Graph structure of neural networks
[DOC File]ECE 539: Artificial Neural Networks
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Determination of Network Structure. The first step taken in the creation of an analytical MLP tool is determining what network structure to use. Many different configurations were tested by setting up an MLP with that configuration, performing training, then using the trained MLP to analyze the training data.
[DOC File]Artificial Neural Network Based Quantitative Structural ...
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The optimal QSPR model was developed based on a 4-4–1 artificial neural network architecture using molecular descriptors calculated from molecular structure alone. The root mean square errors (RMSE) in normal boiling points predictions were 4.46°C for the training set, 3.86°C for the validation set and 4.99 °C for the prediction set.
[DOC File]Analysis of Trained Neural Networks
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Artificial neural networks, analysis, sensitivity, graph theory. 1 Introduction. Neural Networks have been used as an effective method for solving engineering problems in a wide range of application areas. In most of the applications it is essential to be able to ensure that the networks perform as desired in real-life situations.
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Fig. 1 shows schematic structure of an artificial neural. In Fig. 1 “p” and “a” are the input and output to a neural, respectively. ... shows the comparative graph of observed and ...
[DOC File]Modular Neural Networks for Modeling of a Nonlinear ...
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Instructable and adaptive software agents, Web mining, machine learning, neural networks, information retrieval, information extraction 1 Introduction The rapid growth of information on the World Wide Web has boosted interest in using machine learning techniques to solve the problems of retrieving and extracting textual information from the Web ...
[DOC File]3
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An effectiveness of neural networks with hidden neurons is illustrated by Boolean function XOR, which is not correctly classified by simple perceptron without hidden neurons. The used neural networks will contain three layers, the first layer is composed of input neuron, the second one of hidden neurons, and the third (last) one of output neurons.
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