Abstract
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.
Highlights
Adaptive Resonance Theory (ART) [1] is a cognitive neural theory that attempts to explain how the human brain autonomously learns, categorizes, recognizes, and predicts events in a dynamic and changing environment
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
ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network
Summary
Adaptive Resonance Theory (ART) [1] is a cognitive neural theory that attempts to explain how the human brain autonomously learns, categorizes, recognizes, and predicts events in a dynamic and changing environment. The main ART mechanisms that are noted in recent ART based clustering architectures mostly utilize the search, choice, and resonance mechanisms from fuzzy ART. All previously mentioned ART architectures employ the fuzzy ART learning rule and are unable to change connection weights in a positive direction While they provide stable clustering results and solve specific learning problems they do not provide a novel search mechanism compared to the original fuzzy ART. The main advantage as well as novelty of the proposed architecture, in comparison with existing ART based neural architectures, is a two-level categorization and search mechanism This mechanism enhances clustering speed while maintaining high performance, which can be observed when vigilance parameter is set to higher values (ρ > 0.85).
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