Titlebook: Sensitivity Analysis for Neural Networks; Daniel S. Yeung,Ian Cloete,Wing W. Y. Ng Book 2010 Springer-Verlag Berlin Heidelberg 2010 Adalin

BRINK

Sensitivity Analysis with Parameterized Activation Function,r attempts to generalize Piché’s method by parameterizing antisymmetric squashing activation functions, through which a universal expression of MLP’s sensitivity will be derived without any restriction on input or output perturbations.

sultry

STALE

Critical Vector Learning for RBF Networks,bility as well as the construction of its architecture. Bishop (1991) concluded that an RBF network can provide a fast, linear algorithm capable of representing complex nonlinear mappings. Park and Sandberg (1993) further showed that an RBF network can approximate any regular function. In a statisti

DENT

并入

Applications,odologies for reducing input dimensionality and summarize them in three categories: correlation among features, transformation and neural network sensitivity analysis. Furthermore, we propose a novel method for reducing input dimensionality that uses a stochastic RBFNN sensitivity measure. The exper

agonist

Hyper-Rectangle Model, mathematical expectation used in the hyper-rectangle model reflects the network’s output deviation more directly and exactly than the variance does. Moreover, this approach is applicable to the MLP that deals with infinite input patterns, which is an advantage of the MLP over other discrete feedforward networks like Madalines.

横截,横断

Hypomania

Daniel S. Yeung,Ian Cloete,Wing W. Y. NgThis is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks..Includes supplementary material:

nonchalance

rheumatology

https://doi.org/10.1007/978-3-642-02532-7Adaline; Backpropagation algorithm; Hyperrectangle model; Learning; Multilayer perceptron (MLP); Neural n