کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6855298 | 1437611 | 2018 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
PIE-RSPOP: A brain-inspired pseudo-incremental ensemble rough set pseudo-outer product fuzzy neural network
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
A novel pseudo-incremental ensemble rough set pseudo-outer product (PIE-RSPOP) fuzzy neural network is proposed for learning and prediction of trends in complex temporal series. It incorporates four theories pertaining to the learning and memorizing mechanisms in human beings. BCM theory of synaptic metaplasticity replaces Hebbian learning of weights of fuzzy rules and allows for a more natural associative-dissociative learning of weights. Short term forgetting of weights of the fuzzy rules is integrated for rules that are recalled and rules that are not recalled by an instance of data through Ebbinghaus theory of forgetting due to decay and displacement. The long term forgetting of networks in the ensemble is also incorporated through exponential decay of weights based on the age and strengths of the networks. Lastly, a hippocampal mechanism for caching the memories for subsequent recall is proposed. Another contribution of PIE-RSPOP is its ability to deal with concept drift and less storage of historical data. Lastly, despite the usually uninterpretable nature of incremental ensembles of fuzzy networks, a scheme to derive simple interpretable single knowledge base is also proposed. Variety of numerical results on standard datasets are used to demonstrate the advantages of PIE-RSPOP over other incremental learning methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 95, 1 April 2018, Pages 172-189
Journal: Expert Systems with Applications - Volume 95, 1 April 2018, Pages 172-189
نویسندگان
Aparna Ramesh Iyer, Dilip K. Prasad, Chai Hiok Quek,