%0 Journal Article
%T ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory
%A Marko £¿vaco
%A Bojan Jerbi£¿
%A Filip £¿uligoj
%J Advances in Artificial Neural Systems
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/185492
%X 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 () clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm. 1. Introduction 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. ART contains a series of artificial neural networks (ANN), which are used for supervised and unsupervised learning. ART neural networks solve the stability-plasticity dilemma defined by Grossberg [2]. Plasticity of a learning algorithm denotes the characteristic of successful adaptation to changing environmental conditions and the possibility to code new input patterns. Stability of learning algorithms is characterized by the ability to learn new input patterns without catastrophic forgetting [3]. Mechanisms and main principles of Adaptive Resonance Theory can be observed in many areas of the human brain including the visual cortex as noted from significant experiments during the previous decades [4, 5]. Main principles are based on the assumption that learning apart from knowledge update utilizes two major mechanisms: categorization and expectation. The main ART mechanisms that are noted in recent ART based clustering architectures mostly utilize the search, choice, and resonance mechanisms from fuzzy ART. Fuzzy ART [6] enables fast categorization and learning performance of analog input patterns. Long-term connection weight values can only decrease in time which provides fuzzy ART with high clustering stability. The complement coding mechanism ensures stable normalization of input vectors. A fuzzy ART variant algorithm
%U http://www.hindawi.com/journals/aans/2014/185492/