Search Results for author: Grzegorz Dudek

Found 15 papers, 1 papers with code

Ensembles of Randomized NNs for Pattern-based Time Series Forecasting

no code implementations8 Jul 2021 Grzegorz Dudek, Paweł Pełka

In this work, we propose an ensemble forecasting approach based on randomized neural networks.

Time Series Time Series Forecasting

Data-Driven Learning of Feedforward Neural Networks with Different Activation Functions

no code implementations4 Jul 2021 Grzegorz Dudek

This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning.

Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality

no code implementations4 Jul 2021 Grzegorz Dudek

This work contributes to the development of neural forecasting models with novel randomization-based learning methods.

Time Series Time Series Forecasting

Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression

no code implementations4 Jul 2021 Grzegorz Dudek

However, the problem in randomized learning is how to determine the random parameters.

N-BEATS neural network for mid-term electricity load forecasting

1 code implementation24 Sep 2020 Boris N. Oreshkin, Grzegorz Dudek, Paweł Pełka, Ekaterina Turkina

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.

Decision Making Load Forecasting +1

Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting

no code implementations22 Apr 2020 Paweł{ }Pełka, Grzegorz Dudek

The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition.

Load Forecasting Time Series

Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods

no code implementations29 Mar 2020 Paweł Pełka, Grzegorz Dudek

This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs).

Time Series

Are Direct Links Necessary in RVFL NNs for Regression?

no code implementations29 Mar 2020 Grzegorz Dudek

A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems.

Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study

no code implementations3 Mar 2020 Grzegorz Dudek, Pawel Pelka

In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries.

Load Forecasting Time Series

A Constructive Approach for Data-Driven Randomized Learning of Feedforward Neural Networks

no code implementations4 Sep 2019 Grzegorz Dudek

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.

Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It

no code implementations16 Aug 2019 Grzegorz Dudek

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.

Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameters

no code implementations15 Aug 2019 Grzegorz Dudek

In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed.

Data-Driven Randomized Learning of Feedforward Neural Networks

no code implementations11 Aug 2019 Grzegorz Dudek

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.

A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes

no code implementations13 Oct 2017 Grzegorz Dudek

In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned.

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