In this paper, neuro based intelligent diagnosis methods for electro-mechanical control system are proposed. A self organizing map neural network (SOM) is used to classify measured data of the target system as a qualitative diagnostic method. Besides of the above procedure, it is expected to attain more efficient maintenance by a quantitative estimation of failure. For the purpose, new method i...

Image compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. It is important to maintain the image quality even if it is compressed to certain extent. Depends upon this the image compression is classified into two categories: lossy and ...

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualisation of high-dimensional data. In this paper, the SOM algorithm is applied to the analysis of the technology of world paper and pulp industry. It is seen that the method can be used on environmental, technological and nancial data to produce a comprehensive view of the industry as a whole.

The Self-Organizing Map (SOM) is one of the most popular neural network methods. It is a powerful tool in visualization and analysis of high-dimensional data in various engineering applications. The SOM maps the data on a two-dimensional grid which may be used as a base for various kinds of visual approaches for clustering, correlation and novelty detection. In this chapter, we present novel me...

PROPRE is a generic and semi-supervised neural learning paradigm that extracts meaningful concepts of multimodal data flows based on predictability across modalities. It consists on the combination of two computational paradigms. First, a topological projection of each data flow on a self-organizing map (SOM) to reduce input dimension. Second, each SOM activity is used to predict activities in ...

The need to transmit data over Internet is increasing at a very fast pace, which requires techniques that can considerably reduce the size of images so that they occupy less space and bandwidth for transmission. In this paper, we have used Kohonen’s self organizing map (SOM) network, which is a class of neural networks, for image compression and feature extraction. Moreover, a global processing...

Purpose – We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of semantically meaningful events (categories). Design/methodology/approach – We provide a survey of existing techniques that have been proposed, either directly or indirectly, tow...

The scope of this paper is to introduce new analysis and visualization methods for WCDMA cellular networks. The proposed examples are mainly based on the Self-Organizing Map (SOM) method, but also other neural and statistical methods are equally applicable. The main motivation for advanced methods is to increase the abstraction level from the raw network measurements, i.e. radio access network ...

The Self Organizing Map (SOM) involves neural networks, that learns the features of input data thorough unsupervised, competitive neighborhood learning. In the SOM learning algorithm, connection weights in a SOM feature map are initialized at random values, which also sets nodes at random locations in the feature map independent of input data space. The move distance of output nodes increases, ...

No gold standard exists for assessing the risk of individual patients in cardiovascular medicine. The medical data used for such purposes is, itself, inconsistent over a history of patients at any one clinical site, and not always immediately useable. In this paper the clustering of data using Self Organizing Maps (SOM) is described. This method is an unsupervised neural network developed by Te...