A time series forecasting based on cloud model similarity measurement

Yan, Gaowei; Jia, Songda; Ding, Jie; Pang, Yusong

doi:10.1007/s00500-018-3190-1

Cited by 14 publications

(3 citation statements)

References 34 publications

(41 reference statements)

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“…The similarity computing of cloud concept has been widely used in classification, clustering, similarity search, collaborative filtering, target recognition, watermarking technology research, system evaluation, similarity analysis of DNA sequence, data mining of stock time series and other fields [36][37][38][39][40][41][42]. At present, there are three main methods to calculate the similarity of cloud concepts.…”

Section: Similarity Measurement Of Cloud Conceptmentioning

confidence: 99%

Cognitive excursion analysis of uncertainty concepts based on cloud model

Lin

2022

Cognitive Comp and Systems

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Based on the characteristics of human cognition and the cloud model, the excursion of uncertain concepts by the similarity between uncertain concepts from the perspective of conceptual cognition is studied. Firstly, considering the different meanings of the numerical characters in cloud concept, the properties of cloud concepts similarity are given. Furthermore, in order to reflect the various similarities that may exist in uncertain concept, five similarities between cloud concepts are constructed, specifically include the similarity between concept expectations, the similarity between concept entropy, the similarity between concept hyper entropy, the shape similarity and the overall similarity. Secondly, the rationality of these proposed similarity measurements is illustrated by comparing with the existing methods through the specific data analysis. Finally, two cognitive experiments, including concept cognitive processes without prior knowledge and concept cognitive processes with prior knowledge (including positive prior knowledge and negative prior knowledge), are designed. These experiments are all used to study the excursion in the process of concept cognition by the similarity of cloud concepts. The experiment results show that the proposed similarities are reasonable by comparing the proposed method with the existing ones, and the effectiveness of the proposed method is also verified by the excursion of concept cognition.

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Section: Similarity Measurement Of Cloud Conceptmentioning

confidence: 99%

Cognitive excursion analysis of uncertainty concepts based on cloud model

Lin

2022

Cognitive Comp and Systems

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“… Wang et al (2018) defined a new measure of fuzzy distance for model clouds based on the α -cuts and they proposed a new cloud model similarity measurement method using the fuzzy distance measurements (fuzzy distance-based similarity, FDCM). Yan et al (2019) used the overlap-based expectation curve of cloud model (OECM) algorithm as a measurement method to measure the similarity of cloud models. In this algorithm, the overlapping degree is used to describe the overlapping part of two clouds, and the overlapping part is transformed into the similarity of cloud models by using the membership degree of “3 En ” boundary and the intersection of two clouds.…”

Section: Introductionmentioning

confidence: 99%

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

Yang

Han

Wan

et al. 2023

PeerJ Computer Science

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It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

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“…This encompasses two phases: the aggregation of individual variables into a decision-making index and the utilization of the index for an optimized feeding strategy. For the information aggregation of time series, similarity measurement is an effective approach [24][25][26]. The shape-based method of Dynamic Time Warping (DTW) has been widely applied to various fields [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Fed-Batch Culture Strategy Based on Multidimensional Time Series Aggregation

Wang

Gong

et al. 2023

Applied Sciences

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An optimized feeding strategy for the fed-batch fermentation of 2-keto-L-gulonic acid (2-KGA) is proposed to enhance the total profit of a workshop. Instead of assigning the same quantity of substrate to each batch in the empirical scheduling, the online feeding strategy offers a practical approach that allows for the precise management of substrate feeding to maximize economic performance. The forecasting of the profit function has been achieved through the proposed similarity measurement and multidimensional time series aggregation method. This method solves the problem of predicting the total economic benefit at the end of fermentation, in the absence of fermentation substrate measurement data. Pseudo-online simulation has been conducted, utilizing data derived from ninety industrial batches. A 6% increase in total profit is observed, which indicates that the optimized strategy utilizes the substrate and other related resources more efficiently and leads to a higher overall profit.

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A time series forecasting based on cloud model similarity measurement

Cited by 14 publications

References 34 publications

Cognitive excursion analysis of uncertainty concepts based on cloud model

Cognitive excursion analysis of uncertainty concepts based on cloud model

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

An Optimized Fed-Batch Culture Strategy Based on Multidimensional Time Series Aggregation

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