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NC
1998
101views Neural Networks» more  NC 1998»
13 years 9 months ago
Evolutionary Optimized Tensor Product Bernstein Polynomials versus Backpropagation Networks
In this paper a new approach for approximation problems involving only few input and output parameters is presented and compared to traditional Backpropagation Neural Networks (BP...
Günther R. Raidl, Gabriele Kodydek
IWANN
2001
Springer
14 years 14 days ago
Landmark Recognition for Autonomous Navigation Using Odometric Information and a Network of Perceptrons
In this paper two methods for the detection and recognition of landmarks to be used in topological modeling for autonomous mobile robots are presented. The first method is based o...
Javier de Lope Asiaín, Darío Maraval...
NN
1998
Springer
102views Neural Networks» more  NN 1998»
13 years 7 months ago
A learning model for oscillatory networks
A learning model for coupled oscillators is proposed. The proposed learning rule takes a simple form by which the intrinsic frequencies of the component oscillators and the coupli...
Jun Nishii
AUTOMATICA
2006
90views more  AUTOMATICA 2006»
13 years 8 months ago
An ISS-modular approach for adaptive neural control of pure-feedback systems
Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback s...
Cong Wang, David J. Hill, S. S. Ge, Guanrong Chen
ICANN
2010
Springer
13 years 9 months ago
Recurrence Enhances the Spatial Encoding of Static Inputs in Reservoir Networks
We shed light on the key ingredients of reservoir computing and analyze the contribution of the network dynamics to the spatial encoding of inputs. Therefore, we introduce attracto...
Christian Emmerich, René Felix Reinhart, Jo...