924 research outputs found
Invariant template matching in systems with spatiotemporal coding: a vote for instability
We consider the design of a pattern recognition that matches templates to
images, both of which are spatially sampled and encoded as temporal sequences.
The image is subject to a combination of various perturbations. These include
ones that can be modeled as parameterized uncertainties such as image blur,
luminance, translation, and rotation as well as unmodeled ones. Biological and
neural systems require that these perturbations be processed through a minimal
number of channels by simple adaptation mechanisms. We found that the most
suitable mathematical framework to meet this requirement is that of weakly
attracting sets. This framework provides us with a normative and unifying
solution to the pattern recognition problem. We analyze the consequences of its
explicit implementation in neural systems. Several properties inherent to the
systems designed in accordance with our normative mathematical argument
coincide with known empirical facts. This is illustrated in mental rotation,
visual search and blur/intensity adaptation. We demonstrate how our results can
be applied to a range of practical problems in template matching and pattern
recognition.Comment: 52 pages, 12 figure
Adaptive observers for nonlinearly parameterized systems subjected to parametric constraints
We consider the problem of adaptive observer design in the settings when the
system is allowed to be nonlinear in the parameters, and furthermore they are
to satisfy additional feasibility constraints. A solution to the problem is
proposed that is based on the idea of universal observers and non-uniform
small-gain theorem. The procedure is illustrated with an example.Comment: 19th IFAC World Congress on Automatic Control, 10869-10874, South
Africa, Cape Town, 24th-29th August, 201
Adaptive Observer for Nonlinearly Parameterised Hammerstein System with Sensor Delay – Applied to Ship Emissions Reduction
Taking offspring in a problem of ship emission reduction by exhaust gas recirculation control for large diesel engines, an underlying generic estimation challenge is formulated as a problem of joint state and parameter estimation for a class of multiple-input single-output Hammerstein systems with first order dynamics, sensor delay and a bounded time-varying parameter in the nonlinear part. The paper suggests a novel scheme for this estimation problem that guarantees exponential convergence to an interval that depends on the sensitivity of the system. The system is allowed to be nonlinear parameterized and time dependent, which are characteristics of the industrial problem we study. The approach requires the input nonlinearity to be a sector nonlinearity in the time-varying parameter. Salient features of the approach include simplicity of design and implementation. The efficacy of the adaptive observer is shown on simulated cases, on tests with a large diesel engine on test bed and on tests with a container vessel
Adaptive Observers and Parameter Estimation for a Class of Systems Nonlinear in the Parameters
We consider the problem of asymptotic reconstruction of the state and
parameter values in systems of ordinary differential equations. A solution to
this problem is proposed for a class of systems of which the unknowns are
allowed to be nonlinearly parameterized functions of state and time.
Reconstruction of state and parameter values is based on the concepts of weakly
attracting sets and non-uniform convergence and is subjected to persistency of
excitation conditions. In absence of nonlinear parametrization the resulting
observers reduce to standard estimation schemes. In this respect, the proposed
method constitutes a generalization of the conventional canonical adaptive
observer design.Comment: Preliminary version is presented at the 17-th IFAC World Congress,
6-11 Seoul, 200
Observers for canonic models of neural oscillators
We consider the problem of state and parameter estimation for a wide class of
nonlinear oscillators. Observable variables are limited to a few components of
state vector and an input signal. The problem of state and parameter
reconstruction is viewed within the classical framework of observer design.
This framework offers computationally-efficient solutions to the problem of
state and parameter reconstruction of a system of nonlinear differential
equations, provided that these equations are in the so-called adaptive observer
canonic form. We show that despite typical neural oscillators being locally
observable they are not in the adaptive canonic observer form. Furthermore, we
show that no parameter-independent diffeomorphism exists such that the original
equations of these models can be transformed into the adaptive canonic observer
form. We demonstrate, however, that for the class of Hindmarsh-Rose and
FitzHugh-Nagumo models, parameter-dependent coordinate transformations can be
used to render these systems into the adaptive observer canonical form. This
allows reconstruction, at least partially and up to a (bi)linear
transformation, of unknown state and parameter values with exponential rate of
convergence. In order to avoid the problem of only partial reconstruction and
to deal with more general nonlinear models in which the unknown parameters
enter the system nonlinearly, we present a new method for state and parameter
reconstruction for these systems. The method combines advantages of standard
Lyapunov-based design with more flexible design and analysis techniques based
on the non-uniform small-gain theorems. Effectiveness of the method is
illustrated with simple numerical examples
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