Neural networks consist of a large class of different architectures. In many
cases, the issue is ... more Neural networks consist of a large class of different architectures. In many cases, the issue is approximating a static nonlinear, mapping f ( ) x with a neural network fNN ( ) x , where x∈RK . The most useful neural networks in function approximation are Multilayer Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Here we concentrate on MLP networks.
Neural networks consist of a large class of different architectures. In many
cases, the issue is ... more Neural networks consist of a large class of different architectures. In many cases, the issue is approximating a static nonlinear, mapping f ( ) x with a neural network fNN ( ) x , where x∈RK . The most useful neural networks in function approximation are Multilayer Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Here we concentrate on MLP networks.
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cases, the issue is approximating a static nonlinear, mapping f ( ) x with a
neural network fNN ( ) x , where x∈RK .
The most useful neural networks in function approximation are Multilayer
Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Here we
concentrate on MLP networks.
cases, the issue is approximating a static nonlinear, mapping f ( ) x with a
neural network fNN ( ) x , where x∈RK .
The most useful neural networks in function approximation are Multilayer
Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Here we
concentrate on MLP networks.