283,452 research outputs found
The application of neural networks in active suspension
This thesis considers the application of neural networks to automotive suspension
systems. In particular their ability to learn non-linear feedback control
relationships. The speed of processing, once trained, means that neural networks
open up new opportunities and allow increased complexity in the control
strategies employed.
The suitability of neural networks for this task is demonstrated here using multilayer
perceptron, (MLP) feed forward neural networks applied to a quarter vehicle
simulation model. Initially neural networks are trained from a training data set
created using a non-linear optimal control strategy, the complexity of which
prohibits its direct use. They are shown to be successful in learning the
relationship between the current system states and the optimal control. [Continues.
Neural networks can detect model-free static arbitrage strategies
In this paper we demonstrate both theoretically as well as numerically that
neural networks can detect model-free static arbitrage opportunities whenever
the market admits some. Due to the use of neural networks, our method can be
applied to financial markets with a high number of traded securities and
ensures almost immediate execution of the corresponding trading strategies. To
demonstrate its tractability, effectiveness, and robustness we provide examples
using real financial data. From a technical point of view, we prove that a
single neural network can approximately solve a class of convex semi-infinite
programs, which is the key result in order to derive our theoretical results
that neural networks can detect model-free static arbitrage strategies whenever
the financial market admits such opportunities
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