5,537 research outputs found
Comparison of Classifiers for Radar Emitter Type Identification
ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applications such as electronic support measures (ESM). The performance of four ARTMAP variants- ARTMAP (Stage 1), ARTMAP-IC, fuzzy ARTMAP and Gaussian ARTMAP - is assessed with radar data gathered in the field. The k nearest neighbor (kNN) and radial basis function (RDF) classifiers are used for reference. Simulation results indicate that fuzzy ARTMAP and Gaussian ARTMAP achieve an average classification rate consistently higher than that of the other ARTMAP classifers and comparable to that of kNN and RBF. ART-EMAP, ARTMAP-IC and fuzzy ARTMAP require fewer training epochs than Gaussian ARTMAP and RBF, and substantially fewer prototype vectors (thus, smaller physical memory requirements and faster fielded performance) than Gaussian ARTMAP, RBF and kNN. Overall, fuzzy ART MAP performs at least as well as the other classifiers in both accuracy and computational complexity, and better than each of them in at least one of these aspects of performance. Incorporation into fuzzy ARTMAP of the MT- feature of ARTMAP-IC is found to be essential for convergence during on-line training with this data set.Defense Advanced Research Projects Agency and the Office of Naval Research (N000I4-95-1-409 (S.G. and M.A.R.); National Science Foundation (IRI-97-20333) (S.G.); Natural Science and Engineering Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657
A Novel Progressive Multi-label Classifier for Classincremental Data
In this paper, a progressive learning algorithm for multi-label
classification to learn new labels while retaining the knowledge of previous
labels is designed. New output neurons corresponding to new labels are added
and the neural network connections and parameters are automatically
restructured as if the label has been introduced from the beginning. This work
is the first of the kind in multi-label classifier for class-incremental
learning. It is useful for real-world applications such as robotics where
streaming data are available and the number of labels is often unknown. Based
on the Extreme Learning Machine framework, a novel universal classifier with
plug and play capabilities for progressive multi-label classification is
developed. Experimental results on various benchmark synthetic and real
datasets validate the efficiency and effectiveness of our proposed algorithm.Comment: 5 pages, 3 figures, 4 table
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
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
Learning Opposites Using Neural Networks
Many research works have successfully extended algorithms such as
evolutionary algorithms, reinforcement agents and neural networks using
"opposition-based learning" (OBL). Two types of the "opposites" have been
defined in the literature, namely \textit{type-I} and \textit{type-II}. The
former are linear in nature and applicable to the variable space, hence easy to
calculate. On the other hand, type-II opposites capture the "oppositeness" in
the output space. In fact, type-I opposites are considered a special case of
type-II opposites where inputs and outputs have a linear relationship. However,
in many real-world problems, inputs and outputs do in fact exhibit a nonlinear
relationship. Therefore, type-II opposites are expected to be better in
capturing the sense of "opposition" in terms of the input-output relation. In
the absence of any knowledge about the problem at hand, there seems to be no
intuitive way to calculate the type-II opposites. In this paper, we introduce
an approach to learn type-II opposites from the given inputs and their outputs
using the artificial neural networks (ANNs). We first perform \emph{opposition
mining} on the sample data, and then use the mined data to learn the
relationship between input and its opposite . We have validated
our algorithm using various benchmark functions to compare it against an
evolving fuzzy inference approach that has been recently introduced. The
results show the better performance of a neural approach to learn the
opposites. This will create new possibilities for integrating oppositional
schemes within existing algorithms promising a potential increase in
convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
Fuzzy modelling using a simplified rule base
Transparency and complexity are two major concerns of fuzzy rule-based systems. To improve accuracy and precision of the outputs, we need to increase the partitioning of the input space. However, this increases the number of rules exponentially, thereby increasing the complexity of the system and decreasing its transparency. The main factor behind these two issues is the conjunctive canonical form of the fuzzy rules. We present a novel method for replacing these rules with their singleton forms, and using aggregation operators to provide the mechanism for combining the crisp outputs
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