An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting

Chen, Tin-Chih; Chiu, Min-Chi

doi:10.1007/s40747-020-00179-8

Cited by 7 publications

(4 citation statements)

References 54 publications

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“…Furthermore, since the actual value of a predicted target is rarely equal to the predicted value, estimating the target’s range is an alternative. Fuzzy sets are a suitable choice to express this (T. Chen & Chiu, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example

Wu,

Chen,

Wang

2024

Sage Open

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Due to the impact of the COVID-19 pandemic, people have reduced eating out, resulting in a severe drop in the revenue of the catering industry. Health risks have become a major factor affecting the revenue of this industry. Predicting the revenue of the catering industry during the COVID-19 pandemic will not only allow practitioners to adjust their business strategies, but also provide a reference for governments to formulate relief measures. To this end, this study proposes a fuzzy big data analytics approach in which random forests, recursive feature elimination, fuzzy c-means, and deep neural networks are jointly applied. First, random forests and recursive feature elimination are used to select the most influential factors. The data is then divided into clusters by fuzzy c-means. Subsequently, a deep neural network is built for each cluster to make predictions. The prediction results of individual clusters are then aggregated to improve prediction accuracy. The proposed methodology has been applied to forecast the revenue of the catering industry in Taiwan. The results of the experiment showed that the impact of new deaths on the revenue of the catering industry was far greater than the number of newly diagnosed COVID-19 cases.

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Section: Literature Reviewmentioning

confidence: 99%

“…Chen & Chiu, 2021). For example, T. Chen and Chiu (2021) proposed a fuzzy collaborative forecasting method for dynamic random access memory (DRAM) yield forecasting, which was a typical big data problem because of many influencing factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example

Wu,

Chen,

Wang

2024

Sage Open

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“…is a homoscedastic serially uncorrelated error term that can be ignored. However, because learning activities are subject to human intervention [43], a yieldlearning process involves a lot of uncertainty [44]. This uncertainty can be modelled by defining yield as a probability function or fuzzy set.…”

Section: Uncertain Yield-learning Processmentioning

confidence: 99%

Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach

Tsai

Chen

et al. 2021

Mathematics

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Analyzing energy consumption is an important task for a factory. In order to accomplish this task, most studies fit the relationship between energy consumption and product design features, process characteristics, or equipment types. However, the energy-saving effects of product yield learning are rarely considered. To bridge this gap, this study proposes a two-stage fuzzy approach to estimate the energy savings brought about by yield improvement. In the two-stage fuzzy approach, a fuzzy polynomial programming approach is first utilized to fit the yield-learning process of a product. Then, the relationship between monthly electricity consumption and increase in yield was fit to estimate the energy savings brought about by the improvement in yield. The actual case of a dynamic random-access memory factory was used to illustrate the applicability of the two-stage fuzzy approach. According to the experiment results, product yield learning can greatly reduce electricity consumption.

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“…In robot control field, it has become a research hotspot to realize the high-precision control of robots and to further achieve the human-computer interaction (HCI) [1][2][3][4][5][6]. As a core element in the process of HCI, various methods concerning human intention recognition have been researched by many experts.…”

Section: Introductionmentioning

confidence: 99%

Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network

Liu

Peng

Shang

2021

Complex Intell. Syst.

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For the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient’s movement intention is a challenging research work, which needs to be studied from the shallow to the deep. Specifically, it is necessary to ensure that the movement intention of the normal person can be accurately recognized, and then improve the model to realize the recognition of the movement intention of the patients. Therefore, before studying the patient’s movement intention, it is essential to consider the normal person first, which is also for safety considerations. In recent years, a new Hill-based muscle model has been demonstrated to be capable of directly estimating the joint angle intention in an open-loop form. On this basis, by introducing a recurrent neural network (RNN), the whole prediction process can achieve more accuracy in a closed-loop form. However, for the traditional RNN algorithms, the activation function must be convex, which brings some limitations to the solution of practical problems. Especially, when the convergence speed of the traditional RNN model is limited in the practical applications, as the error continues to decrease, the convergence performance of the traditional RNN model will be greatly affected. To this end, a projected recurrent neural network (PRNN) model is proposed, which relaxes the condition of the convex function and can be used in the saturation constraint case. In addition, the corresponding theoretical proof is given, and the PRNN method with saturation constraint has been successfully applied in the experiment of intention recognition of lower limb movement compared with the traditional RNN model.

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An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting

Cited by 7 publications

References 54 publications

Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example

Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example

Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach

Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network

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