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Brain Emotional Learning-Inspired Models
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
2014 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press , 2014. , s. v, 31
Serie
Halmstad University Dissertations ; 8
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:hh:diva-25428ISBN: 978-91-87045-16-5 (tryckt)ISBN: 978-91-87045-15-8 (digital)OAI: oai:DiVA.org:hh-25428DiVA, id: diva2:719765
Presentation
2014-06-17, 13:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2014-06-02 Skapad: 2014-05-27 Senast uppdaterad: 2018-05-24Bibliografiskt granskad
Delarbeten
1. Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting
Öppna denna publikation i ny flik eller fönster >>Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting
2012 (Engelska)Ingår i: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012), Vol. 1, Piscataway, NJ: IEEE Press, 2012, s. 532-539, artikel-id 6495090Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper presents a new architecture based on a brain emotional learning model that can be us.ed in a wide varieties of AI applications such as prediction, identification and classification. The architecture is referred to as: Brain Emotional Learning Based Fuzzy Inference System (BELFIS) and it is developed from merging the idea of prior emotional models with fuzzy inference systems. The main aim of this model is presenting a desirable learning model for chaotic system prediction imitating the brain emotional network. In this research work, the model is used for predicting the solar activity, since it has been recognized as a threat to critical infrastructures in modern society. Specifically sunspot numbers are predicted by applying the proposed brain emotional learning model. The prediction results are compared with the outcomes of using other previous models like the locally linear model tree (LOLIMOT) and radial bias function (RBF) and adaptive neuro-fuzzy inference system (ANFIS). © 2012 IEEE.

Ort, förlag, år, upplaga, sidor
Piscataway, NJ: IEEE Press, 2012
Nyckelord
brain emotional learning, fuzzy inference system, multi-year ahead prediction, solar activity forecasting, solar cycle 23, sunspot chaotic time series
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:hh:diva-19531 (URN)10.1109/ICTAI.2012.78 (DOI)000320861900069 ()2-s2.0-84876835024 (Scopus ID)978-1-4799-0227-9 (ISBN)978-0-7695-4915-6 (ISBN)
Konferens
24th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2012, Athens, Greece, November 7-9, 2012
Tillgänglig från: 2012-09-07 Skapad: 2012-09-07 Senast uppdaterad: 2018-03-22Bibliografiskt granskad
2. Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS)
Öppna denna publikation i ny flik eller fönster >>Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS)
2013 (Engelska)Ingår i: International Journal of Reasoning-based Intelligent Systems, ISSN 1755-0556, Vol. 5, nr 2, s. 113-126Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

In this paper an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called BELRFS, which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networksto mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognized as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and Auroral Electrojet (AE) index. The obtained results of BELRFS are compared with Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy. Copyright © 2013 Inderscience Enterprises Ltd.

Ort, förlag, år, upplaga, sidor
Olney, Bucks, UK: InderScience Publishers, 2013
Nyckelord
Brain emotional learning, Chaotic time series, Neuro-fuzzy adaptive networks, Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm, Space weather forecasting, Solar activity forecasting
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:hh:diva-24452 (URN)10.1504/IJRIS.2013.057273 (DOI)2-s2.0-84892145721 (Scopus ID)
Forskningsfinansiär
KK-stiftelsen
Anmärkning

Special Issue on Innovations of Intelligent Systems and Engineering; This paper is a revised and expanded version of a paper entitled ‘Neuro-fuzzy models, BELRFS and LoLiMoT, for prediction of chaotic time series’ presented at the INISTA’12, Trabzon, 2–4 July, 2012.

Tillgänglig från: 2014-01-27 Skapad: 2014-01-27 Senast uppdaterad: 2018-03-22Bibliografiskt granskad
3. An emotional learning-inspired ensemble classifier (ELiEC)
Öppna denna publikation i ny flik eller fönster >>An emotional learning-inspired ensemble classifier (ELiEC)
2013 (Engelska)Ingår i: Proceedings of the 2013 Federated Conference on Computer Science and Information Systems (FedCSIS) / [ed] M. Ganzha, L. Maciaszek & M. Paprzycki, Los Alamitos, CA: IEEE Computer Society, 2013, s. 137-141, artikel-id 6643988Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

In this paper, we suggest an inspired architecture by brain emotional processing for classification applications. The architecture is a type of ensemble classifier and is referred to as 'emotional learning-inspired ensemble classifier' (ELiEC). In this paper, we suggest the weighted k-nearest neighbor classifier as the basic classifier of ELiEC. We evaluate the ELiEC's performance by classifying some benchmark datasets. © 2013 Polish Information Processing Society.

Ort, förlag, år, upplaga, sidor
Los Alamitos, CA: IEEE Computer Society, 2013
Serie
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Nyckelord
brain, learning (artificial intelligence), pattern classification, ELiEC, brain emotional processing, emotional learning-inspired ensemble classifier, weighted k-nearest neighbor classifier, Accuracy, Benchmark testing, Brain models, Data models, Iris, Training data
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:hh:diva-25466 (URN)000347171500021 ()2-s2.0-84892547009 (Scopus ID)978-83-60810-52-1 (ISBN)978-1-4673-4471-5 (ISBN)978-83-60810-53-8 (ISBN)
Konferens
2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Krakow, Poland, 8-11 September 2013
Tillgänglig från: 2014-06-02 Skapad: 2014-06-02 Senast uppdaterad: 2018-03-22Bibliografiskt granskad

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