Principles of Neurocomputing for Science and Engineering

By F. Ham, I. Kostanic. 2000

673 citations recorded by the source corpus.

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A Shunt Active Power Filter With Enhanced Performance Using ANN-Based Predictive and Adaptive Controllers (2011, methodology): “Here, a neuron is trained by Hebb’s rule [27], [28].”
Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context (2003, unknown): “Features having smaller integer value ranges like duration [0, 58329], wrong_fragment [0, 3], urgent [0, 14], hot [0, 101], num_failed_logins [0, 5], num_compromised [0, 9], su_attempted [0, 2], num_root [0, 7468], num_file_creations [0, 100], num_shells [0, 5], num_access_files [0, 9], count [0, 51”
Adaptive neuro-fuzzy inference system for prediction of water level in reservoir (2006, methodology): “Recently, artificial neural networks (ANNs) have been accepted as a potentially useful tool for modeling complex non-linear systems and widely used for prediction [16].”
FUNDAMENTALS OF NEURAL NETWORKS (2001, methodology): “Curve fitting approximations can also be used for training a neural network [ 10 ].”