4,298 research outputs found
Incremental construction of LSTM recurrent neural network
Long Short--Term Memory (LSTM) is a recurrent neural network that
uses structures called memory blocks to allow the net remember
significant events distant in the past input sequence in order to
solve long time lag tasks, where other RNN approaches fail.
Throughout this work we have performed experiments using LSTM
networks extended with growing abilities, which we call GLSTM.
Four methods of training growing LSTM has been compared. These
methods include cascade and fully connected hidden layers as well
as two different levels of freezing previous weights in the
cascade case. GLSTM has been applied to a forecasting problem in a biomedical domain, where the input/output behavior of five
controllers of the Central Nervous System control has to be
modelled. We have compared growing LSTM results against other
neural networks approaches, and our work applying conventional
LSTM to the task at hand.Postprint (published version
Multi-step learning rule for recurrent neural models: an application to time series forecasting
Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information. The interest in this work is the development of nonlinear neural models for the purpose of building multi-step time series prediction schemes. In that context, the most popular neural models are based on the traditional feedforward neural networks. However, this kind of model may present some disadvantages when a long-term prediction problem is formulated because they are trained to predict only the next sampling time. In this paper, a neural model based on a partially recurrent neural network is proposed as a better alternative. For the recurrent model, a learning phase with the purpose of long-term prediction is imposed, which allows to obtain better predictions of time series in the future. In order to validate the performance of the recurrent neural model to predict the dynamic behaviour of the series in the future, three different data time series have been used as study cases. An artificial data time series, the logistic map, and two real time series, sunspots and laser data. Models based on feedforward neural networks have also been used and compared against the proposed model. The results suggest than the recurrent model can help in improving the prediction accuracy.Publicad
Advanced Methods for Time Series Prediction Using Recurrent Neural Networks
Recurrent Neural Networks for Temporal Data Processing, Intech, pp. 15-36, ISBN 978-953-307-685-0
Energy performance forecasting of residential buildings using fuzzy approaches
The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications.Peer ReviewedPostprint (published version
Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination
An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination
TFN: An Interpretable Neural Network with Time-Frequency Transform Embedded for Intelligent Fault Diagnosis
Convolutional Neural Networks (CNNs) are widely used in fault diagnosis of
mechanical systems due to their powerful feature extraction and classification
capabilities. However, the CNN is a typical black-box model, and the mechanism
of CNN's decision-making are not clear, which limits its application in
high-reliability-required fault diagnosis scenarios. To tackle this issue, we
propose a novel interpretable neural network termed as Time-Frequency Network
(TFN), where the physically meaningful time-frequency transform (TFT) method is
embedded into the traditional convolutional layer as an adaptive preprocessing
layer. This preprocessing layer named as time-frequency convolutional (TFconv)
layer, is constrained by a well-designed kernel function to extract
fault-related time-frequency information. It not only improves the diagnostic
performance but also reveals the logical foundation of the CNN prediction in
the frequency domain. Different TFT methods correspond to different kernel
functions of the TFconv layer. In this study, four typical TFT methods are
considered to formulate the TFNs and their effectiveness and interpretability
are proved through three mechanical fault diagnosis experiments. Experimental
results also show that the proposed TFconv layer can be easily generalized to
other CNNs with different depths. The code of TFN is available on
https://github.com/ChenQian0618/TFN.Comment: 20 pages, 15 figures, 5 table
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