Back-propagation neural networks for modeling complex systems

By A. Goh. 1995

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Citations in the corpus

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters (2003, methodology): “Among these algorithms, we can classify in several categories using: (i) the connection weights (Garson 1991; Goh 1995), (ii) the connection weights and a fictitious input matrix considering a successive variation of one input variable while the others are kept constant at a fixed value (Lek et…”
Opening the black box - data driven visualization of neural networks (2005, background): “Garson [12] and Goh [15] multiply the weights between layers to obtain the relative importance of the input variables.”
Review and comparison of methods to study the contribution of variables in artificial neural network models (2003, methodology): “It was then necessary to work on methods like contribution or sensitivity analysis to add power to ANNs in their explanatory capacity (Dimopoulos et al., 1995, 1999; Garson, 1991; Goh, 1995; Scardi and Harding, 1999; Lek et al., 1996a,b; Balls et al., 1996; Maier and Dandy, 1996).”
The Prediction of Swell Percent and Swell Pressure by Using Neural Networks (2011, unknown): “A. T. C. Goh, 1995.”
Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir (2019, background): “Similarly, the weights of neurons in the middle layers change in such a way that the error value between the output of neural network and real output is minimized (Demuth et al. 2014; Goh 1995; Rafiq et al. 2001).”
A Bayesian regularized artificial neural network for stock market forecasting (2013, background): “This link is stored as a weighted value which provides a measure of the connection between two nodes (Garson, 1991; Goh, 1995).”
Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey (2013, background): “A common strategy for finding the optimum number of hidden layer neurons starts with a few numbers of neurons and keeps increasing the number of neurons while monitoring the performance criteria, until no significant improvement is observed (Goh 1995).”
Improving the interpretability of deep neural networks with stimulated learning (2015, methodology): “One of the common ways for inspecting the feature importance for neural network models is Garson’s algorithm [4], and was improved by [5].”