By David J. Livingstone
During this ebook, foreign specialists file the background of the applying of ANN to chemical and organic difficulties, offer a advisor to community architectures, education and the extraction of principles from proficient networks, and canopy many state of the art examples of the appliance of ANN to chemistry and biology. tools concerning the mapping and interpretation of Infra pink spectra and modelling environmental toxicology are incorporated. This ebook is a wonderful consultant to this fascinating box.
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During this e-book, overseas specialists document the heritage of the appliance of ANN to chemical and organic difficulties, offer a consultant to community architectures, education and the extraction of principles from proficient networks, and canopy many state of the art examples of the applying of ANN to chemistry and biology.
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Extra resources for Artificial Neural Networks: Methods and Applications (Methods in Molecular Biology, Vol. 458)
Because the terminology and symbols vary from one method to another and this leads to confusion, the symbols used here are consistent even if a little unfamiliar in some circumstances. As an example, the term coefficients is used in linear regression whereas the term weights is used in the literature of neural networks. Choices, therefore, have to be made; and in this case, the term weights has been chosen, since the chapter is about neural networks. For readers with an interest in Bayesian methods, a comprehensive overview applied to pattern recognition and regression is given by Bishop  and Nabney .
Once the position (central neuron c ) of the input vector is defined, the weights of the input and 4 Kohonen and Counterpropagation Neural Networks 51 output layers of the counterpropagation neural network are corrected accordingly. The corrections in the output layer are defined next (Eq. 4), while the corrections of the weights in the input layer were given previously, see Eq. 2 . 2. Properties of Trained Counterpropagation Neural Networks The input layer of the trained counterpropagation neural network is identical to the Kohonen neural network; it accommodates all objects from the training set.
8) Using these equations, the values of α, β, and γ are computed, following the minimization of S(w), by using Eqs. 9) in an iterative loop that converges in γ. The time-consuming step in this cycle is the production of the eigenvalues, λi. However, for most QSAR problems, this loop is much faster than the minimization of S(w), which uses a conjugate-gradient or some such minimizer. 4 shows the flow chart of a typical BRANN. References 1. Burden FR, Winkler DA (1999) Robust QSAR models using Bayesian regularized neural networks.
Artificial Neural Networks: Methods and Applications (Methods in Molecular Biology, Vol. 458) by David J. Livingstone