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SelfOrganizing Neural Network
"... Application to Drill Wear Classification The article describes an application of a simulated neural network to drill wear classification from cutting force signals generated by the drilling process. As the input to the neural network, a multicomponent vector composed of a sensory part and a descript ..."
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descriptive part is used. The components of the sensory part represent characteristic features ofthe cutting momentum and the feed force power spectra, while the descriptive part encodes the corresponding drill wear class. During adaptation, the selforganizing neural network is used to form a set
SelfOrganizing Neural Networks
"... ures that they "discover" from the input data, namely, "shape" of data and clusters of points. Generalised Hebbian Learning extracts from data a set of principal directions along which data is organised in a pdimensional space. Each direction is represent by a relevant weight v ..."
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ures that they "discover" from the input data, namely, "shape" of data and clusters of points. Generalised Hebbian Learning extracts from data a set of principal directions along which data is organised in a pdimensional space. Each direction is represent by a relevant weight vector. The number of those principal directions is, at most, equal to the dimensionality of the input space p. In an illustrative example presented in Figure 82 the twodimensional data is organised along two principal directions, w 1 and w 2 . # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Figure 82: A 2D pattern with principal directions Competitive Learning extracts from data a set of centers of data clusters. Each center point is stored as a weight vector. It is obvious that the number of clusters is independent of dimensionality of the input space. In Figure 83 twodimensional data is organised in three clusters, their centres represented by th
Endmember Extraction by a SelfOrganizing Neural Network on
 Hyperspectral Images,” Proc. International Conference on Automation, Robotics and Computer Vision, Nanyang Technological Institute
"... The present work exploits the possibility of using a SelfOrganizing Neural Network to obtain the endmembers (class prototypes) on hyperspectral images. The SelfOrganizing neural network has the advantage that obtains by competitive procedures this endmembers on hyperspectral images. We propose a n ..."
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The present work exploits the possibility of using a SelfOrganizing Neural Network to obtain the endmembers (class prototypes) on hyperspectral images. The SelfOrganizing neural network has the advantage that obtains by competitive procedures this endmembers on hyperspectral images. We propose a
Spatial Organization Using SelfOrganizing Neural Networks
"... Spatial hypertext systems use physical properties as color, dimensions, and position to represent relationships between documents. These systems allows the user to express a lot of different relationships between information but the structure should be build by hand by the user. This can be complex ..."
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if a large number of information is involved. Selforganizing neural networks map can automatically generate a document map in which clusters of similar documents are organized. These maps can be used as a navigation tool “per se ” or as a starting point for more complex spatial organizations. Systems
A Comparison of SelfOrganizing Neural Networks for
 of Radar Pulses,” Signal Processing
, 1998
"... Four selforganizing neural networks are compared for automatic deinterleaving of radar pulse streams in electronic warfare systems. The neural networks are the Fuzzy Adaptive Resonance Theory, Fuzzy MinMax Clustering, Integrated Adaptive Fuzzy Clustering, and SelfOrganizing Feature Mapping. Gi ..."
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Four selforganizing neural networks are compared for automatic deinterleaving of radar pulse streams in electronic warfare systems. The neural networks are the Fuzzy Adaptive Resonance Theory, Fuzzy MinMax Clustering, Integrated Adaptive Fuzzy Clustering, and SelfOrganizing Feature Mapping
On Conditions for Intermittent Search in SelfOrganizing Neural Networks
"... Abstract. Selforganizing neural networks (SONN) driven by softmax weight renormalization are capable of finding high quality solutions of difficult assignment optimization problems. The renormalization is shaped by a temperature parameter as the system cools down the assignment weights become incr ..."
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Abstract. Selforganizing neural networks (SONN) driven by softmax weight renormalization are capable of finding high quality solutions of difficult assignment optimization problems. The renormalization is shaped by a temperature parameter as the system cools down the assignment weights become
SelfOrganizing Neural Networks for Behavior Modeling in Games
"... Abstract—This paper proposes selforganizing neural networks for modeling behavior of nonplayer characters (NPC) in first person shooting games. Specifically, two classes of selforganizing neural models, namely SelfGenerating Neural Networks (SGNN) and Fusion Architecture for Learning and COgnit ..."
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Abstract—This paper proposes selforganizing neural networks for modeling behavior of nonplayer characters (NPC) in first person shooting games. Specifically, two classes of selforganizing neural models, namely SelfGenerating Neural Networks (SGNN) and Fusion Architecture for Learning
Convergence Properties of Selforganizing Neural Networks
"... In this paper we analyze the convergence properties of a class of selforganizing neural networks, introduced and popularized by Kohonen, using the ODE approach. It is shown that Kohonen's learning law converges to the best locally affine feature map. A new integrally distributed selforganizin ..."
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In this paper we analyze the convergence properties of a class of selforganizing neural networks, introduced and popularized by Kohonen, using the ODE approach. It is shown that Kohonen's learning law converges to the best locally affine feature map. A new integrally distributed selforganizing
0Forced Accretion andyAssimilation Based on SelfOrganizing Neural Network
"... The high level abstraction is developed layer after layer. This abstraction and generalization are important in language evolution and the study of mental processes. This chapter presents a selforganizing neural network based on cascading a series of layered perceptrons that resemble the developmen ..."
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The high level abstraction is developed layer after layer. This abstraction and generalization are important in language evolution and the study of mental processes. This chapter presents a selforganizing neural network based on cascading a series of layered perceptrons that resemble
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