43,168 research outputs found
Polynomial-Chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields
for designing and optimizing systems, as well as for assessing their
reliability. To cope with demanding analysis such as optimization and
reliability, surrogate models (a.k.a meta-models) have been increasingly
investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging
are two popular non-intrusive meta-modelling techniques. PCE surrogates the
computational model with a series of orthonormal polynomials in the input
variables where polynomials are chosen in coherency with the probability
distributions of those input variables. On the other hand, Kriging assumes that
the computer model behaves as a realization of a Gaussian random process whose
parameters are estimated from the available computer runs, i.e. input vectors
and response values. These two techniques have been developed more or less in
parallel so far with little interaction between the researchers in the two
fields. In this paper, PC-Kriging is derived as a new non-intrusive
meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal
polynomials (PCE) approximates the global behavior of the computational model
whereas Kriging manages the local variability of the model output. An adaptive
algorithm similar to the least angle regression algorithm determines the
optimal sparse set of polynomials. PC-Kriging is validated on various benchmark
analytical functions which are easy to sample for reference results. From the
numerical investigations it is concluded that PC-Kriging performs better than
or at least as good as the two distinct meta-modeling techniques. A larger gain
in accuracy is obtained when the experimental design has a limited size, which
is an asset when dealing with demanding computational models
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
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