Fuzzy associative memories with autoencoding mechanisms

Li, Lina; Pedrycz, Witold; Qu, Ting; Li, Zhiwu

doi:10.1016/j.knosys.2019.105090

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…FAM which was first published by Bart Kosko [9], [16]. So that FAM is a fuzzy system that maps one fuzzy set to another [17], [18].…”

Section: Fammentioning

confidence: 99%

“…So that the FAM method is one of the algorithms that have fuzzy logic so that this method can make flexible decisions [7], [8]. Fuzzy systems have the closeness of human reasoning [9], [10], so that the solutions obtained are easy to apply and understand. By utilizing the fuzzy system, it can be used as an evaluation of the perception of each consumer's complaint which can measure the level of satisfaction with the patient.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of FAM in satisfaction of inpatient services

Ramadhan¹,

Jalinus

Refdinal

et al. 2021

TELKOMNIKA

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Patients are the main object who must get the best service at a hospital, because the quality of hospital services determines the recovery of a patient and the quality of the hospital. The quality of hospital services has two components, namely the fulfillment of predetermined quality standards and the fulfillment of patient satisfaction. Hospitals must provide services that focus on patient satisfaction. Improving the quality of health services can be started by evaluating each element that plays a role in shaping patient satisfaction. The application of evaluation measures to patient care in each hospital is needed as an increase in the quality of service to patients. The analysis of the fuzzy associate memory method has the closeness of human reasoning to get solutions to various problems so that they are easy to apply and understand. Utilization of decision support system (DSS) analysis with fuzzy associate memory method can be used as an evaluation of the perception of each consumer complaint to measure the level of patient satisfaction.

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“…FAM which was first published by Bart Kosko [9], [16]. So that FAM is a fuzzy system that maps one fuzzy set to another [17], [18].…”

Section: Fammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of FAM in satisfaction of inpatient services

Ramadhan¹,

Jalinus

Refdinal

et al. 2021

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…One should point out that the first associative memory presented in the state of the art, that is, the Lernmatrix [1], is the basis of the associative classifier LMτ9 and has proven to be such a competitive classifier with most used algorithms in machine learning [39,40]. Owing to their versatility, associative memories have continued to be the subject of research for the last decade [20,[22][23][24][25][26][27][28][29][30][31][32][33][35][36][37][41][42][43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

New Model of Heteroasociative Min Memory Robust to Acquisition Noise

et al. 2022

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Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

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Fuzzy Models: Fundamentals and Design

Pedrycz

2020

An Introduction to Computing With Fuzzy Sets

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Fuzzy associative memories with autoencoding mechanisms

Cited by 9 publications

References 40 publications

Analysis of FAM in satisfaction of inpatient services

Analysis of FAM in satisfaction of inpatient services

New Model of Heteroasociative Min Memory Robust to Acquisition Noise

Fuzzy Models: Fundamentals and Design

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