An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and reservoir prediction. Facies identification based on supervised machine learning methods usually requires a large amount of labelled data, which are sometimes difficult to obtain. Here, we introduce the deep autoencoder to learn the hidden features and conduct facies classification from elastic attributes. Both labelled and unlabelled data are involved in the training process. Then, we develop a semi‐supervised deep autoencoder by taking the mean of intra‐class and the whole population of facies into account to construct a classification regularization term, thereby improving the classification accuracy and reducing the uncertainty. The new method inherits the profits of deep autoencoder and absorbs the information provided by labelled data. The proposed method performs well and produces promising results when it is used to address problems of reservoir prediction and facies identification. The new method is evaluated on both well and seismic data and compared with the conventional deep autoencoder method, which demonstrates its feasibility and superiority with respect to classification accuracy.

Semi‐supervised deep autoencoder for seismic facies classification

Depuis son introduction en 1982, la carte auto-organisatrice de Kohonen (Self-Organizing Map : SOM) a prouve ses capacites de classification et visualisation des donnees multidimensionnelles dans differents domaines d’application. Les implementations materielles de la carte SOM, en exploitant le taux de parallelisme eleve de l’algorithme de Kohonen, permettent d’augmenter les performances de ce modele neuronal souvent au detriment de la flexibilite. D’autre part, la flexibilite est offerte par les implementations logicielles qui quant a elles ne sont pas adaptees pour les applications temps reel a cause de leurs performances temporelles limitees. Dans cette these nous avons propose une architecture materielle distribuee, adaptable, flexible et extensible de la carte SOM a base de NoC dediee pour une implantation materielle sur FPGA. A base de cette approche, nous avons egalement propose une architecture materielle innovante d’une carte SOM a structure croissante au cours de la phase d’apprentissage

https://tel.archives-ouvertes.fr/tel-01868313/document

Réalisation d'un réseau de neurones "SOM" sur une architecture matérielle adaptable et extensible à base de réseaux sur puce "NoC"

The quality of cluster result and the learning speed of Self-organizing map (SOM) are dependent on the initialization of weights since the initial values for weight vectors affect the performance of SOM training when applied to clustering. In this paper, improvement of SOM was achieved with the application of the Nguyen-Widrow algorithm to initialize weights. Nguyen-Widrow initialization algorithm is a method for initialization of the weights of neural networks to speed up the training process. Performance of the modified SOM was determined in terms of cluster error rate and the number of iterations to achieve convergence using different datasets and results show that the modified SOM algorithm produces better cluster results and improved training speed compared to traditional SOM.

/pdf/improving-self-organizing-map-with-nguyen-widrow-130yg965ff.pdf

Improving self-organizing map with nguyen-widrow initialization lgorithm

A novel Hybrid technique for solving Data Aggregation problem based on Extended Weighted K-means algorithm and Key Performance Indicators

Self Organizing Maps perform clustering of data based on unsupervised learning. It is of concern that initialization of the weight vector contributes significantly to the performance of SOM and since real world datasets being high-dimensional, the complexity of SOM tend to increase tremendously leading to increased time consumption as well. Our work focuses on the analysis of different weight initialization strategies and various dimensionality reduction measures with the intent to make SOM flexible for handling high-dimensional datasets. We use two methods of comparison, one on projected space and another before projection. The datasets used are real world datasets taken from UCI repository.

https://www.amrita.edu/system/files/publications/a-comparative-performance-analysis-of-self-organizing-maps-on-weight-initializations-using-different-strategies.pdf

A Comparative Performance Analysis of Self Organizing Maps on Weight Initializations Using different Strategies

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A Comparative Performance Analysis of Self Organizing Maps on Weight Initializations Using different Strategies