IEEE Transactions on Pattern Analysis and Machine Intelligence
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
Self organized mapping of data clusters to neuron groups
Neural Networks
Information Maximization in a Linear Manifold Topographic Map
Neural Processing Letters
Applied Intelligence
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
A sequential algorithm for training the SOM prototypes based on higher-order recursive equations
Advances in Artificial Neural Systems
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Binary tree time adaptive self-organizing map
Neurocomputing
Pattern Recognition Letters
Incremental self-organizing map (iSOM) in categorization of visual objects
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Hi-index | 0.00 |
The time adaptive self-organizing map (TASOM) network is a modified self-organizing map (SOM) network with adaptive learning rates and neighborhood sizes as its learning parameters. Every neuron in the TASOM has its own learning rate and neighborhood size. For each new input vector, the neighborhood size and learning rate of the winning neuron and the learning rates of its neighboring neurons are updated. A scaling vector is also employed in the TASOM algorithm for compensation against scaling transformations. Analysis of the updating rules of the algorithm reveals that the learning parameters may increase or decrease for adaptation to a changing environment, such that the minimum increase or decrease is achieved according to a specific measure. Several versions of the TASOM-based networks are proposed in this paper for different applications, including bilevel thresholding of grey level images, tracking of moving objects and their boundaries, and adaptive clustering. Simulation results show satisfactory performance of the proposed methods in the implemented applications.