In this paper dynamic parameter adjustment in particle swarm optimization (PSO) for modular neural network (MNN) design using granular computing and fuzzy logic (FL) is proposed. Nowadays, there are a plethora of optimization techniques, but their implementations require having knowledge about these techniques in order to establish their parameters, because the performance and final results of a particular technique depend on the optimal parameter values. For this reason, in this paper the fuzzy adjustment of parameters during the execution is proposed, and this proposal allows to adjust the parameters depending on current PSO behavior in each iteration. The proposed method performs modular neural network optimization applied to human recognition using benchmark ear, iris and face databases. Two fuzzy inference systems are proposed to perform this dynamic adjustment, comparisons against a PSO without this dynamic adjustment (simple PSO) are performed to verify if the proposed adjustment using a fuzzy system is better improving recognition rate and execution time. The PSO variants presented in this paper are aimed at performing MNNs optimization. This optimization consists on finding optimal parameters, such as: the number of modules (or sub granules), percentage of data for the training phase, learning algorithm, goal error, number of hidden layers and their number of neurons.
AgarwalV. and BhanotS., “Firefly inspired feature selection for face recognition”, Contemporary Computing (IC3), 2015 Eighth International Conference, 2015, pp. 257–262.
2.
AjiatmoD. and RobandiI., “A hybrid Fuzzy Logic Controller-Firefly Algorithm (FLC-FA) based for MPPT Photovoltaic (PV) system in solar car”, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE), 2016, pp. 606–610.
3.
AlpaydinE., Machine Learning: The New AI: The MIT Press, 2016.
4.
AtisS. and EkrenN., “Development of an outdoor lighting control system using expert system”, Energy and Buildings130 (2016), 773–786.
5.
BagheriM., MiriM. and ShabakhtyN., “Fuzzy reliability analysis using a new alpha level set optimization approach based on particle swarm optimization”, Journal of Intelligent & Fuzzy Systems30(1) (2016), 235–244.
6.
BingülZ. and KarahanO., “A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control”, Expert Systems with Applications38 (2011), 1017–1031.
7.
ButenkovaS., ZhukovaA., NagorovbA. and KrivshacN., “Granular Computing Models and Methods Based on the Spatial Granulation”, Procedia Computer Science103 (2017), 295–302.
8.
ChangB.-M., TsaiH.-H. and ShihJ.-S., “Using fuzzy logic and particle swarm optimization to design a decision-based filter for cDNA microarray image restoration”, Engineering Applications of Artificial Intelligence36 (2014), 12–26.
9.
CosteaA., “Applying Fuzzy Logic and Machine Learning Techniques in Financial Performance Predictions”, Procedia Economics and Finance10 (2014), 4–9.
DaugmanJ., “Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns”, International Journal of Computer Vision45(1) (2001), 25–38.
14.
DiricanC., “The Impacts of Robotics, Artificial Intelligence On Business and Economics”,– , Social and Behavioral Sciences195 (2015), 564–573.
15.
DorigoM., “Optimization, learning and natural algorithms”, Politecnico di Milano, Italy, PhD Thesis, 1992.
16.
EberhartR.C. and KennedyJ., “A New Optimizer using Particle Swarm”, Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43.
17.
EberhartR.C. and ShiY., “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization”, In Proceedings of the IEEE Congress on Evolutionary Computation1 (2000), 84–88.
18.
EngelbrechtA., Fundamentals of Computational Swarm Intelligence: Wiley, 2005.
19.
GaxiolaF., MelinP. and LopezM.A., “Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris Biometric Measurement”, Soft Computing for Recognition Based on Biometrics2010 (2010), 137–153.
20.
GaxiolaF., MelinP., ValdezF., CastroJ.R. and CastilloO., “Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO”, Applied Soft Computing38 (2016), 860–871.
21.
GoldbergD.E., Genetic Algorithms in Search Optimization and Machine Learning: Addison-Wesley, 1989.
22.
GutierrezL., MelinP. and LopezM.A., “Modular neural network integrator for human recognition from ear images”, IJCNN, Barcelona, Spain, 2010, pp. 1–5.
23.
HassounM., Fundamentals of Artificial Neural Networks: A Bradford Book, 2003.
HosgurmathS. and MallappaV.V., “Grey Wolf Optimizer with Linear Collaborative Discriminant Regression Classification based Face Recognition”, International Journal of Intelligent Engineering and Systems12(2) (2019), 202–210.
26.
HoussamiN., LeeC.I., BuistD.S.M. and TaoD., “Artificial intelligence for breast cancer screening: Opportunity or hype?”, The Breast36 (2017), 31–33.
27.
KavianiM. and MirRokniS.M., “Applying Genetic Algorithm in Architecture and Neural Network Training”, IJCSNS International Journal of Computer Science and Network Security17(6) (2017), 118–124.
28.
KennedyJ. and EberhartR.C., “Particle Swarm Optimization”, In Proceedings of the IEEE international Joint Conference on Neuronal Networks (1995), pp. 1942–1948.
29.
KeshtegarB. and BagheriM., “Fuzzy relaxed-finite step size method to enhance the instability of the fuzzy first-order reliability method using conjugate discrete map”, Nonlinear Dynamics91(3) (2018), 1443–1459.
30.
KeshtegarB., BagheriM. and YaseenZ.M., “Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model”, Composite Structures212 (2019), 230–242.
31.
ManK.F., TangK.S. and KwongS., Genetic Algorithms: Concepts and Designs, Springer, Ed., 1999.
32.
MasekL. and KovesiP., “MATLAB Source Code for a Biometric Identification System Based on Iris Patterns”, The School of Computer Science and Software Engineering, The University of Western Australia, 2003.
33.
MelinP., OlivasF., CastilloO., ValdezF., SoriaJ. and ValdezM., “Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic”, Expert Systems with Applications40(1) (2013), 3196–3206.
34.
MelinP., PulidoM. and CastilloO., “Ensemble Neural Network with Type-1 and Type-2 Fuzzy Integration for Time Series Prediction and Its Optimization with PSO”, Imprecision and Uncertainty in Information Representation and Processing, 2016, pp. 375–388.
35.
MirjaliliS., MirjaliliS.M. and LewisA., “Grey Wolf Optimizer”, Advances in Engineering Software69 (2014), 46–61.
36.
Mohd RahimM.S., RehmanA., KurniawanF. and SabaT., “Ear biometrics for human classification based on region features mining”, Biomedical Research28(10) (2017), 4660–4664.
37.
NobileM.S., CazzanigaP., BesozziD., ColomboR., MauriG. and PasiG., “Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization”, Swarm and Evolutionary Computation39 (2018), 70–85.
38.
OlivasF., ValdezF. and CastilloO., “Fuzzy Classification System Design Using PSO with Dynamic Parameter Adaptation Through Fuzzy Logic”, Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics: Springer, 2014, pp. 29–47.
RobatiA., BaraniG.A., Abadi PourH.N., FadaeeM.J. and Pour AnarakiJ.R., “Balanced fuzzy particle swarm optimization”, Applied Mathematical Modelling36 (2012), 2169–2177.
43.
RojasR., Neural Networks: Springer-Verlag, 1996.
44.
RumelhartD.E., HintonG.E. and WilliamsR.J., “Learning representations by back-propagating errors”, Nature323 (1986), 533–536.
45.
SánchezD. and MelinP., Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation, 1st ed.: Springer, 2016.
46.
SánchezD. and MelinP., “Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure”, Engineering Applications of Artificial Intelligence27 (2014), 41–56.
47.
SánchezD., MelinP. and CastilloO., “A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition”, 2017 (2017), 4180510:1–4180510:26.
48.
SánchezD., MelinP. and CastilloO., “Optimization of modular granular neural networks using a firefly algorithm for human recognition”, Engineering Applications of Artificial Intelligence64 (2017), 172–186.
49.
SatoneM. and KharateG., “Feature Selection Using Genetic Algorithm for Face Recognition Based on PCA, Wavelet and SVM”, International Journal on Electrical Engineering and Informatics6(1) (2014), 39–52.
50.
ShiY. and EberhartR., “Parameter selection in particle swarm optimization”, International Conference on Evolutionary Programming (1998), pp. 591–600.
51.
ValdezF., MelinP. and CastilloO., “Fuzzy Control for Dynamical Parameter Adaptation in a Parallel Evolutionary Method Combining Particle Swarm Optimization and Genetic Algorithms”, Soft Computing for Intelligent Control and Mobile Robotics: Springer, 318 (2010), 161–178.
52.
ValdezF., VázquezJ.C. and GaxiolaF., “Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control”, Advances in Fuzzy Systems2018 (2018), 1274969:1–1274969:19.
53.
ValdezF., VázquezJ.C., MelinP. and CastilloO., “Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution”, Applied Soft Computing52 (2017), 1070–1083.
54.
VázquezJ.C. and ValdezF., “Fuzzy logic for dynamic adaptation in PSO with multiple topologies”, IFSA/NAFIPS, 2013, pp. 1197–1202.
55.
VijayM. and JenaD., “PSO based neuro fuzzy sliding mode control for a robot manipulator”, Science Direct4 (2017), 243–256.
56.
YangX.S., “Firefly algorithms for multimodal optimization”, Proc. 5th Symposium on Stochastic Algorithms, Foundations and Applications5792 (2009), 169–178.
57.
YaoY.Y., “Perspectives of granular computing”, IEEE International Conference on granular computing (GrC), pp. 85–90, 2005.
58.
ZadehL.A., “Fuzzy Sets”, Information and Control8 (1965), 338–353.
59.
ZadehL.A., “Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems”, Soft Computing2 (1998), 23–25.
