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IAJIT
2010
117views more  IAJIT 2010»
8 years 10 months ago
Development of Neural Networks for Noise Reduction
: This paper describes the development of neural network models for noise reduction. The networks used to enhance the performance of modeling captured signals by reducing the effec...
Lubna Badri
ECCC
2000
59views more  ECCC 2000»
8 years 12 months ago
A Simple Model for Neural Computation with Firing Rates and Firing Correlations
A simple extension of standard neural network models is introduced which provides a model for neural computations that involve both firing rates and firing correlations. Such an ex...
Wolfgang Maass
TNN
2008
93views more  TNN 2008»
9 years 1 days ago
Towards the Optimal Design of Numerical Experiments
This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagre...
S. Gazut, J.-M. Martinez, Gérard Dreyfus, Y...
NECO
2008
170views more  NECO 2008»
9 years 3 days ago
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Nicolas Le Roux, Yoshua Bengio
JUCS
2008
181views more  JUCS 2008»
9 years 3 days ago
An IP Core and GUI for Implementing Multilayer Perceptron with a Fuzzy Activation Function on Configurable Logic Devices
: This paper describes the development of an Intellectual Property (IP) core in VHDL able to implement a Multilayer Perceptron (MLP) artificial neural network (ANN) topology with u...
Alfredo Rosado Muñoz, Luis Gómez-Cho...
JCP
2008
190views more  JCP 2008»
9 years 4 days ago
Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach
In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonl...
Vishwas R. Puttige, Sreenatha G. Anavatti
ESWA
2008
223views more  ESWA 2008»
9 years 6 days ago
Credit risk assessment with a multistage neural network ensemble learning approach
In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the ...
Lean Yu, Shouyang Wang, Kin Keung Lai
ICTAI
2007
IEEE
9 years 6 months ago
CompoNet: Programmatically Embedding Neural Networks into AI Applications as Software Components
The provision of embedding neural networks into software applications can enable variety of Artificial Intelligence systems for individual users as well as organizations. Previous...
Uzair Ahmad, Andrey Gavrilov, Sungyoung Lee, Young...
WWW
2005
ACM
10 years 25 days ago
Adaptive page ranking with neural networks
Recent developments in the area of neural networks provided new models which are capable of processing general types of graph structures. Neural networks are well-known for their ...
Franco Scarselli, Sweah Liang Yong, Markus Hagenbu...
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