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There is a probability that the neural network misclassifies a grasp.
If the neural network-based diagnosis fails, a case-base is used to realize a more specific diagnosis.
The knowledge representation and reasoning of model-based and rule-based diagnoses are symbol-based, while those of neural network-based and case-based diagnoses are instance-based.
The results obtained with the hybrid model are better than those using just a neural network to solve the problem, as shown in the table.
I describe an example of a potential synergy based on studies of neural network pruning.
This explains the criticism of neural network modeling and connectionism (p. 33 and other parts of the book).
From a functional point of view, a single neural network may be considered sometimes as a pattern generating and/or a pattern recognizing device.
To assess the influence of external and internal factors on the neural network accuracy, a three-phase experiment is created.
A neural network approach to hippocampal function in classical conditioning.
An artificial neural network constitutes the "brain" that controls the agent's behavior.
The neural network could not predict test results when the interval exceeded 2 days.
These investigations have examined clinical interventions for depression based upon a neural network model.
A linear predictor model is developed and used as a benchmark for evaluating the performance of the neural network.
By sampling the trained neural network, approximations of likelihoods were obtained for design variable values, and, consequently, constraint satisfaction problemsolving capability was achieved.
This information will be used for formulation of the problem and will be input to the neural network as well.
