FUNDAMENTALS OF NEURAL NETWORKS

By Ali Zilouchian. 2001

887 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d4a402e2610c743beb24cffbe34b44861f026872

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In Artificial Intelligence

Citations in the corpus

Generative Adversarial Networks (2021, neutral): “Deep learning has the potential of discovering comprehensive multilayer models [1] capable of describing probabilities for data used in artificial intelligence applications.”
The competition model: the input, the context, and the brain (2001, background): “Models based on this type of computation are called “connectionist” models (Fausett, 1994), and they can be developed to offer computationally explicit accounts of second language learning.”
Reservoir-system simulation and optimization techniques (2013, methodology): “A neural network is characterized by its architecture that represents the pattern of connection between nodes, its method of determining the connection weights, and the activation function Fausett (1994).”
Predicting surface roughness in machining: a review (2003, background): “It is essentially a mathematical model that mimics the human reasoning and neurobiology and that is based on the following assumptions [35]:”
Efficient Iris Recognition through Improvement of Feature Vector and Classifier (2001, background): “In general, the competitive learning neural network like LVQ has the faster learning mechanism than the error backpropagation algorithm but its performance is easily affected by initial weight vectors [12], [13].”
An empirical model for estimating aquatic invertebrate respiration (2010, background): “They are able to learn and to generalize relationships between input and output data from examples presented to the network (e.g. Dayhoff 1990; Fausett 1994).”