In this paper, a new powerful method in artificial neural networks, called modular network SOM (mnSOM) is introduced. mnSOM is a generalization of Self Organizing Maps (SOM) formed by replacing each vector unit of SOM with function module. The modular function could be a multi layer perceptron, a recurrent neural network or even SOM itself. Having this flexibility, mnSOM becomes a new powerful ...

This research develops a hybrid model by integrating Self Organization Map (SOM) neural network, Genetic Algorithms (GA) and Fuzzy Rule Base (FRB) to forecast the future sales of a printed circuit board factory. This hybrid model encompasses two novel concepts: (1) clustering an FRB into different clusters, thus the interaction between fuzzy rules is reduced and a more accurate prediction model...

Somatostatin (Som), one of the most concentrated neuropeptides in the brain, is highly expressed in the olfactory bulb (OB). However, the temporal profile by which OB somatostatin-expressing (Som+) interneurons are produced and the molecular mechanisms controlling this profile are totally unknown. In the present study, we found that all the Som+ interneurons in the mouse external plexiform laye...

Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of generalisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, bp-som, to avoid overfitting. bp-som is a hybrid neural network which combines a multi-layered feed-forward network (mfn) with Kohonen’s s...

We analyzed relations between the content of total organic carbon (TOC) and the content of soil organic matter (SOM) in 47 soil profiles dug out in dry coniferous forests, fresh coniferous forests, and mixed deciduous-coniferous forests. Soils in the analyzed sites are derived from sands. In each soil horizon, bulk density (BD), pH, soil texture, and TOC and SOM were determined. Correlation bet...

This paper is a short presentation of the SOM (Semantic Object Model) approach. SOM was created to fulfill two main objectives. The first objective is the revision of the traditional data modeling techniques in order to integrate them within an object oriented framework, without sacrificing the main object-oriented principles, namely encapsulation, extendibility and reusability. The paper advoc...

The self-organising map (SOM) and its variant, visualisation induced SOM (ViSOM), have been known to yield similar results to multidimensional scaling (MDS). However, the exact connection has not been established. In this paper, a review on the SOM and its cost function and topological measures is provided first. We then examine the exact scaling effect of the SOM and ViSOM from their objective...

In this study, a multistage modular self-organizing map (SOM) model is proposed for parallel web text clustering. In the first stage, the large textual datasets are divided into some small disjoint datasets (i.e., task decomposition). In the second stage, each small data set is input into different unitary SOM models for word clustering map (i.e., modularization learning). In this stage, differ...