Som Input
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منابع مشابه
The Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملThe Time Adaptive Self-organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملSomatostatin-Expressing Inhibitory Interneurons in Cortical Circuits
Cortical inhibitory neurons exhibit remarkable diversity in their morphology, connectivity, and synaptic properties. Here, we review the function of somatostatin-expressing (SOM) inhibitory interneurons, focusing largely on sensory cortex. SOM neurons also comprise a number of subpopulations that can be distinguished by their morphology, input and output connectivity, laminar location, firing p...
متن کاملComparing the PLSOM and the SOM over normal distributed input spaces
The Self-Organizing Map (SOM) [11, 15] is a method for mapping data relationships and distributions in high dimensions to lower dimensions, where they are easier to visualize and process. One of the problems with the SOM is that it needs an externally applied annealing scheme to learn mappings. There is no firm theoretical guidance for selecting annealing schemes and their parameters, something...
متن کاملSelf-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization
In the real world, it is not always true that the nextdoor house is close to my house, in other words, “neighbors” are not always “true neighbors”. In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain the m...
متن کاملMonthly runoff forecasting by means of artificial neural networks (ANNs)
Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...
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تاریخ انتشار 2007