Optimal design of a general type-2 fuzzy classifier for the pulse level and its hardware implementation

Abstract . Nowadays, soft computing has been of great help in solving real-world problems and satisfying the needs in our everyday life. We require more than ever the development and implementation of models and techniques that assist in medical issues due to the recent pandemic. The aim of this work is the development and hardware implementation of a general type-2 fuzzy classifier for the pulse levels and the optimization of the general type-2 membership functions parameters using the Ant Lion Optimizer to compare results of type 1 and interval type-2 fuzzy classifiers with the patients of the Framingham database. This study additionally explains the implementation process of the general type-2 fuzzy classifier on the Jetson Nano hardware Development Board and the comparison of execution time with interval type-2, and type-1 fuzzy classifiers. This work offers a novel perspective in that the general type-2 fuzzy classifier can be implemented for embedded applications with excellent performance regarding hardware resources consumption.

[1]  Chun-Hsian Huang,et al.  HDA: Hierarchical and dependency-aware task mapping for network-on-chip based embedded systems , 2020, J. Syst. Archit..

[2]  Oscar Castillo,et al.  Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic , 2014, IEEE Transactions on Fuzzy Systems.

[3]  Oscar Castillo,et al.  Implementation of a Fuzzy Controller for an Autonomous Mobile Robot in the PIC18F4550 Microcontroller , 2020, Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine.

[4]  Robert Ivor John,et al.  Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice , 2016, Inf. Sci..

[5]  Luis Garcia,et al.  An experimental realization of a pulsed control method for the KSS chaotic circuit , 2011, IEEE Latin America Transactions.

[6]  Oscar Castillo,et al.  Multi-Metaheuristic Competitive Model for Optimization of Fuzzy Controllers , 2019, Algorithms.

[7]  Patricia Melin,et al.  Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification , 2019, Expert Syst. Appl..

[8]  Patricia Melin,et al.  Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm , 2018, Algorithms.

[9]  Patricia Melin,et al.  Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification , 2019, Axioms.

[10]  Oscar Castillo,et al.  An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[11]  Masoud Daneshtalab,et al.  DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems , 2020, Microprocess. Microsystems.

[12]  Rui Guo,et al.  Non-singleton General Type-2 Fuzzy Control for a Two-Wheeled Self-Balancing Robot , 2019, Int. J. Fuzzy Syst..

[13]  Oscar Castillo,et al.  High order α-planes integration: A new approach to computational cost reduction of General Type-2 Fuzzy Systems , 2018, Eng. Appl. Artif. Intell..

[14]  Ahmad M. El-Nagar,et al.  Practical Implementation for the interval type-2 fuzzy PID controller using a low cost microcontroller , 2014 .

[15]  Oscar Castillo,et al.  Design and FPGA Implementation of Real-Time Edge Detectors Based on Interval Type-2 Fuzzy Systems , 2019, J. Multiple Valued Log. Soft Comput..

[16]  Oscar Castillo,et al.  An improved sobel edge detection method based on generalized type-2 fuzzy logic , 2014, Soft Computing.

[17]  Oscar Castillo,et al.  Comparative analysis of noise robustness of type 2 fuzzy logic controllers , 2018, Kybernetika.

[18]  Fevrier Valdez,et al.  Dynamic parameter adaptation in the harmony search algorithm for the optimization of interval type-2 fuzzy logic controllers , 2020, Soft Comput..

[19]  Adriana Mexicano,et al.  Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis , 2015 .

[20]  Hwa Jen Yap,et al.  A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification , 2019, Intell. Decis. Technol..

[21]  Adil Iguider,et al.  Heuristic algorithms for multi-criteria hardware/software partitioning in embedded systems codesign , 2020, Comput. Electr. Eng..

[22]  Miguel A. Melgarejo,et al.  An Embedded Type-2 Fuzzy Processor For The Inverted Pendulum Control Problem , 2011 .

[23]  Hani Hagras,et al.  Toward General Type-2 Fuzzy Logic Systems Based on zSlices , 2010, IEEE Transactions on Fuzzy Systems.

[24]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..

[25]  Jerry M. Mendel,et al.  On KM Algorithms for Solving Type-2 Fuzzy Set Problems , 2013, IEEE Transactions on Fuzzy Systems.

[26]  Patricia Melin,et al.  Comparative study of interval Type-2 and general Type-2 fuzzy systems in medical diagnosis , 2020, Inf. Sci..

[27]  Patricia Melin,et al.  A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems , 2019, Eng. Appl. Artif. Intell..

[28]  Patricia Melin,et al.  A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis , 2018, Expert Syst. Appl..

[29]  Juan R. Castro,et al.  A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems , 2016, Inf. Sci..

[30]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[31]  José Eugenio Naranjo,et al.  Deep Learning Framework for Vehicle and Pedestrian Detection in Rural Roads on an Embedded GPU , 2020, Electronics.

[32]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[33]  Mohammad Hassan Khooban,et al.  An Intelligent Type-2 Fuzzy Stabilization of Multi-DC Nano Power Grids , 2021, IEEE Transactions on Emerging Topics in Computational Intelligence.

[34]  Witold Pedrycz,et al.  A new approach to control of multivariable systems through a hierarchical aggregation of fuzzy controllers , 2019 .

[35]  Weiyi Tan,et al.  Title : The Cardiovascular Burden of Coronavirus Disease 2019 ( COVID-19 ) with a Focus on Congenital Heart Disease Author Names and Affiliations : , 2022 .

[36]  Oscar Castillo,et al.  Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems , 2015, Expert Syst. Appl..