Kalman Filtering and Neural Networks

By Simon S. Haykin. 2001

2,087 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “A combination of a decoupled extended Kalman filter (Feldkamp, Prokhorov, Eagen, & Yuan, 1998; Feldkamp, Prokhorov, & Feldkamp, 2003; Haykin, 2001; Kalman, 1960; Puskorius & Feldkamp, 1994; Williams, 1992b) and an LSTM RNN (Pérez-Ortiz et al., 2003) learned to deal correctly with values of n up to…”
Deep Kalman Filters (2015, methodology): “We point the reader to Haykin (2004) for a summary of some approaches to learn Kalman ﬁlters.”
A Nonlinear Bayesian Filtering Framework for ECG Denoising (2007, methodology): “The theory of the UKF and its implementation issues have already been discussed in the literature and the reader is referred to [26]”
TIME‐CHANGED ORNSTEIN–UHLENBECK PROCESSES AND THEIR APPLICATIONS IN COMMODITY DERIVATIVE MODELS (2012, methodology): “ition equation is not Gaussian, and the particle lter algorithm (or the extended particle lter or the unscented particle lter) can be used since we know the transition density of Xin closed form (see Haykin (2001) and Javaheri et al. (2003)). 3.2 The Maturity Eect The maturity eect (also known as th”
SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, methodology): “Thomas Bayes would later derive his famous rule for statistical inference, which forms the basis for some Bayesian estimation techniques [32].”
Generic and Scalable Framework for Automated Time-series Anomaly Detection (2015, methodology): “ARIMA [30], Exponential Smoothing [11], Kalman Filter [9], State Space Models [6], etc.”
State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge (2008, methodology): “To achieve this, the dual EKF [5,8] is used to estimate simultaneously the SoC and the capacity.”
Gesture recognition for human-robot collaboration: A review (2017, positive): “Extend Kalman Filter (EKF) is a functional tracking algorithm even if the model is nonlinear (Haykin, 2004).”
Sigma-point kalman filters for probabilistic inference in dynamic state-space models (2004, methodology): “It has been applied successfully to problems in all areas of probabilistic inference, including state estimation , parameter estimation (machine learning) and dual estimation [29] [2] [15] [12] [10].”
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling (2018, background): “An additional discrete latent state z t ∈ { 1 , . . . , K } determines the linear dynamics at time t , where learning of the model and can utilize the learning tools developed for linear systems (Haykin, 2001).”