7,630 research outputs found
Connections Between Adaptive Control and Optimization in Machine Learning
This paper demonstrates many immediate connections between adaptive control
and optimization methods commonly employed in machine learning. Starting from
common output error formulations, similarities in update law modifications are
examined. Concepts in stability, performance, and learning, common to both
fields are then discussed. Building on the similarities in update laws and
common concepts, new intersections and opportunities for improved algorithm
analysis are provided. In particular, a specific problem related to higher
order learning is solved through insights obtained from these intersections.Comment: 18 page
Contracting Nonlinear Observers: Convex Optimization and Learning from Data
A new approach to design of nonlinear observers (state estimators) is
proposed. The main idea is to (i) construct a convex set of dynamical systems
which are contracting observers for a particular system, and (ii) optimize over
this set for one which minimizes a bound on state-estimation error on a
simulated noisy data set. We construct convex sets of continuous-time and
discrete-time observers, as well as contracting sampled-data observers for
continuous-time systems. Convex bounds for learning are constructed using
Lagrangian relaxation. The utility of the proposed methods are verified using
numerical simulation.Comment: conference submissio
Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach
Superposition of sigmoid function over a finite time interval is shown to be
equivalent to the linear combination of the solutions of a linearly
parameterized system of logistic differential equations. Due to the linearity
with respect to the parameters of the system, it is possible to design an
effective procedure for parameter adjustment. Stability properties of this
procedure are analyzed. Strategies shown in earlier studies to facilitate
learning such as randomization of a learning sequence and adding specially
designed disturbances during the learning phase are requirements for
guaranteeing convergence in the learning scheme proposed.Comment: 30 pages, 7 figure
Discrete and Continuous-time Soft-Thresholding with Dynamic Inputs
There exist many well-established techniques to recover sparse signals from
compressed measurements with known performance guarantees in the static case.
However, only a few methods have been proposed to tackle the recovery of
time-varying signals, and even fewer benefit from a theoretical analysis. In
this paper, we study the capacity of the Iterative Soft-Thresholding Algorithm
(ISTA) and its continuous-time analogue the Locally Competitive Algorithm (LCA)
to perform this tracking in real time. ISTA is a well-known digital solver for
static sparse recovery, whose iteration is a first-order discretization of the
LCA differential equation. Our analysis shows that the outputs of both
algorithms can track a time-varying signal while compressed measurements are
streaming, even when no convergence criterion is imposed at each time step. The
L2-distance between the target signal and the outputs of both discrete- and
continuous-time solvers is shown to decay to a bound that is essentially
optimal. Our analyses is supported by simulations on both synthetic and real
data.Comment: 18 pages, 7 figures, journa
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
- …