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Full-Text Articles in Engineering
Adaptive Neural Network Controller For Atm Traffic, Jeffrey E. Larson
Adaptive Neural Network Controller For Atm Traffic, Jeffrey E. Larson
Theses and Dissertations
Broadband-Integrated Services Digital Networks (B-ISDN), along with Asynchronous Transfer Mode (ATM), were designed to meet the requirements of modern communication networks to handle multiple users and a wide variety of diverse traffic including voice, data and video. ATM responds to requests for admission to the network by analyzing whether or not the grade of service (GOS) requirement, specified in the admission request, can be guaranteed without violating the GOS guaranteed to traffic already accepted into the network. The GOS is typically a parameter such as cell loss rate (CLR), average delay, or some other measurement associated with network performance. In …
An Investigation Of Preliminary Feature Screening Using Signal-To-Noise Ratios, David B. Sumrell
An Investigation Of Preliminary Feature Screening Using Signal-To-Noise Ratios, David B. Sumrell
Theses and Dissertations
A new saliency metric and a new saliency screening method are developed. This new metric, the SN saliency metric, is based upon signal-to-noise ratios, where the signal is provided by a sum of squared weights associated with a given feature, and the noise is based upon a sum of squared weights associated with a reference noise feature which is injected into the data. The resultant metric allows for a direct comparison of the feature of interest with a reference noise feature which is known to be nonsalient. The SN saliency screening method, which uses the SN saliency metric, offers the …
Configuring The Radial Basis Function Neural Network, Insoo Sohn
Configuring The Radial Basis Function Neural Network, Insoo Sohn
Theses
The most important factor in configuring an optimum radial basis function (RBF) network is the training of neural units in the hidden layer. Many algorithms have been proposed, e.g., competitive learning (CL), to train the hidden units. CL suffers from producing "dead-units." The other major factor Which was ignored in the past is the appropriate selection of the number of neural units in the hidden layer. The frequency sensitive competitive learning (FSCL) algorithm was proposed to alleviate the problem of dead-units, but it does not alleviate the latter problem. The rival penalized competitive learning (RPCL) algorithm is an improved version …