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Showing 1 to 4 of 4 for “"evolutionary neural networks"”.

  1. Evolutionary neural networks

    To create neural networks that work, one needs to specify a structure and the interconnection weights between each pair of connected computing elements. The structure of a network can be selected by the designer depending on the application, although the selection of interconnection weights is a …

    vt Repository record for Evolutionary neural networks (opens in a new tab)

  2. Evolutionary neural networks : models and applications

    … problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to …

    aston Repository record for Evolutionary neural networks : models and applications (opens in a new tab)

  3. Muscle activation mapping of skeletal hand motion: an evolutionary approach.

    … by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a …

    bournemouth Repository record for Muscle activation mapping of skeletal hand motion: an evolutionary approach. (opens in a new tab)

  4. The resurgence of structure in deep neural networks

    Machine learning with deep neural networks ("deep learning") allows for learning complex features directly from raw input data, completely eliminating hand-crafted, "hard-coded" feature extraction from the learning pipeline. This has lead to state-of-the-art performance being achieved across …

    cambridge Repository record for The resurgence of structure in deep neural networks (opens in a new tab)