193,965 research outputs found
Evolution of Neural Networks for Helicopter Control: Why Modularity Matters
The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so
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
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Identification of Evolving Rule-based Models.
An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Communicability in temporal networks
A first-principles approach to quantify the communicability between pairs of nodes in temporal networks is proposed. It corresponds to the imaginary-time propagator of a quantum random walk in the temporal network, which accounts for unique structural and temporal characteristics of both streaming and nonstreaming temporal networks. The influence of the system's temperature on the perdurability of information and how the communicability identifies patterns of communication hidden in the temporal and topological structure of the networks are also studied for synthetic and real-world systems
- ā¦