This paper proposes an extension of an SOM called the “SOM of SOMs,” or SOM, in which objects to be mapped are self-organizing maps. In SOM, each nodal unit of a conventional SOM is replaced by a function module of SOM. Therefore, SOM can be regarded as a variation of a modular network SOM (mnSOM). Since each child SOM module in SOM is trained to represent an individual map, the parent map in S...

Self-organizing map SOM neural networks have been widely applied in information sciences. In particular, Su and Zhao proposes in 2009 an SOM-based optimization SOMO algorithm in order to find a wining neuron, through a competitive learning process, that stands for the minimum of an objective function. In this paper, we generalize the SOM-based optimization SOMO algorithm to so-called SOMO-m alg...

Neural receiver structures have been developed for adaptive discrete-signal detection in telecommunication applications. Neural networks combined with conventional equalizers improve the performance especially in compensating for nonlinear distortions. These distortions may result, for instance, from nonlinear ampliication implemented for reducing the power consumption. In this paper, the behav...

Electromyograph (EMG) features have the properties of large variations and nonstationarity. An important issue in the classification of EMG is the classifier design. The major goal of this paper is to develop a classifier for the classification of eight kinds of prehensile postures to achieve high classification rate and reduce the online learning time. The cascaded architecture of neural netwo...

Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production techn...

The Self-Organizing Map (SOM) is an artificial neural network model based on unsupervised learning. In this paper, the use of the SOM in natural language processing is considered. The main emphasis is on natural features of natural language including contextuality of interpretation, and the communicative and social aspects of natural language learning and usage. The SOM is introduced as a gener...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

Self-Organizing Map (SOM) is an unsupervised neural network using for object detection and recognition in numerous image processing applications. This paper presents a methodology to achieve an automated detection of nipples by obscene pictures. The proposed system is composed of a human skin detection using for non-skin removal process based on image processing and a nipple detection using for...