Visualization and Data Mining of Multi-Objective Electric Machine Optimizations with Self-Organizing Maps: A Case Study on Switched Reluctance Machines

With Internet of Things (IoT) being prevalently adopted in recent years, traditional machine learning and data mining methods can hardly be competent to deal with the complex big data problems if applied alone. However, hybridizing those who have complementary advantages could achieve optimized practical solutions. This work discusses how to solve multivariate regression problems and extract intrinsic knowledge by hybridizing Self-Organizing Maps (SOM) and Regression Trees. A dual-layer SOM map is developed in which the first layer accomplishes unsupervised learning and then regression tree layer performs supervised learning in the second layer to get predictions and extract knowledge. In this framework, SOM neurons serve as kernels with similar training samples mapped so that regression tree could achieve regression locally. In this way, the difficulties of applying and visualizing local regression on high dimensional data are overcome. Further, we provide an automated growing mechanism based on a few stop criteria without adding new parameters. A case study of solving Electrical Vehicle (EV) range anxiety problem is presented and it demonstrates that our proposed hybrid model is quantitatively precise and interpretive. key words: Multivariate Regression, Big Data, Machine Learning, Data Mining, Self-Organizing Maps (SOM), Regression Tree, Electrical Vehicle (EV), Range Estimation, Internet of Things (IoT)

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An Implementation of Self-Organizing Maps for Airfoil Design Exploration via Multi-Objective Optimization Technique

Journal of Aerospace Technology and Management ◽

10.5028/jatm.v8i2.585 ◽

2016 ◽

Vol 8 (2) ◽

pp. 193-202 ◽

Cited By ~ 3

Author(s):

SungKi Jung ◽

Won Choi ◽

Luiz S. Martins-Filho ◽

Fernando Madeira

Keyword(s):

Optimization Technique ◽

Multi Objective Optimization ◽

Self Organizing Maps ◽

Design Exploration ◽

Multi Objective ◽

Self Organizing

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Analisis Cluster Data Interkomparasi Anak Timbangan dengan Algoritma Self Organizing Maps

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v7i2.3698 ◽

2021 ◽

Vol 7 (2) ◽

Author(s):

Arif Fajar Solikin ◽

Kusrini Kusrini ◽

Ferry Wahyu Wibowo

Keyword(s):

Data Mining ◽

Statistical Test ◽

Optimum Number ◽

Data Normalization ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Number Of Clusters ◽

Map Algorithm ◽

Cluster Data ◽

Self Organizing

Intercomparison was conducted to determine the ability and the performance of the laboratory. Intercomparison results are usually expressed in the range of En ratio values (En ?|1|) which express the equivalence of one laboratory with other laboratories. If the laboratory is declared unequal, then it needs to identify the source of the problem by itself. To make it easier, it can be done by Clustering which is one of the data mining techniques. Clustering is done by applying a self organizing map algorithm on the KNIME (Konstanz Information Miner) analytic tools. Several experiments were carried out with different layer size and data normalization status from one experiment to another experiment. The results were analyzed through pseudo F statistical test and icdrate test. The largest pseudo F statistic value was obtained from the 8th experiment (setting the layer size 2x2 without data normalization) with a pseudo F statistic value of 167.53 for 1kg artifacts and a Pseudo F statistic value of 104.86 for 200 g artifacts where the optimum number of clusters are 4. The smallest icdrate value was obtained from the 5th experiment (setting the 2x3 layer size without data normalization) with an icdrate value of 0.0713 for 1kg artifacts and icdrate value of 0.2889 for 200g artifacts with the best number of clusters being 6. From 12 laboratories can be grouped into 6 groups where each group has the same identification. There are groups 1, 3 and 6 have 1 member, while groups 2, 4 and 5 have 3 members.

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