نتایج جستجو برای: organizing map som neural networks finally
تعداد نتایج: 1198270 فیلتر نتایج به سال:
In this paper is presented a demonstration of Kohonen's self-organizing maps, also known as SOM. Likewise is prepared a study of the functioning of Kohonen's maps in one and two dimensions and the most important characteristics of this type of network that works in similar way that the human brain. Finally, this paper details the characteristics necessaries for the network's training and how is...
This study aims to develop a generalized framework of an SOM called a modular network SOM (mnSOM). The mnSOM has an array structure consisting of functional modules that are trainable neural networks, e.g., multi-layer perceptrons (MLPs), instead of the vector units of the conventional SOM. In the case of MLP-modules, an mnSOM learns a group of systems or functions in terms of the input-output ...
This paper presents the results of modelling to predict the effluent biological oxygen demand (BOD5) concentration for primary clarifiers using a hybridisation of unsupervised and supervised artificial neural networks. The hybrid model is based on the unsupervised self-organising map (SOM) whose features were then used to train a multi-layered perceptron, feedforward back propagation artificial...
In this article, the use of the self-organizing map (SOM) is approached on the basis of current theories of learning. Possibilities of computer and networked platforms that aim at helping human learning are also inspected. It is shown how the SOM can be considered a model of constructive learning. The area of constructive learning is outlined and two cases of using the self-organizing map in co...
We present a neural unsupervised pattern recognition approach for two applications related to significant topics of Earth Observation (EO) imagery: (a) EO image region classification; (b) multispectral pixel classification. The proposed model is based on the Self-Organizing Map (SOM) clustering, which is compared to two benchmark unsupervised classifiers: k-means and fuzzy c-means. We propose t...
The Self Organizing Map SOM is a powerful neural network for analysis and visualization of high dimensional data It maps nonlinear statistical relationships be tween high dimensional input data into simple geometric relationships on a usually two dimensional grid The mapping roughly preserves the most important topological and metric relationships of the original data elements and thus inherent...
This paper presents a method for Data Mining and Knowledge Discovery in Image Data. This method is based on the Self-Organizing Map (SOM) which is an unsupervised artificial neural network algorithm. The SOM possesses unique properties of clustering, classification, modelling and visualization and is used here as a Data Mining tool. This enables us to get informative yet simpler pictures of the...
With increases in the amounts of available DNA sequence data, it has become increasingly important to develop tools for comprehensive systematic analysis and comparison of species-specific characteristics of protein-coding sequences for a wide variety of genomes. In the present study, we used a novel neural-network algorithm, a self-organizing map (SOM), to efficiently and comprehensively analy...
The Self Organizing Map SOM is a powerful neural network method for the analysis and visualization of high dimensional data It maps nonlinear statistical relationships between high dimensional input data into simple geometric relationships on a usually two dimensional grid The mapping roughly preserves the most important topological and metric relationships of the original data elements and thu...
In this paper, a method for automatically creating circuit schematic diagrams from the topological information contained in network data files has been proposed. This method is based on Self-Organizing Map (SOM) neural network and the basic idea behind the method is to let the network span itself according to a given “shape” of the network grid. The topology of a network is defined by the conne...
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