A generalized bipolar auto-associative memory model based on discrete recurrent neural networks

Zhou, Caigen; Zeng, Xiaoqin; Jiang, Haibo; Han, Lixin

doi:10.1016/j.neucom.2015.03.052

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…In artificial model, an associative memory is a category of neural network that enables recalling output pattern given a set of input patterns. Several models of AMs are described in [12,13,14,15,16,17,18,19,20]. The connectionist approach of modifying the weight of connections between neurons as the fundamental mechanism underlying associative learning and memory was mainly inspired from Hebb"s theory of cell assemblies [21].…”

Section: Introductionmentioning

confidence: 99%

Various Ways to Recall Cross-modality Perceptual Information in Cognitive Machine

-¹

2023

IJFMR

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As human, we actively stimulate our memory via variety of recalls for information across different modalities in our brains. The variety of recalls enriches our crossmodal recall mechanism and equip our cognitive system to retrieve information for different needs. Inspired by our cognitive capabilities, this paper formulates, implement and examine different ways in performing crossmodal associative recall of perceptual information in machine cognition. Simulations with different cases and scenarios are conducted and examined on the recalls. Results show that different recalls possess their own features and properties which are useful to equip cognitive machine with variety of cognitive recall capabilities for different purposes.

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Section: Introductionmentioning

confidence: 99%

Various Ways to Recall Cross-modality Perceptual Information in Cognitive Machine

-¹

2023

IJFMR

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“…One of the most notable techniques of AI is the artificial neural network (ANN). ANN is a representation in the form of machine learning emerging from the concept of simulating the human brain (Zhou et al, 2015). The Hopfield neural network (HNN) serves as a good illustration and model of ANN.…”

Section: Introductionmentioning

confidence: 99%

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Mohd Kasihmuddin,

Abdul Halim,

Mohd Jamaludin

et al. 2023

Journal of Information and Communication Technology

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Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset.

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“…Associative memories (AMs) are brain-style devices designed to store a set of patterns as stable equilibria such that the stored patterns can be reliably retrieved with the initial probes containing sufficient information about the patterns [18]. AMs can be divided into Autoassociative Memory (AAM) and Hetero-Associative Memory (HAM) [18]. When the input and the output are identical, is said to be memorized with in the AAM, whereas is said to be memorized with in the HAM.…”

Section: Introductionmentioning

confidence: 99%

Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Han

Deng

et al. 2018

Complexity

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In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

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A generalized bipolar auto-associative memory model based on discrete recurrent neural networks

Cited by 13 publications

References 28 publications

Various Ways to Recall Cross-modality Perceptual Information in Cognitive Machine

Various Ways to Recall Cross-modality Perceptual Information in Cognitive Machine

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Research on Face Recognition Method by Autoassociative Memory Based on RNNs

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