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Erschienen in:
Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

19. Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment

verfasst von : Nikola K. Kasabov

Erschienen in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Verlag: Springer Berlin Heidelberg

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Abstract

This chapter presents methods for using eSNN and BI-SNN for deep, incremental learning and predictive modelling of streaming data and for deep knowledge representation. The methods are applied for predictive modelling in the areas of finance, ecology, transport and environment using respective multisensory streaming data. Each of these applications require specific model design in terms of data preparation, SNN model parameters, experimental setting and validation. Each of the methods are illustrated with case study problems and data, but their applicability can be extended to a wider class of problems where multisensory streaming data is available.

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Vorheriges Kapitel Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
Nächstes Kapitel From von Neumann Machines to Neuromorphic Platforms
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Metadaten
Titel
Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment
verfasst von
Nikola K. Kasabov
Copyright-Jahr
2019
Verlag
Springer Berlin Heidelberg
Buch
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-662-57715-8_19