Andere Kunden interessierten sich auch für
![Combat Identification Modeling Using Robust Optimization Techniques]( "Combat Identification Modeling Using Robust Optimization Techniques")
Combat Identification Modeling Using Robust Optimization Techniques61,99 €![Image Fusion Using Autoassociative-Heteroassociative Neural Networks]( "Image Fusion Using Autoassociative-Heteroassociative Neural Networks")
Image Fusion Using Autoassociative-Heteroassociative Neural Networks60,99 €![Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks]( "Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks")
Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks60,99 €![Component Hiding Using Identification and Boundary Blurring Techniques]( "Component Hiding Using Identification and Boundary Blurring Techniques")
Component Hiding Using Identification and Boundary Blurring Techniques60,99 €![Using Upper Layer Weights to Efficiently Construct and Train Feedforward Neural Networks Executing Backpropagation]( "Using Upper Layer Weights to Efficiently Construct and Train Feedforward Neural Networks Executing Backpropagation")
Using Upper Layer Weights to Efficiently Construct and Train Feedforward Neural Networks Executing Backpropagation62,99 €![Using Neural Networks for Estimating Cruise Missile Reliability]( "Using Neural Networks for Estimating Cruise Missile Reliability")
Using Neural Networks for Estimating Cruise Missile Reliability62,99 €![An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks]( "An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks")
An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks60,99 €
The purposes of this research were: (1) validating Kim's (2007) simulation method by applying analytic methods and (2) comparing the two different Robust Parameter Design methods with three measures of performance (label accuracy for enemy, friendly, and clutter). Considering the features of CID, input variables were defined as two controllable (threshold combination of detector and classifier) and three uncontrollable (map size, number of enemies and friendly). The first set of experiments considers Kim's method using analytical methods. In order to create response variables, Kim's method uses Monte Carlo simulation. The output results showed no difference between simulation and the analytic method. The second set of experiments compared the measures of performance between a standard RPD used by Kim and a new method using Artificial Neural Networks (ANNs). To find optimal combinations of detection and classification thresholds, Kim's model uses regression with a combined array design, whereas the ANNs method uses ANN with a crossed array design. In the case of label accuracy for enemy, Kim's solution showed the higher expected value, however it also showed a higher variance. Additionally, the model's residuals were higher for Kim's model.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.