Estimation of mass matrix in machine tool’s weak components research by using symbolic regression

Liu, Hongqi; Lin, Hai; Jiang, Xuchu; Mao, Xinyong; Liu, Quanxin; Li, Bin

doi:10.1016/j.cie.2018.11.033

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Te Jaccard coefcient represents the similarity between the two data objects [13], as shown by the following equation:…”

Section: 􏽱 (7)mentioning

confidence: 99%

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Time Series Symbolization Method for the Data Mining K-Means Algorithm

Wang

2023

Discrete Dynamics in Nature and Society

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Time series is a data type frequently encountered in data analysis. With the current depth and breadth of the data and the improvement in computer processing capabilities, the dimensionality and the complexity of time series are getting higher and higher. Time series symbolization is to cluster and assign complex and lengthy time series in the form of symbols to achieve the purpose of reducing the dimensionality of the sequence or making the sequence easier to process. Considering the excellent performance of the K-means algorithm in data mining and processing, as well as in the allocation algorithm for clustering, we plan to develop a simple method for the symbolization of time series for the K-means algorithm and hope that this method can realize the high-dimensional time series dimensionality reduction, processing of the special points in time series, and so on. Based on this, this article proposes an improved sans algorithm based on the K-means algorithm and discusses the representation method and the data processing of time series symbolization. Experimental results show that this method can effectively reduce the dimensionality of high-dimensional time series. After dimensionality reduction, the information retention rate contained in the elevation of the sequence can reach more than 90%, which is very effective for the detection of outliers in low-dimensional sequences.

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“…Te Jaccard coefcient represents the similarity between the two data objects [13], as shown by the following equation:…”

Section: 􏽱 (7)mentioning

confidence: 99%

“…Ensure that the number of states in each sequence variable is the same [13], as shown by the following equation:…”

Section: 􏽱 (7)mentioning

confidence: 99%

Time Series Symbolization Method for the Data Mining K-Means Algorithm

Wang

2023

Discrete Dynamics in Nature and Society

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“…Moreover, SR has been utilized to derive models capable of describing the underlying connection between alloy composition, cooling time and hardness, in welding heat-affected zone of low alloy steel [ 185 ]. Additionally, SR has been employed to estimate constitutive model parameters in an alloy research [ 186 ], or even facilitated to create expressions to search for the optimized components during machine tool design by finding the modal mass distribution matrix, which is usually hard to obtain [ 187 ].…”

Section: Application In Science and Technologymentioning

confidence: 99%

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Angelis

Sofos

Karakasidis

2023

Arch Computat Methods Eng

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Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

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“…In practical application, this system architecture can not only carry out offline computing, but also real-time computing, and can integrate Hadoop, Kafka, and other big data components. At the same time, based on the practical computer system operation analysis, Lambda architecture has the advantages of low delay, horizontal expansion, versatility, extensibility and so on [9,10]. The recommendation system built in the previous stand-alone environment is now unable to meet the large-scale storage and computing needs of computers for large data, while the Hadoop platform can process a large amount of data information based on the standard of big data technology and combine it with Lambda architecture.…”

Section: System Architecturementioning

confidence: 99%

Research on the Design of Mass Recommendation System Based on Lambda Architecture

Chen

Zhu

et al. 2021

JWE

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In the era of big data, in order to increasing the information data for conforms to the personalized needs of content, research scholars put forward based on the Lambda mass recommendation system architecture design, it can not only to the recessive and dominant behavior of users of the system data collection storage and research analysis, can also be based on the analysis of cascading hybrid algorithm to explore how to carry out real-time recommendation. Therefore, on the basis of understanding the research and development achievements of recommender systems at home and abroad in recent years, and based on the understanding and analysis of Lambda architecture and cascading hybrid algorithm, this paper aims at how to design a massive recommender system in line with users’ behavior, and makes clear the recommendation effect by combining with system testing.

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Estimation of mass matrix in machine tool’s weak components research by using symbolic regression

Cited by 9 publications

References 24 publications

Time Series Symbolization Method for the Data Mining K-Means Algorithm

Time Series Symbolization Method for the Data Mining K-Means Algorithm

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Research on the Design of Mass Recommendation System Based on Lambda Architecture

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