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A dynamic neural network architecture with immunology inspired optimization for weather data forecasting
یک معماری شبکه عصبی پویا با ایمنولوژی بهینه سازی برای پیش بینی داده های آب و هوایی-2018 Recurrent neural networks are dynamical systems that provide for memory
capabilities to recall past behaviour, which is necessary in the prediction of time series. In this
paper, a novel neural network architecture inspired by the immune algorithm is presented and
used in the forecasting of naturally occurring signals, including weather big data signals. Big
Data Analysis is a major research frontier, which attracts extensive attention from academia,
industry and government, particularly in the context of handling issues related to complex
dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors,
and ambient intelligence systems led to an exponential growth of data in the climate domain. In
this study, we concentrate on the analysis of big weather data by using the Dynamic Self
Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the
network focuses on the local properties of the signal using a self-organised hidden layer inspired
by the immune algorithm, while the recurrent links of the network aim at recalling previously
observed signal patterns. The proposed network exhibits improved performance when compared
to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the
Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used
in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps
ahead prediction. Improvements in the prediction results are observed with respect to the mean
value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional
computational complexity, due to presence of recurrent links.
Keywords: Recurrent Neural Networks ،Immune Systems Optimisation، Time Series Data analytics ، weather forecasting |
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