Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies
1996; Elsevier BV; Linguagem: Inglês
10.1016/b978-012213815-7/50002-9
Autores Tópico(s)Fuzzy Logic and Control Systems
ResumoBackpropagation algorithm is the most popular and widely used of artificial neural networks. A backpropagation neural network (BNN) is constructed from simple processing units called "neurons" or "nodes," which are arranged in a series of layers bounded by input and output layers encompassing a variable number of hidden layers. Each neuron is connected to other neurons in the network by connections of different strengths or weights. This chapter presents an overview of the current usage of the BNNin quantitative structure–activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. Emphasis is placed on practical aspects related to the selection of the training and testing sets, the preprocessing of the data, the choice of an architecture with adequate parameters, and the comparison of models. Advantages and limitations of BNN are discussed, as well as the usefulness of hybrid systems which mix and match a BNN with other intelligent techniques for solving complex modeling problems.
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