Abstract — This study presents a new concept that generalizes the self-organizing map (SOM) by adopting the idea of modular network, which we call “modular network SOM (mnSOM)”. In the mnSOM, each codebook vector in the conventional SOM is replaced by a functional module which is a neural network. With mnSOM, the application targets can be widely expanded from fields involving vectorized data t...

In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot’s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervise...

In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot’s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervise...

Hyperspectral data is not linearly separable, and it has a high characteristic dimension. This paper proposes a new algorithm that combines a deep belief network based on the Boltzmann machine with a self-organizing neural network. The primary features of the hyperspectral image are extracted with a deep belief network. The weights of the network are fine-tuned using the labeled sample. Feature...

Web Usage Mining becomes a vital aspect in network traffic analysis. Previous study on Web usage mining using a synchronized Clustering, Neural based approach has shown that the usage trend analysis very much depends on the performance of the clustering of the number of requests. Self Organizing Networks is useful for representation of building unsupervised learning, clustering, and Visualizati...

Abst"'uct-Self-organizing neural network solutions to control problems are described. Conlpetitive networks �reate spatial filters and geonletry connedion� in a �elf-organizing fa�h­ ion. The goal position, the obstacles and the object under control all create neural activi­ ties through the filters. Spreading activation that discriminates between the controlled ob­ jectl the goal position and ...

A system based on a neural network framework is considered. We used two neural networks, an Elman network [1][2] and a Kohonen (concurrent) network [3], for a categorization task. The input of the system are objects derived from three general prototypes: circle, square, polygon. We varied the size and orientation of the objects in a continuous way. The system is trained using a new algorithm, b...