Estimating the urban OD matrix: a neural network approach

Article Abstract:

The Hopfield Neural Network (HNN) model was utilized to approximate the urban orientation-destination (OD) distribution matrix from link volumes of the transportation network. The HNN model, unlike the conventional optimization model, was proven to be highly reliable in terms of speedy computation, parallel distributed processing and electrical realization. It also has the ability to determine global optimal solution to problems being investigated.

Research and Development in the Physical, Engineering, and Life Sciences, Transportation, Mathematics, Research, Transportation industry

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Training the random neural network using quasi-Newton methods

Article Abstract:

The use of quasi-Newton methods in training the random neural network is discussed. Details necessary for the application are given and its results in experimental settings described.

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Survey of random neural network applications

Article Abstract:

Random neural network models and related applications are evaluated in real-world engineering applications. The advantages of the models are discussed.

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