Overview of computational intelligence application on prediction of global solar radiation

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dc.contributor.authorFashoto, Stephen Gbenga
dc.date.accessioned2017-07-21T11:28:07Z
dc.date.available2017-07-21T11:28:07Z
dc.date.issued2017
dc.descriptionThe conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.en_US
dc.description.abstractComputational Intelligence is not just about robots. It is also about understanding the nature of intelligent thought and action using computers as experimental devices. New applications using computational intelligence are still being developed, although computational intelligence is an established field. The essence of this keynote address is to give a general picture of the research directions which may give an insight into the future of this research area. Meanwhile, an attempt to comprehensively address how computational intelligence may enhance the progress of global solar radiation can be addressed in near future.en_US
dc.description.sponsorshipStrathmore University; Institute of Electrical and Electronics Engineers (IEEE)en_US
dc.identifier.citationFashoto, S. G. (2017). Overview of computational intelligence application on prediction of global solar radiation. In Pan African Conference on Science, Computing and Telecommunications (PACT). Nairobi: Strathmore University. Retrieved from https://su-plus.strathmore.eduen_US
dc.identifier.urihttp://hdl.handle.net/11071/5180
dc.publisherStrathmore Universityen_US
dc.subjectComputational Intelligenceen_US
dc.subjectMethodologies of Computational Intelligenceen_US
dc.subjectGlobal Solar Radiationen_US
dc.titleOverview of computational intelligence application on prediction of global solar radiationen_US
dc.typeLearning Objecten_US
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