1,291 research outputs found
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
New Procedures for Model Selection in Feedforward Neural Networks for Time Series Forecasting
The aim of this paper is to propose two new procedures for model selection in Neural Networks (NN) for time series forecasting. Firstly, we focused on the derivation of the asymptotic properties and asymptotic normality of NN parameters estimator. Then, we developed the model building strategies based on statistical concepts particularly statistics test based on the Wald test and the inference of R2 incremental. In this paper, we employ these new procedures in two main approaches for model building in NN, i.e. fully bottom-up or forward scheme by using the inference of R2 incremental, and the combination between forward (by using the inference of R2 incremental) and top-down or backward (by implementing Wald test). Bottom-up approach starts with an empty model, whereas top-down approach begins with a large NN model. We used simulation data as a case study. The results showed that a combination between statistical inference of R2 incremental and Wald test was an effective procedure for model selection in NN for time series forecasting
Methods for Model Complexity Reduction for the Nonlinear Calibration of Amplifiers Using Volterra Kernels
Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation of a large number of parameters and have, consequently, potentially large computational costs. The pruning of Volterra models is thus of fundamental importance to reduce the computational costs of nonlinear calibration, and improve stability and speed, while preserving accuracy. Several techniques (LASSO, DOMP and OBS) and their variants (WLASSO and OBD) are compared in this paper for the experimental calibration of an IF amplifier. The results show that Volterra models can be simplified, yielding models that are 4–5 times sparser, with a limited impact on accuracy. About 6 dB of improved Error Vector Magnitude (EVM) is obtained, improving the dynamic range of the amplifiers. The Symbol Error Rate (SER) is greatly reduced by calibration at a large input power, and pruning reduces the model complexity without hindering SER. Hence, pruning allows improving the dynamic range of the amplifier, with almost an order of magnitude reduction in model complexity. We propose the OBS technique, used in the neural network field, in conjunction with the better known DOMP technique, to prune the model with the best accuracy. The simulations show, in fact, that the OBS and DOMP techniques outperform the others, and OBD, LASSO and WLASSO are, in turn, less efficient. A methodology for pruning in the complex domain is described, based on the Frisch–Waugh–Lovell (FWL) theorem, to separate the linear and nonlinear sections of the model. This is essential because linear models are used for equalization and cannot be pruned to preserve model generality vis-a-vis channel variations, whereas nonlinear models must be pruned as much as possible to minimize the computational overhead. This methodology can be extended to models other than the Volterra one, as the only conditions we impose on the nonlinear model are that it is feedforward and linear in the parameters
Automated Architecture Design for Deep Neural Networks
Machine learning has made tremendous progress in recent years and received
large amounts of public attention. Though we are still far from designing a
full artificially intelligent agent, machine learning has brought us many
applications in which computers solve human learning tasks remarkably well.
Much of this progress comes from a recent trend within machine learning, called
deep learning. Deep learning models are responsible for many state-of-the-art
applications of machine learning. Despite their success, deep learning models
are hard to train, very difficult to understand, and often times so complex
that training is only possible on very large GPU clusters. Lots of work has
been done on enabling neural networks to learn efficiently. However, the design
and architecture of such neural networks is often done manually through trial
and error and expert knowledge. This thesis inspects different approaches,
existing and novel, to automate the design of deep feedforward neural networks
in an attempt to create less complex models with good performance that take
away the burden of deciding on an architecture and make it more efficient to
design and train such deep networks.Comment: Undergraduate Thesi
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