2,816 research outputs found
Real-time support for high performance aircraft operation
The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown
Learning with Local Gradients at the Edge
To enable learning on edge devices with fast convergence and low memory, we
present a novel backpropagation-free optimization algorithm dubbed Target
Projection Stochastic Gradient Descent (tpSGD). tpSGD generalizes direct random
target projection to work with arbitrary loss functions and extends target
projection for training recurrent neural networks (RNNs) in addition to
feedforward networks. tpSGD uses layer-wise stochastic gradient descent (SGD)
and local targets generated via random projections of the labels to train the
network layer-by-layer with only forward passes. tpSGD doesn't require
retaining gradients during optimization, greatly reducing memory allocation
compared to SGD backpropagation (BP) methods that require multiple instances of
the entire neural network weights, input/output, and intermediate results. Our
method performs comparably to BP gradient-descent within 5% accuracy on
relatively shallow networks of fully connected layers, convolutional layers,
and recurrent layers. tpSGD also outperforms other state-of-the-art
gradient-free algorithms in shallow models consisting of multi-layer
perceptrons, convolutional neural networks (CNNs), and RNNs with competitive
accuracy and less memory and time. We evaluate the performance of tpSGD in
training deep neural networks (e.g. VGG) and extend the approach to multi-layer
RNNs. These experiments highlight new research directions related to optimized
layer-based adaptor training for domain-shift using tpSGD at the edge
Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives
In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well
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