Consumer’s Purchasing Behavior Analysis Based on the Self-Organizing Feature Map Neural Network Algorithm in E-Supply Chain

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

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Clustering Urban Multifunctional Landscapes Using the Self-Organizing Feature Map Neural Network Model

Journal of Urban Planning and Development ◽

10.1061/(asce)up.1943-5444.0000170 ◽

2014 ◽

Vol 140 (2) ◽

pp. 05014001 ◽

Cited By ~ 9

Author(s):

Yang Gao ◽

Zhe Feng ◽

Yang Wang ◽

Jin-Long Liu ◽

Shuang-Cheng Li ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

The Self ◽

Feature Map ◽

Multifunctional Landscapes ◽

Self Organizing

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Identification of Diagenetic Facies in Low-Permeability Sandstone Reservoirs Based on Self-Organizing-Map Neural Network Algorithm

10.2523/iptc-20304-abstract ◽

2020 ◽

Author(s):

Yan Lu ◽

Keyu Liu ◽

Ya Wang

Keyword(s):

Neural Network ◽

Low Permeability ◽

Self Organizing Map ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Sandstone Reservoirs ◽

Diagenetic Facies ◽

Self Organizing

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Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189532 ◽

2020 ◽

pp. 1-12

Author(s):

Shaoqin Lu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Risk Assessment ◽

Supply Chain ◽

Semantic Network ◽

System Structure ◽

Supply Chain Risk ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Risk Knowledge

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.

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