148 research outputs found
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
Neuroengineering of Clustering Algorithms
Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv
Function Approximation with ARTMAP Architectures
We analyze function approximation (regression) capability of Fuzzy ARTMAP (FAM) architectures - well-known incremental learning neural networks. We focus especially on the universal approximation property. In our experiments, we compare the regression performance of FAM networks with other standard neural models. It is the first time that ARTMAP regression is overviewed, both from theoretical and practical points of view
Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this
research
Hybrid Computational Intelligence Models With Symbolic Rule Extraction For Pattern Classification
Tesis ini adalah berkenaan dengan pembangunan model kecerdikan berkomputer hibrid bagi menangani masalah pengelasan corak.
This thesis is concerned with the development of hybrid Computational Intelligence (CI) models for tackling pattern classification problems
Machine condition monitoring using artificial intelligence: The incremental learning and multi-agent system approach
Machine condition monitoring is gaining importance in industry due to the
need to increase machine reliability and decrease the possible loss of production
due to machine breakdown. Often the data available to build a condition
monitoring system does not fully represent the system. It is also often common
that the data becomes available in small batches over a period of time. Hence,
it is important to build a system that is able to accommodate new data as
it becomes available without compromising the performance of the previously
learned data. In real-world applications, more than one condition monitoring
technology is used to monitor the condition of a machine. This leads to large
amounts of data, which require a highly skilled diagnostic specialist to analyze.
In this thesis, artificial intelligence (AI) techniques are used to build a
condition monitoring system that has incremental learning capabilities. Two
incremental learning algorithms are implemented, the first method uses Fuzzy
ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. In addition,
intelligent agents and multi-agent systems are used to build a condition
monitoring system that is able to accommodate various analysis techniques.
Experimentation was performed on two sets of condition monitoring data; the
dissolved gas analysis (DGA) data obtained from high voltage bushings and the
vibration data obtained from motor bearing. Results show that both Learn++
and FAM are able to accommodate new data without compromising the performance
of classifiers on previously learned information. Results also show
that intelligent agent and multi-agent system are able to achieve modularity
and flexibility
Lipase Mediated Transesterification Of Waste Cooking Palm Oil For Biodiesel Production : Batch And Continuous Studies [TP359.B46 S623 2008 f rb].
Pembangunan strategi baru yang lebih cekap untuk menghasilkan biodiesel adalah perkara yang sangat penting. Ini kerana biodiesel telah diterima di seluruh dunia sebagai bahan bakar alternatif untuk enjin diesel.
The development of new strategies to efficiently synthesize biodiesel is of extreme important. This is because biodiesel has been accepted worldwide as an alternative fuel for diesel engines
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