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AMC
2008
99views more  AMC 2008»
13 years 8 months ago
Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of c...
Roderick V. N. Melnik
IWANN
2005
Springer
14 years 2 months ago
Real-Time Spiking Neural Network: An Adaptive Cerebellar Model
Abstract. A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in re...
Christian Boucheny, Richard R. Carrillo, Eduardo R...
IJCNN
2000
IEEE
14 years 29 days ago
Fog Forecasting Using Self Growing Neural Network 'CombNET-II: ' A Solution for Imbalanced Training Sets Problem
This paper proposes a method to solve problem that comes with imbalanced training sets which is often seen in the practical applications. We modi ed Self Growing Neural Network Co...
Anto Satriyo Nugroho, Susumu Kuroyanagi, Akira Iwa...
TNN
1998
146views more  TNN 1998»
13 years 8 months ago
Fuzzy lattice neural network (FLNN): a hybrid model for learning
— This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neu...
Vassilios Petridis, Vassilis G. Kaburlasos
IJCNN
2007
IEEE
14 years 2 months ago
System Identification for the Hodgkin-Huxley Model using Artificial Neural Networks
— A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially ...
Manish Saggar, Tekin Meriçli, Sari Andoni, ...