Permeability can be directly measured using cores taken from the reservoir in the laboratory. Due to high cost associated with coring, cores are available in a limited number of wells in a field. Many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. The main obj...

An incremental and Growing network model is introduced which is able to learn the topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. First an overview of the most known models of Self-Organizing Maps (SOM) is given. Then we propose a new algorithm for a SOM which can learn new input data (plasticity) without degrading the previously trained netwo...

An implementation of a network based Intrusion Detection System using a SelfOrganizing Map (SOM) as a clustering tool is described. The system uses a SOM to classify Ethernet data in real-time. A graphical tool constantly displays the clustered data to reflect network activities. The impact of using different techniques for data collection, data preprocessing and classifier design is discussed....

In this paper, a hybrid intelligent system that integrates the SOM (Self-Organizing Map) neural network, kMER (kernel-based Maximum Entropy learning Rule), and Probabilistic Neural Network (PNN) for data visualization and classification is proposed. The rationales of this Probabilistic SOM-kMER model are explained, and its applicability is demonstrated using two benchmark data sets. The results...

The Adaptive-Subspace SOM (ASSOM) is a modular neural-network architecture, the modules of which learn to identify input patterns subject to some simple transformations. The learning process is unsupervised, competitive, and related to that of the traditional SOM (Self-Organizing Map). Each neural module becomes adaptively speciic to some restricted class of transformations, and modules close t...

Image segmentation plays an important role in image retrieval system. In this paper, a method for segmenting images based on SOM neural network is proposed. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished with a SOM network. Then, the clustered blocks are merged to a specific number of regions. Experiments show that thes...

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering...

In this paper, we proposed a hybrid system to combine the self-organizing map (SOM) of neural network with case-based reasoning (CBR) method, for sales forecast of new released books. CBR systems have been successfully used in several domains of artificial intelligence. In order to enhance efficiency and capability of CBR systems, we connected the SOM method to deal with cluster problems of CBR...

permeability can be directly measured using cores taken from the reservoir in the laboratory. due to high cost associated with coring, cores are available in a limited number of wells in a field. many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. the main obj...

Artificial Neural Network in particular Self Organizing Map (SOM) has been widely used in clustering analysis operations. SOM maps high dimensional data space into two dimensions colored grid. The crisp clustering which employs one threshold to determine cluster boundaries has poor performance in many complex and high dimension data sets. In this paper to improve the performance of clustering a...