Multi Resolution Self Organizing Neural Networks For Pattern Recognition
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Multi-resolution Self-organizing Neural Networks for Pattern Recognition
The MRF-ART neural network employs fast competitive learning and efficient parallel matching to solve complex data classification problems. The architecture of the MRF-ART not only preserves the ART-type neural network's characteristics but also extends its capability to represent input patterns in a hierarchical fashion. To achieve this, the MRF-ART network uses multiple output layers arranged in a cascaded manner which is completely different from a conventional fuzzy ART network with only one output layer. Moreover, the parallel matching process makes the MRF-ART network suitable for hardware implementation.
Issues in the Use of Neural Networks in Information Retrieval
This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality.It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules.Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.
Neural Nets WIRN09
This book reports the proceedings of WIRN09, the 19th Italian Workshop of the Italian Society for Neural Networks (SIREN). Neural networks explore thought mechanisms that efficient computational tools and a representative physics of our brain share together and that ultimately produce the loops of our thoughts. The general approach is to see how these loops run and which tracks they leave.