73 research outputs found
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Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects
In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multi-objective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted
Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions
This paper is concerned with the least-squares linear centralized estimation problem
in multi-sensor network systems from measured outputs with uncertainties modeled by random
parameter matrices. These measurements are transmitted to a central processor over different
communication channels, and owing to the unreliability of the network, random one-step delays and
packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion,
at each sampling time, each sensor’s data packet is transmitted just once, but due to the uncertainty
of the transmissions, the processing center may receive either one packet, two packets, or nothing.
Different white sequences of Bernoulli random variables are introduced to describe the observations
used to update the estimators at each sampling time. To address the centralized estimation problem,
augmented observation vectors are defined by accumulating the raw measurements from the different
sensors, and when the current measurement of a sensor does not arrive on time, the corresponding
component of the augmented measured output predictor is used as compensation in the estimator
design. Through an innovation approach, centralized fusion estimators, including predictors, filters,
and smoothers are obtained by recursive algorithms without requiring the signal evolution model.
A numerical example is presented to show how uncertain systems with state-dependent multiplicative
noise can be covered by the proposed model and how the estimation accuracy is influenced by both
sensor uncertainties and transmission failures.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de
Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
In this paper, a cluster-based approach is used to address the distributed fusion estimation
problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of
random deception attacks. At each sampling time, measured outputs of the signal are provided by
a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local
processor which gathers the measured outputs of its sensors and, in turn, the local processors of all
clusters are connected with a global fusion center. The proposed cluster-based fusion estimation
structure involves two stages. First, every single sensor in a cluster transmits its observations to the
corresponding local processor, where least-squares local estimators are designed by an innovation
approach. During this transmission, deception attacks to the sensor measurements may be randomly
launched by an adversary, with known probabilities of success that may be different at each sensor.
In the second stage, the local estimators are sent to the fusion center, where they are combined
to generate the proposed fusion estimators. The covariance-based design of the distributed fusion
filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution
model, but only the first and second order moments of the processes involved in the observation
model. Simulations are provided to illustrate the theoretical results and analyze the effect of the
attack success probability on the estimation performance.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de
Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Discrete Time Systems
Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area
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Latent state estimation in a class of nonlinear systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problem of estimating latent or unobserved states of a dynamical system from observed data is studied in this thesis. Approximate filtering methods for discrete time series for a class of nonlinear
systems are considered, which, in turn, require sampling from a partially specified discrete distribution. A new algorithm is proposed to sample from partially specified discrete distribution, where the specification is in terms of the first few moments of the distribution. This algorithm generates deterministic sigma points and corresponding probability weights, which match exactly a specified mean vector, a specified covariance matrix, the average of specified marginal skewness and the average of specified marginal kurtosis. Both the deterministic particles and the probability weights are given in closed form and no numerical optimization is required. This algorithm is then used in approximate Bayesian filtering for generation of particles and the associated probability weights which propagate higher order moment information about latent states. This method is extended to generate random sigma points (or particles) and corresponding probability weights that match the same moments. The
algorithm is also shown to be useful in scenario generation for financial optimization. For a variety of important distributions, the proposed moment-matching algorithm for generating particles is shown
to lead to approximation which is very close to maximum entropy approximation. In a separate, but related contribution to the field of nonlinear state estimation, a closed-form linear minimum variance filter is derived for the systems with stochastic parameter uncertainties. The expressions
for eigenvalues of the perturbed filter are derived for comparison with eigenvalues of the unperturbed Kalman filter. Moment-matching approximation is proposed for the nonlinear systems with multiplicative stochastic noise
Latent state estimation in a class of nonlinear systems
The problem of estimating latent or unobserved states of a dynamical system from observed data is studied in this thesis. Approximate filtering methods for discrete time series for a class of nonlinear systems are considered, which, in turn, require sampling from a partially specified discrete distribution. A new algorithm is proposed to sample from partially specified discrete distribution, where the specification is in terms of the first few moments of the distribution. This algorithm generates deterministic sigma points and corresponding probability weights, which match exactly a specified mean vector, a specified covariance matrix, the average of specified marginal skewness and the average of specified marginal kurtosis. Both the deterministic particles and the probability weights are given in closed form and no numerical optimization is required. This algorithm is then used in approximate Bayesian filtering for generation of particles and the associated probability weights which propagate higher order moment information about latent states. This method is extended to generate random sigma points (or particles) and corresponding probability weights that match the same moments. The algorithm is also shown to be useful in scenario generation for financial optimization. For a variety of important distributions, the proposed moment-matching algorithm for generating particles is shown to lead to approximation which is very close to maximum entropy approximation. In a separate, but related contribution to the field of nonlinear state estimation, a closed-form linear minimum variance filter is derived for the systems with stochastic parameter uncertainties. The expressions for eigenvalues of the perturbed filter are derived for comparison with eigenvalues of the unperturbed Kalman filter. Moment-matching approximation is proposed for the nonlinear systems with multiplicative stochastic noise.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Stochastic Processes with Applications
Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines.This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems
Robust data cleaning procedure for large scale medium voltage distribution networks feeders
Relatively little attention has been given to the short-term load forecasting problem of primary substations mainly because load forecasts were not essential to secure the operation of passive distribution networks. With the increasing uptake of intermittent generations, distribution networks are becoming active since power flows can change direction in a somewhat volatile fashion. The volatility of power flows introduces operational constraints on voltage control, system fault levels, thermal constraints, systems losses and high reverse power flows. Today, greater observability of the networks is essential to maintain a safe overall system and to maximise the utilisation of existing assets. Hence, to identify and anticipate for any forthcoming critical operational conditions, networks operators are compelled to broaden their visibility of the networks to time horizons that include not only real-time information but also hour-ahead and day-ahead forecasts. With this change in paradigm, progressively, large scales of short-term load forecasters is integrated as an essential component of distribution networks' control and planning tools.
The data acquisition of large scale real-world data is prone to errors; anomalies in data sets can lead to erroneous forecasting outcomes. Hence, data cleansing is an essential first step in data-driven learning techniques. Data cleansing is a labour-intensive and time-consuming task for the following reasons: 1) to select a suitable cleansing method is not trivial 2) to generalise or automate a cleansing procedure is challenging, 3) there is a risk to introduce new errors in the data. This thesis attempts to maximise the performance of large scale forecasting models by addressing the quality of the modelling data. Thus, the objectives of this research are to identify the bad data quality causes, design an automatic data cleansing procedure suitable for large scale distribution network datasets and, to propose a rigorous framework for modelling MV distribution network feeders time series with deep learning architecture. The thesis discusses in detail the challenges in handling and modelling real-world distribution feeders time series. It also discusses a robust technique to detect outliers in the presence of level-shifts, and suitable missing values imputation techniques. All the concepts have been demonstrated on large real-world distribution network data.Open Acces
Dynamic non-linear system modelling using wavelet-based soft computing techniques
The enormous number of complex systems results in the necessity of high-level and cost-efficient
modelling structures for the operators and system designers. Model-based approaches offer a very
challenging way to integrate a priori knowledge into the procedure. Soft computing based models
in particular, can successfully be applied in cases of highly nonlinear problems. A further reason
for dealing with so called soft computational model based techniques is that in real-world cases,
many times only partial, uncertain and/or inaccurate data is available.
Wavelet-Based soft computing techniques are considered, as one of the latest trends in system
identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based
approaches to model the non-linear dynamical systems in real world problems in conjunction with
possible twists and novelties aiming for more accurate and less complex modelling structure.
Initially, an on-line structure and parameter design has been considered in an adaptive Neuro-
Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy
rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus
(Monascus ruber van Tieghem) is examined against several other approaches for further
justification of the proposed methodology.
By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have
been introduced. Increasing the accuracy and decreasing the computational cost are both the
primary targets of proposed novelties. Modifying the synoptic weights by replacing them with
Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA)
comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for
the above challenges. These two models differ from the point of view of structure while they share
the same HLA scheme. The second approach contains an additional Multiplication layer, plus its
hidden layer contains several sub-WNNs for each input dimension. The practical superiority of
these extensions is demonstrated by simulation and experimental results on real non-linear
dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT)
whole milk, and consolidated with comprehensive comparison with other suggested schemes.
At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is
presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network
(FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a
modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from
the data by building accurate regression, but also for the identification of complex systems.
The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the
consequent parts of rules. In order to improve the function approximation accuracy and general
capability of the FWNN system, an efficient hybrid learning approach is used to adjust the
parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is
employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which
is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world
application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the
above technique
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