scholarly journals ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Marko Švaco ◽  
Bojan Jerbić ◽  
Filip Šuligoj
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Adaptive Resonance Theory ◽  
Neural Network Architecture ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Search Mechanism ◽  
Robotic Applications ◽  
Computation Speed

This paper proposes a novel neural network architecture based on adaptive resonance theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object structures. The main novelty of the proposed architecture is a two-level categorization and search mechanism that can enhance computation speed while maintaining high performance in cases of higher vigilance values. ARTgrid is developed for specific robotic applications for work in unstructured environments with diverse work objects. For that reason simulations are conducted on random generated data which represents actual manipulation objects, that is, their respective 2D structures. ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network. Simulation results show that by applying higher vigilance values (ρ>0.85) clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm.

A Supervised Learning Network Based on Adaptive Resonance Theory

1997 ◽  
Vol 08 (02) ◽  
pp. 239-246 ◽  
Author(s):  
J. Zhou ◽  
S. Bennett
Keyword(s):  
Supervised Learning ◽  
Network Architecture ◽  
Recognition Task ◽  
Classification Problem ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Logistic Discrimination ◽  
Network Modules

A neural network architecture, fuzzy ART with logistic discrimination (ART-LD), is introduced as a method of realising the pattern recognition task in a supervised learning manner. The system is formed by the hierarchical organisation of two network modules: a fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART module self-organises the input patterns into category clusters, whose operations are governed by fuzzy set theory and "competitive learning" dynamics that ensure fast and stable learning. Then the outputs, which can be interpreted as fuzzy memberships of an input pattern to the encoded categories, provide the spatial distance informtion that is generalised by the subsequent logistic discrimination to give the final prediction. Examples are presented, and the generalisation capabilities of ART-LD are demonstrated through two simulated and one real classification problem.

Clustering Large Data with Mixed Values Using Extended Fuzzy Adaptive Resonance Theory

2016 ◽  
Vol 4 (3) ◽  
pp. 617
Author(s):  
Asadi Srinivasulu ◽  
Gadupudi Dakshayani
Keyword(s):  
Mixed Type ◽  
Traditional Approach ◽  
Feature Learning ◽  
Large Data ◽  
Adaptive Resonance Theory ◽  
Unsupervised Feature Learning ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Fuzzy Adaptive

<p>Clustering is one of the technique or approach in content mining and it is used for grouping similar items. Clustering software datasets with mixed values is a major challenge in clustering applications. The previous work deals with unsupervised feature learning techniques such as k-Means and C-Means which cannot be able to process the mixed type of data. There are several drawbacks in the previous work such as cluster tendency, partitioning, less accuracy and less performance. To overcome all those problems the extended fuzzy adaptive resonance theory (EFART) came into existence which indicates that the usage of fuzzy ART with some traditional approach. This work deals with mixed type of data by applying unsupervised feature learning for achieving the sparse representation to make it easier for clustering algorithms to separate the data. The advantages of extended fuzzy adaptive resonance theory are high accuracy, high performance, good partitioning, and good cluster tendency. This EFART adopts unsupervised feature learning which helps to cluster the large data sets like the teaching assistant evaluation, iris and the wine datasets. Finally, the obtained results may consist of clusters which are formed based on the similarity of their attribute type and values.</p>

Gearbox Fault Diagnosis Based on Selective Integrated Soft Competitive Learning Fuzzy Adaptive Resonance Theory

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiao-Jin Wan ◽  
Licheng Liu ◽  
Zengbing Xu ◽  
Zhigang Xu
Keyword(s):  
Fault Diagnosis ◽  
Lateral Inhibition ◽  
Adaptive Resonance Theory ◽  
Competitive Learning ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Diagnosis Model ◽  
Fuzzy Adaptive ◽  
Inhibition Theory

In this work, a soft competitive learning fuzzy adaptive resonance theory (SFART) diagnosis model based on multifeature domain selection for the single symptom domain and the single-target model is proposed. In order to solve the problem that the performance of traditional fuzzy ART (FART) is affected by the order of sample input, the similarity criterion of YU norm is introduced into the fuzzy ART network. In the meanwhile, the lateral inhibition theory is introduced to solve the wasteful problem of fuzzy ART mode node. By combining YU norm and lateral inhibition theory with fuzzy ART network, a soft competitive learning ART neural network diagnosis model that allows multiple mode nodes to learn simultaneously is designed. The feature parameters are extracted from the perspectives of time domain, frequency domain, time series model, wavelet analysis, and wavelet packet energy spectrum analysis, respectively. To further improve the diagnostic accuracy, the selective weighted majority voting method is integrated into the diagnosis model. Finally, the selected feature parameters are inputted to the integrated model to complete the fault classification and diagnosis. Finally, the proposed method is verified with a gearbox fault diagnosis test.

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