Multi-channel Bayesian Adaptive Resonance Associate Memory for on-line topological map building

Chin, Wei Hong; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki; Toda, Yuichiro

doi:10.1016/j.asoc.2015.09.031

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…According to Vigdor and Lerner (2007) and Chin et al (2016), we have mentioned the optimized range of parameters' setting. Therefore, we follow the parameters' setup mentioned in paper (Vigdor and Lerner, 2007;Chin et al, 2016) because the authors have determined the optimized value for the Bayesian ART parameters; therefore, we will not cover in details in this paper.…”

Section: Methodsmentioning

confidence: 99%

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An Odometry-Free Approach for Simultaneous Localization and Online Hybrid Map Building

et al. 2016

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In this article, we propose a new approach for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM comprises two main components: (1) steadystate genetic algorithm (SSGA) for self-localization and occupancy grid map building and (2) Bayesian Adaptive Resonance Associative Memory (ARAM) for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid based, and it is incrementally constructed during the exploration process. During occupancy map building, the robot-estimated self-position is updated by SSGA. At the same time, the robot-estimated self-position is transmitted to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on the mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment to verify the proposed method can be implemented in real world.

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Section: Methodsmentioning

confidence: 99%

“…The algorithm consists of three core process (Chin et al, 2016), namely, "node competition, " "node matching (vigilance test), " and "node learning. "…”

Section: Topological Map Learning and Updatementioning

confidence: 99%

An Odometry-Free Approach for Simultaneous Localization and Online Hybrid Map Building

et al. 2016

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“…Bayesian ARTMAP variants have been developed for various tasks, such as semi-supervised learning (Nooralishahi et al, 2018;Tang & Han, 2010) and associative memory (Chin et al, 2016).…”

Section: Bayesian Artmapmentioning

confidence: 99%

A survey of adaptive resonance theory neural network models for engineering applications

2019

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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. 7 Code repositories 54 8 Conclusions 55 2 Compute activation function(s): Tj = fT (x, θj, α), ∀j ∈ C. 3 Perform WTA competition: J = arg max j∈Λ (Tj). 4 Compute match function(s):7 Update category J: θ new J = fL(x, θ old J , β). 8 else 9 Deactivate category J: Λ ← Λ − {J}. 10 if Λ = {∅} then 11 Go to step 3. 12 else 13 Set J = |C| + 1. 14 Create new category: C ← C ∪ {J}. 15 Initialize new category: θ new J = fN (x, λ). 16 Set output: y where the vigilance criterion is M J ≥ ρ.Learning. If the winning category satisfies the vigilance test, then resonance occurs, and the radius R J and centroid m J of the winning node are updated as follows:where ρ ∈ (0, 1] is the vigilance parameter.Learning. If the winning category J satisfies M J ≥ ρ, then resonance occurs, and it is updated as follows:J J needs to be updated after the presentation of each sample, then γ k (new) can be estimated incrementally. Particularly, when there is a resonant committed node J, if x k is a meta-information feature, thenotherwise,

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“…That means high-level information cannot feedback to low-level networks. Chang and Tan [40] and Chin et al [41] based on ART only used input to activate a cognitive node in the category field and read out the weight of the winner node. Kasaei et al [42] based on hierarchical object representation and extended Latent Dirichlet Allocation model also focused on the classification task and pre-defined many learning parameters.…”

Section: (B) the Process Mainly Involves Audiovisual Integration Andmentioning

confidence: 99%

An Autonomous Developmental Cognitive Architecture Based on Incremental Associative Neural Network With Dynamic Audiovisual Fusion

Huang

Song

et al. 2019

IEEE Access

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Developing cognition is difficult to achieve yet crucial for robots. Infants can gradually improve their cognition through parental guidance and self-exploration. However, conventional learning methods for robots often focus on a single modality and train a pre-defined model by large datasets in an offline way. In this paper, we propose a hierarchical autonomous cognitive architecture for robots to learn object concepts online by interacting with humans. Two pathways for audiovisual information are devised. Each pathway has three layers based on the self-organizing incremental neural networks. Visual features and names of objects are incrementally learned and self-organized in an unsupervised way in sample layers, respectively, in which we propose a dynamically adjustable similarity threshold strategy to allow the network itself to control cluster rather than using a pre-defined threshold. Two symbol layers abstract the cluster results from the corresponding sample layer to form concise symbols and transmit them to an associative layer. An associative relationship between two modalities can be built in real time by binding activated visual and auditory symbols simultaneously in the associative layer. In this layer, a top-down response strategy is proposed to let robots autonomously recall another associative modality, solve conflicting associative relationships, and adjust learned knowledge from the top down. The experimental results on two objects datasets and a real task show that our architecture is efficient to learn and associate object view and name in an online way. What is more, the robot can autonomously improve its cognitive level by utilizing its own experience without enquiring with humans. INDEX TERMS Cognitive development, concept online learning, self-organizing incremental neural network, object recognition, audiovisual integration.

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Multi-channel Bayesian Adaptive Resonance Associate Memory for on-line topological map building

Cited by 13 publications

References 30 publications

An Odometry-Free Approach for Simultaneous Localization and Online Hybrid Map Building

An Odometry-Free Approach for Simultaneous Localization and Online Hybrid Map Building

A survey of adaptive resonance theory neural network models for engineering applications

An Autonomous Developmental Cognitive Architecture Based on Incremental Associative Neural Network With Dynamic Audiovisual Fusion

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