A Genetic Algorithm and Growing Cell Structure Approach to Learning Case Retrieval Structures

2001 ◽  
pp. 115-146 ◽  
Author(s):  
Werner Dubitzky ◽  
Francisco Azuaje
Keyword(s):  
Genetic Algorithm ◽  
Cell Structure ◽  
Case Retrieval ◽  
Structure Approach ◽  
Approach To Learning ◽  
Growing Cell

scholarly journals A Genetic Algorithm Based Clustering Ensemble Approach to Learning Relational Databases

2015 ◽  
Vol 21 (10) ◽  
pp. 3313-3317
Author(s):  
Rayner Alfred ◽  
Gabriel Jong Chiye ◽  
Joe Henry Obit ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Kim On Chin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Relational Databases ◽  
Clustering Ensemble ◽  
Approach To Learning ◽  
Ensemble Approach

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2009 ◽  
Vol 5 (4) ◽  
pp. 44-57 ◽  
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
Document Models ◽  
Clustering Quality

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this article, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

scholarly journals Synthesis of Bistable Periodic Structures Using Topology Optimization and a Genetic Algorithm

2005 ◽  
Author(s):  
Jitendra Prasad ◽  
Alejandro Diaz
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Periodic Structures ◽  
Performance Criterion ◽  
Ground Structure ◽  
Structure Approach ◽  
Automatic Synthesis ◽  
Compliant Structure ◽  
Beam Elements ◽  
Ground Structure Approach

Formulations for the automatic synthesis of two-dimensional bistable, compliant periodic structures are presented, based on standard methods for topology optimization. The design space is parameterized using non-linear beam elements and a ground structure approach. A performance criterion is suggested, based on characteristics of the load-deformation curve of the compliant structure. A genetic algorithm is used to find candidate solutions. A numerical implementation of this methodology is discussed and illustrated using a simple example.

scholarly journals Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Magnus Johnsson ◽  
Christian Balkenius
Keyword(s):  
Neural Network ◽  
Haptic Perception ◽  
Cell Structure ◽  
The Self ◽  
Robot Hand ◽  
Self Organizing Map ◽  
Training Set ◽  
Input Space ◽  
Growing Cell ◽  
Self Organizing

We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self-organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2011 ◽  
pp. 307-319
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
The Neural Network ◽  
Structure Algorithm

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this paper, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

scholarly journals Global synchronization analysis of droop-controlled microgrids—A multivariable cell structure approach

Automatica ◽  
2019 ◽  
Vol 109 ◽  
pp. 108550 ◽  
Author(s):  
Johannes Schiffer ◽  
Denis Efimov ◽  
Romeo Ortega
Keyword(s):  
Cell Structure ◽  
Global Synchronization ◽  
Structure Approach
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