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Found 21 papers, 5 papers with code
A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes
In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned.
Data-Driven Randomized Learning of Feedforward Neural Networks
In this work, a method which adjusts the random parameters, representing the slopes and positions of the sigmoids, to the target function features is proposed.
Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameters
In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed.
Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It
The standard method of generating random weights and biases in feedforward neural networks with random hidden nodes, selects them both from the uniform distribution over the same fixed interval.
A Constructive Approach for Data-Driven Randomized Learning of Feedforward Neural Networks
Feedforward neural networks with random hidden nodes suffer from a problem with the generation of random weights and biases as these are difficult to set optimally to obtain a good projection space.
Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study
In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries.
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs).
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting.
Are Direct Links Necessary in RVFL NNs for Regression?
A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems.
Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting
The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition.
N-BEATS neural network for mid-term electricity load forecasting
We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem.
Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression
However, the problem in randomized learning is how to determine the random parameters.
Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality
This work contributes to the development of neural forecasting models with novel randomization-based learning methods.
Data-Driven Learning of Feedforward Neural Networks with Different Activation Functions
This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning.
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting
In this work, we propose an ensemble forecasting approach based on randomized neural networks.
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
A multi-layer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS.
ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance.
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting
The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling.
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality.
STD: A Seasonal-Trend-Dispersion Decomposition of Time Series
To deal with heteroscedasticity in time series, the method proposed in this work -- a seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal component and component related to the dispersion of the time series.
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
