliding controller (PSSC)
formulation for setpoint tracking of constrained linear systems. The PSSC
scheme is developed by combining the concepts of model predictive control (MPC)
and second order discrete sliding mode control. In order to guarantee the
feasibility of the PSSC during setpoint changes, a virtual reference variable
is added to the PSSC cost function to calculate the closest admissible set
point. The states of the system are then driven asymptotically to this
admissible setpoint by the control action of the PSSC. The performance of the
proposed PSSC is evaluated for an advanced automotive engine case study, where
a high fidelity physics-based model of a reactivity controlled compression
ignition (RCCI) engine is utilized to serve as the virtual test-bed for the
simulations. Considering the hard physical constraints on the RCCI engine
states and control inputs, simultaneous tracking of engine load and optimal
combustion phasing is a challenging objective to achieve. The simulation
results of testing the proposed PSSC on the high fidelity RCCI model show that
the developed predictive controller is able to track desired engine load and
combustion phasing setpoints, with minimum steady state error, and no
overshoot. Moreover, the simulation results confirm the robust tracking
performance of the PSSC during transient operations, in the presence of engine
cyclic variability.Comment: 6 pages, 5 figures, 2018 American Control Conferance (ACC), June
27-29, 2018, Milwaukee, WI, USA. [Accepted in Jan. 2018
Contraction analysis of nonlinear random dynamical systems
In order to bring contraction analysis into the very fruitful and topical
fields of stochastic and Bayesian systems, we extend here the theory describes
in \cite{Lohmiller98} to random differential equations. We propose new
definitions of contraction (almost sure contraction and contraction in mean
square) which allow to master the evolution of a stochastic system in two
manners. The first one guarantees eventual exponential convergence of the
system for almost all draws, whereas the other guarantees the exponential
convergence in of to a unique trajectory. We then illustrate the relative
simplicity of this extension by analyzing usual deterministic properties in the
presence of noise. Specifically, we analyze stochastic gradient descent, impact
of noise on oscillators synchronization and extensions of combination
properties of contracting systems to the stochastic case. This is a first step
towards combining the interesting and simplifying properties of contracting
systems with the probabilistic approach.Comment: No. RR-8368 (2013
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