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» Optimizing number of hidden neurons in neural networks
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DATE
2003
IEEE
114views Hardware» more  DATE 2003»
14 years 9 days ago
Extraction of Piecewise-Linear Analog Circuit Models from Trained Neural Networks Using Hidden Neuron Clustering
This paper presents a new technique for automatically creating analog circuit models. The method extracts - from trained neural networks - piecewise linear models expressing the l...
Simona Doboli, Gaurav Gothoskar, Alex Doboli
IJCNN
2006
IEEE
14 years 1 months ago
Durability of Affordable Neural Networks against Damages
— In this study, we address the durability of the brain, which is able to operate in various imperfect situations. In our previous research, we have proposed a new network struct...
Yoko Uwate, Yoshifumi Nishio, Ruedi Stoop
IJON
1998
82views more  IJON 1998»
13 years 6 months ago
Solving arithmetic problems using feed-forward neural networks
We design new feed-forward multi-layered neural networks which perform di erent elementary arithmetic operations, such as bit shifting, addition of N p-bit numbers, and multiplica...
Leonardo Franco, Sergio A. Cannas
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
13 years 11 months ago
Evolving the placement and density of neurons in the hyperneat substrate
The Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach demonstrated that the pattern of weights across the connectivity of an artificial neural network ...
Sebastian Risi, Joel Lehman, Kenneth O. Stanley
IWANN
2009
Springer
13 years 11 months ago
Lower Bounds for Approximation of Some Classes of Lebesgue Measurable Functions by Sigmoidal Neural Networks
We propose a general method for estimating the distance between a compact subspace K of the space L1 ([0, 1]s ) of Lebesgue measurable functions defined on the hypercube [0, 1]s ,...
José Luis Montaña, Cruz E. Borges