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Results 1 - 10
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98
Multi-Scale Switching Linear Dynamical Systems
, 2003
"... Switching linear dynamic systems can monitor systems that operate in different regimes. In this article we introduce a class of multiscale switching linear dynamical systems that are particularly suited if such regimes form a hierarchy. The setup consists of a specific switching linear dynamical sys ..."
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Switching linear dynamic systems can monitor systems that operate in different regimes. In this article we introduce a class of multiscale switching linear dynamical systems that are particularly suited if such regimes form a hierarchy. The setup consists of a specific switching linear dynamical
Variational learning for switching state-space models
- Neural Computation
, 1998
"... We introduce a new statistical model for time series which iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models -- hidden Ma ..."
Abstract
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Cited by 173 (5 self)
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Markov models and linear dynamical systems -- and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs et al., 1991) to its fully dynamical version, in which both expert and gating
Switching State-Space Models
- King’s College Road, Toronto M5S 3H5
, 1996
"... We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time se ..."
Abstract
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Cited by 46 (2 self)
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series models---the hidden Markov model and the linear dynamical system---and is related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network model (Jacobs et al., 1991) to its fully dynamical version
Non-linear Functional Approximation of Heterogeneous Dynamics
, 2005
"... In modeling phenomena continuously observed and/or sampled at discrete time sequences, on problem is that often dynamics come from heterogeneous sources of uncertainty. This turns out particularly challenging with a low signal-to-noise ratio, due to the structural or experimental conditions; for ins ..."
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to account for both switching multi-scaling regimes and cascade system dynamics. We emphasize the role of independent component analysis, or ICA, for achieving dimensionality reduction of the addressed inverse problem, and also stress the relevance of atomic functional dictionaries in improving
Tree-Recursive Computation of Gradient Information for Structures
"... Abstract. Recently, the so-called Backpropagation Through Structure (BPTS) gradient calculation algorithm has been developed to capture learning scenarios where data is adequately represented by hybrid continuous-discrete structures (e.g. labeled ordered trees, nodes augmented by continuous informat ..."
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information). BPTS can be viewed as an extension of the well-known Backpropagation Through Time (BPTT) algorithm for discrete-time dynamical systems and sequence processing. The well-known (functionally equivalent) Real-time Recurrent Learning (RTRL) algorithm has to be favored to BPTT if long sequences
Efficient Methodology for Multibody Simulations with Discontinuous Changes in System Definition
, 2007
"... A new method is presented for accurately and efficiently simulating multi-scale multibody systems with discontinuous changes in system definitions as encountered in adaptive switching between models with different resolutions as well as models with different system topologies. An example of model re ..."
Abstract
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Cited by 5 (4 self)
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A new method is presented for accurately and efficiently simulating multi-scale multibody systems with discontinuous changes in system definitions as encountered in adaptive switching between models with different resolutions as well as models with different system topologies. An example of model
© Author(s) 2015. CC Attribution 3.0 License.
, 2015
"... www.hydrol-earth-syst-sci.net/19/3667/2015/ doi:10.5194/hess-19-3667-2015 ..."
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www.hydrol-earth-syst-sci.net/19/3667/2015/ doi:10.5194/hess-19-3667-2015
Analysis of lactose metabolism in E.coli using reachability analysis of hybrid systems
- IEE PROCEEDINGS - SYSTEMS BIOLOGY
, 2007
"... We propose an abstraction method for medium scale biomolecular networks, based on hybrid dynamical systems with continuous multi-affine dynamics. This abstraction method follows naturally from the notion of approximating nonlinear rate laws with continuous piecewise linear functions and can be easil ..."
Abstract
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Cited by 14 (3 self)
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We propose an abstraction method for medium scale biomolecular networks, based on hybrid dynamical systems with continuous multi-affine dynamics. This abstraction method follows naturally from the notion of approximating nonlinear rate laws with continuous piecewise linear functions and can
2004 Special Issue Motor primitive and sequence self-organization in a hierarchical recurrent neural network
, 2003
"... www.elsevier.com/locate/neunet This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of ..."
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topologically ordered mapping of initial cell activation states to motor primitive sequences self-organizes by utilizing the initial sensitivity characteristics of non-linear dynamical systems. The biological plausibility of the model’s essential principles is discussed.
A Vision System for Horizon Tracking and Object Recognition for Micro Air Vehicles
"... Abstract — In this paper, we develop a unified vision system for small-scale aircraft, known broadly as Micro Air Vehicles (MAVs), that not only addresses basic flight stability and control, but also enables more intelligent missions, such as ground object recognition and moving-object tracking. The ..."
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, incorporating both color and texture through a dynamic representation of image details. This representation is ideally suited for horizon detection and sky/ground segmentation of images, which we accomplish through the probabilistic representation of tree-structured belief networks (TSBN). Specifically, we
Results 1 - 10
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98
