This study presents a fuzzy brain emotional learning classifier (FBELC), combined with a modified particle swarm optimization (PSO) algorithm, that allows a network to automatically determine the optimum values for a reward signal and a classification threshold. The designed FBELC model imitates the brain decision process including the emotion information. To verify the predictive performance, a novel fitness function based on the accuracy of the training and cross-validation datasets is used for a PSO algorithm. This PSO-FBELC model is used to diagnose breast tumors and heart diseases. A comparison of simulations using the proposed PSO-FBELC with other processes shows that the proposed model performs better in terms of recognition accuracy.
MorénJ. and BalkeniusC., A computational model of emotional learning in the amygdala, Simulation of Adaptive Behavior (2000).
2.
CésarM.B., GonçalvesJ., CoelhoJ. and RuiC.D.B., Brain emotional learning based control of a SDOF structural system with a MR damper, CONTROLO402 (2017), 547–557.
3.
JafariM., XuH. and CarrilloL.R.G., Brain emotional learning-based intelligent controller for flocking of multi-agent systems, American Control Conference IEEE (2017), 1996–2001.
4.
LinC.M. and ChungC.C., Fuzzy brain emotional learning control system design for nonlinear systems, International Journal of Fuzzy Systems17 (2015), 117–128.
5.
ZhouQ., ChaoF. and LinC.M., A functional-link-based fuzzy brain emotional learning network for breast tumor classification and chaotic system synchronization, International Journal of Fuzzy Systems20 (2017), 1–17.
6.
JavadiB., SetayeshiS. and PriceG., Applying brain emotional learning based fuzzy inference system for EEG signal classication between schizophrenics and control participant. In: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2017), 1–8.
7.
AsadM.U., FarooqU., GuJ., AminJ., SadaqatA., El-HawaryM.E. and LuoJ., Neo-fuzzy supported brain emotional learing based pattern recognizer for classification problems, IEEE Access5 (2017), 6951–6968.
8.
ZamirpourE. and MoslehM., A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network, Biologically Inspired Cognitive Architectures26 (2018), 80–90.
9.
MiladH.S.A., FarooqU., El-HawaryM.E. and AsadM.U., Neo-fuzzy integrated adaptive decayed brain emotional learning network for online time series prediction, IEEE Access5 (2016), 1037–1049.
10.
MiladH.S.A., FarooqU., El-HawaryM.E., BalasV.E. and AsadM.U., Fuzzy logic based parameter adjustment model for adaptive decayed brain emotional learning network with application to online time series prediction, 2017 IEEE Electrical Power and Energy Conference (2017), 1–6.
11.
ZhouQ., LinC. and ChaoF., Adaptive noise cancelation using fuzzy brain emotional learning network, UK workshop on computational intelligence (2017), 171–182.
12.
KennedyJ. and EberhartR., Particle swarm optimization, International Conference on Networks (1995), 1942–1948.
13.
WangS. and LiuY., A PSO-based fuzzy-controlled searching for the optimal charge pattern of Li-Ion batteries, IEEE Transactions on Industrial Electronics62 (2015), 2983–2993.
14.
ChenS. and HsinW., Weighted fuzzy interpolative reasoning based on the slopes of fuzzy sets and particle swarm optimization techniques, IEEE Transactions on Cybernetics45 (2015), 1250–1261.
15.
BahraniL.T.A., PatraJ.C. and KowalczykR., Multi-gradient PSO algorithm for economic dispatch of thermal generating units in smart grid, IEEE Innovative Smart Grid Technologies Asia, (2016) 258–263.
16.
LinC.M. and LeT.L., WCMAC-based control system design for nonlinear systems using PSO, Journal of Intelligent and Fuzzy Systems33 (2017), 807–818.
17.
LinC.M. and LeT.L., PSO-self-organizing interval type-2 fuzzy neural network for antilock braking systems, Journal of Intelligent and Fuzzy Systems19 (2017), 1362–1374.
18.
PhoemphonS., So-InC. and NiyatoD.T., A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization, Applied Soft Computing65 (2018), 101–120.
19.
YuJ., DingF., ZhaoX. and ZhouF., Error compensation method of magnetometer for attitude measurement using modified artificial bee colony algorithm, International Symposium on Computational Intelligence and Design (2017), 348–351.
20.
NeagoeV. and NeghinaC., An artificial bee colony approach for classification of remote sensing imagery, International Conference on Electronics, Computers and Artificial Intelligence, (2018), 1–4.
21.
KokareM.B. and TadeS.V., Application of artificial bee colony method for unit commitment, International Conference on Computing Communication Control and Automation (2018), 1–6.
22.
LiuS. and LiZ., A modified genetic algorithm for community detection in complex networks, International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (2017), 1–3.
23.
ÇínaroğluS. and BodurS., A new hybrid approach based on genetic algorithmforminimumvertex cover, Innovations in Intelligent Systems and Applications (2018), 1–5.
24.
PravesjitS. and KantawongK., An improvement of genetic algorithm for optimization problem, International Conference on Digital Arts, Media and Technology (2017), 226–229.
25.
DorrahH.T., El-GarhyA.M. and El-ShimyM.E., PSO-BELBIC scheme for two-coupled distillation column process, Journal of Advanced Research2 (2011), 73–83.
26.
MeiY., YangX., LiuZ., WuH. and GuoJ., Real-time facial expression recognition based on the improved brain emotional learning model, Chinese Control Conference (2018), 3924–3928.
27.
FakhrmoosavyS.H., SetayeshiS. and SharifiA., An intelligent method for generating artificial earthquake records based on hybrid PSO–parallel brain emotional learning inspired model, Engineering with Computers34 (2018), 449–463.
28.
LeT.L., LinC.M. and HuynhT.T., Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO, Applied Soft Computing73 (2018), 418–433.
29.
SunY. and LinC.M., A fuzzy brain emotional learning classifier design and application in medical diagnosis, Polytechnica Hungarica16 (2019), 27–43.
30.
MehrabianA.R. and LucasC., Emotional learning based intelligent robust adaptive controller for stable uncertain nonlinear systems, International Journal of Computational Intelligence2 (2005), 1304–4508.
31.
PengW., YangZ., LiuC., XiuJ. and ZhangZ., An improved PSO algorithm for battery parameters identification optimization based on thevenin battery model, IEEE International Conference on Cloud Computing and Intelligence Systems, (2018), 295–298.
32.
SatapathyS.K., DehuriS. and JagadevA.K., EEG signal classification using PSO trained RBF neural network for epilepsy identification, Informatics in Medicine Unlocked6 (2017), 1–11.
33.
LópezM.G., PonceP., SorianoL.A., MolinaA. and RivasJ.J.R., A novel fuzzy-PSO controller for increasing the lifetime in power electronics stage for brushless DC drives, IEEE Access7 (2019), 47841–47855.
PolatK. and GüneS., Breast cancer diagnosis using least square support vector machine, Digital Signal Processing17 (2007), 694–701.
36.
CyrilG. and EricG., A probabilistic interpretation of precision, recall and F-score, with implication for evaluation, International Journal of Radiation Biology & Related Studies in Physics Chemistry & Medicine51 (2005), 952–952.
37.
MakhoulJ., KubalaF., SchwartzR. and WeischedelR., Performance measures for information extraction, Proceedings of DAROA Broadcast News Workshop (1999), 249–252.
38.
ChintaP.M., BalamuruganP., ShevadeS. and MurtyM.N., Optimizing F-measure with non-convex loss and sparse linear classifiers, The 2013 International Joint Conference on Neural Networks (2013), 1–8.
