نتایج جستجو برای: kohenen self organizing neural networks

تعداد نتایج: 1148962  

2000
Gabriela Guimarães Wolfgang Urfer

This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical levels enables the discovery of complex structures as present in this type of applications. Furthermore,...

1999
Taira NAKAJIMA Hiroyuki TAKIZAWA Hiroaki KOBAYASHI Tadao NAKAMURA

We propose a learning algorithm for selforganizing neural networks to form a topology preserving map from an input manifold whose topology may dynamically change. Experimental results show that the network using the proposed algorithm can rapidly adjust itself to represent the topology of nonstationary input distributions. key words: competitive Hebbian learning rule, law of the jungle mechanis...

1997
Carolina Chang Paolo Gaudiano

We have recently introduced a neural network for reactive obstacle avoidance based on a model of classical and operant conditioning. In this article we describe the success of this model when implemented on two real autonomous robots. Our results show the promise of self-organizing neural networks in the domain of intelligent robotics.

2007
Carolina Chang Paolo Gaudiano

In this paper we describe a neural network for reactive and adaptive robot navigation. The network is based on a model of classical and operant conditioning first proposed by Grossberg [3]. The network has been successfully implemented on the real Khepera robot. This work shows the potential of applying self-organizing neural networks to the area of intelligent robotics.

Journal: :Transactions of the Society of Instrument and Control Engineers 2005

Journal: :International Journal of Artificial Intelligence & Applications 2017

2004
Francisco Flórez-Revuelta Juan Manuel García Chamizo José García Rodríguez Antonio Hernández Sáez

Self-organizing neural networks endeavour to preserve the topology of an input space by means of competitive learning. There are diverse measures that allow to quantify how good is this topology preservation. However, most of them are not applicable to measure non-linear input manifolds, since they don't consider the topology of the input space in their calculation. In this work, we have modifi...

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