Article
Mathematics, Applied
Panqing Gao, Hai Zhang, Renyu Ye, Ivanka Stamova, Jinde Cao
Summary: This paper focuses on the quasi-uniform synchronization (Q-US) issues of discrete fractional fuzzy neural networks (DFFNNs) with time delays. It includes the analysis of neural network systems with fuzzy time delays in discrete cases and utilizes discrete fractional calculus to enrich the analysis of the discrete system. Furthermore, a designed delay feedback controller is used to handle complex time delays and promote the system's response, and the obtained results are efficiently verified by simulation examples.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Mathematics, Interdisciplinary Applications
F. A. W. A. Z. W. Alsaade, M. O. H. A. M. M. E. D. S. Al-Zahrani
Summary: This study proposes a novel control technique for time-delayed fractional-order systems. It utilizes a finite-time disturbance observer and an active controller to estimate uncertainties within finite time. The stability of the method and the finite-time convergence of the estimator are guaranteed.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2023)
Article
Computer Science, Theory & Methods
Feifei Du, Jun-Guo Lu
Summary: This paper investigates the adaptive finite-time synchronization problems for a class of fractional-order delayed fuzzy cellular neural networks (FODFCNNs). First, new perspective is provided to explore finite-time synchronization of fractional-order systems by establishing novel fractional-order differential inequalities with the aid of the properties of the Mittag-Leffler function and the Laplace transform with the parameter. Second, sufficient conditions are given based on these inequalities and the designed adaptive controller to guarantee the adaptive finite-time synchronization of FODFCNNs. Finally, the effectiveness of the results is demonstrated by a numerical example.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Feng Zhao, Jigui Jian, Baoxian Wang
Summary: This paper addresses the finite-time synchronization problem of fractional-order memristive fuzzy neural networks (FOMFNNs) with leakage and transmission delays. Two types of discontinuous control schemes with leakage and transmission delays are designed: state feedback control and fractional-order adaptive control. By utilizing differential inclusion theory and fractional-order differential inequalities, new delay-independent algebraic conditions are derived to ensure the finite-time synchronization of drive-response FOMFNNs with leakage and transmission delays. Additionally, an estimation of the upper bound of the settling time for the finite-time synchronization is provided. Finally, two numerical examples with simulations are presented to illustrate the feasibility and effectiveness of the proposed theoretical results.
FUZZY SETS AND SYSTEMS
(2023)
Article
Mathematics, Applied
Juanping Yang, Hong-Li Li, Long Zhang, Cheng Hu, Haijun Jiang
Summary: This paper investigates the problems of quasi-projective synchronization (QPS) and finite-time synchronization (FTS) for a type of delayed fractional-order BAM neural networks (DFOBAMNNs). By designing fresh quantized controllers, the goals of synchronization are achieved and the settling time and error level are accurately evaluated.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Hong-Li Li, Cheng Hu, Long Zhang, Haijun Jiang, Jinde Cao
Summary: This paper addresses the issues of complete synchronization (CS) and finite-time synchronization (F-TS) for a class of fractional-order fuzzy neural networks based on nonlinear feedback control. It establishes a fractional-order finite-time convergence principle and designs two novel nonlinear controllers. It also derives easily validated criteria to guarantee CS and F-TS and effectively estimates the settling time of F-TS. Numerical results are presented to demonstrate the validity of the derived theoretical results.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Hong-Li Li, Cheng Hu, Long Zhang, Haijun Jiang, Jinde Cao
Summary: This paper addresses the issues of complete synchronization (CS) and finite-time synchronization (F-TS) for a class of fractional-order fuzzy neural networks based on nonlinear feedback control. It establishes a fractional-order finite-time convergence principle and designs two novel nonlinear controllers. Criteria to guarantee CS and F-TS are derived with the help of analysis techniques and the newly established convergence principle, and the settling time of F-TS is effectively estimated.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Feifei Du, Jun-Guo Lu
Summary: The finite-time synchronization of fractional-order delayed fuzzy cellular neural networks with parameter uncertainties is studied. Two new nonlinear finite-time inequalities are derived based on the C-p inequality and the rule for fractional-order derivative of composite function, providing new tools for researching the finite-time stability and synchronization of fractional-order systems. On the basis of these new inequalities, a feedback controller is designed and two novel criteria for finite-time synchronization of fractional-order fuzzy cellular neural networks with parameter uncertainties are obtained. Finally, two examples are presented to verify the effectiveness of the derived results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Mathematics, Interdisciplinary Applications
Bo Wang, Hadi Jahanshahi, Stelios Bekiros, Yu-Ming Chu, J. F. Gomez-Aguilar, Fawaz E. Alsaadi, Madini O. Alassafi
Summary: This paper introduces a fractional-order financial risk system and investigates the effects of fractional derivatives on its dynamical behavior. Two finite-time fault-tolerant controllers are proposed to push the system states to desired values in a short amount of time. The controllers are robust against uncertainties, faults, and failures in actuators.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2021)
Article
Mathematics
Shuang Wang, Hai Zhang, Weiwei Zhang, Hongmei Zhang
Summary: This paper investigates the finite-time projective synchronization of Caputo type fractional-order complex-valued neural networks with time delay. By constructing suitable Lyapunov function and designing two new types controllers, sufficient criteria are derived to ensure the synchronization between drive and response systems, with an effectively estimated synchronization time. Numerical examples are presented to verify the effectiveness and feasibility of the proposed results.
Article
Automation & Control Systems
Haipeng Su, Runzi Luo, Jiaojiao Fu, Meichun Huang
Summary: This article investigates the fixed time synchronization of chaotic neural systems using adaptive control method. A new fixed time stability theorem and an adaptive control scheme are proposed based on Lyapunov stability theory, ensuring global convergence and fast convergence rate. The effectiveness of the proposed scheme is demonstrated through numerical simulations of a typical two-order chaotic neural system.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Mathematics, Applied
Jie Li, Hong-Li Li, Juanping Yang, Jikai Yang, Long Zhang
Summary: This paper explores complete synchronization of a class of fractional-order delayed complex-valued fuzzy neural networks (FDCFNNs) by employing hybrid nonlinear controller. The authors design a new hybrid adaptive nonlinear controller and derive the sufficient synchronization conditions of FDCFNNs through fractional calculus theory and inequality scaling techniques. The validity of theoretical results is verified through a numerical example.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Xiaolin Yuan, Guojian Ren, Yongguang Yu, Wenjiao Sun
Summary: This paper investigates the mean-square pinning control problem of fractional stochastic discrete-time complex networks. It establishes a new model with stochastic noise and develops pinning controllers and sufficient conditions for the complex networks. By utilizing Lyapunov energy function theory and matrix analysis theory, it proves that synchronization of the networks can be achieved in a mean-square sense via pinning control. Furthermore, these results are extended to solve the synchronization problem of general fractional discrete-time complex networks without noise.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Juanping Yang, Yuhong Sheng, Hong-Li Li, Cheng Hu
Summary: This paper investigates stability and synchronization issues for delayed uncertain fractional-order gene regulatory networks (DUFOGRNs). The existence and uniqueness of the equilibrium point of the DUFOGRNs are explored via constructing a contraction map. A stability criterion is studied using the fractional-order Lyapunov-Razumikhin approach and the Mittag-Leffler function. A novel fractional-order lemma is established to discuss finite-time stability and synchronization of fractional-order systems (FOSs). Adaptive control strategies are designed based on the established lemma, and sufficient synchronization criteria are derived for guaranteeing the complete synchronization (CS) and finite-time synchronization (F-TS) of DUFOGRNs. Three numerical examples are provided to verify the proposed method's effectiveness.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Hong -Li Li, Jinde Cao, Cheng Hu, Long Zhang, Haijun Jiang
Summary: This paper addresses the complete synchronization problem for discrete-time fractional-order fuzzy neural networks with time-varying delays. A novel discrete-time adaptive controller with time-varying delay is proposed, and the effectiveness of the theoretical results is demonstrated through numerical examples.
Article
Computer Science, Artificial Intelligence
Debabrata Dansana, Raghvendra Kumar, Aishik Bhattacharjee, D. Jude Hemanth, Deepak Gupta, Ashish Khanna, Oscar Castillo
Summary: The COVID-19 pandemic, which began in December 2019 in China, has rapidly spread worldwide and infected over ten million people. This study explores the binary classification of pneumonia using convolution neural networks on X-ray and CT scan images. The findings show that fine-tuned versions of VGG-19 and Inception_V2 models demonstrate high accuracy rates.
Article
Computer Science, Artificial Intelligence
Patricia Melin, Julio Cesar Monica, Daniela Sanchez, Oscar Castillo
Summary: In this paper, we propose a hybrid ensemble modular neural network approach for predicting the COVID-19 time series worldwide. This approach combines nonlinear autoregressive neural networks to form efficient predictors for each country. The analysis using publicly available datasets reveals interesting conclusions that can aid countries in devising effective strategies for combating the pandemic. The proposed approach may also offer guidance for similar countries.
Article
Computer Science, Artificial Intelligence
Patricia Melin, Daniela Sanchez, Julio Cesar Monica, Oscar Castillo
Summary: This paper presents a method for predicting global COVID-19 pandemic using a firefly algorithm to design an ensemble neural network architecture. It takes into account the differences between countries and utilizes type-2 fuzzy logic and weighted average integration to improve prediction accuracy.
Editorial Material
Multidisciplinary Sciences
Shaobo He
Article
Automation & Control Systems
Patricia Ochoa, Oscar Castillo, Patricia Melin, Juan R. Castro
Summary: This article presents the usage of interval type-3 fuzzy sets in the differential evolution algorithm for the first time. A study is conducted to explore the influence of the LowerScale (lambda) parameter on the convergence of differential evolution. The results from experiments on benchmark functions and motor control optimization demonstrate that the combination of interval type-3 fuzzy sets and differential evolution outperforms type-1 and interval type-2 variants.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Mario Garcia-Valdez, Alejandra Mancilla, Oscar Castillo, Juan Julian Merelo-Guervos
Summary: In this work, a distributed and asynchronous bio-inspired algorithm is proposed to speed up the design process of a controller by executing simulations in parallel. The algorithm uses a multi-population multi-algorithmic approach with isolated populations interacting asynchronously using a distributed message queue. The results demonstrate the speedup benefit of the proposed algorithm and the advantages of mixing populations with distinct metaheuristics.
Editorial Material
Computer Science, Artificial Intelligence
Oscar Castillo, Oscar Montiel, Fevrier Valdez
Retraction
Computer Science, Artificial Intelligence
Patricia Melin, Julio Cesar Monica, Daniela Sanchez, Oscar Castillo
Article
Multidisciplinary Sciences
Lucio Amezquita, Oscar Castillo, Jose Soria, Prometeo Cortes-Antonio
Summary: This work introduces multiple variations of the Multi-verse Optimizer Algorithm (MVO) by incorporating chaotic maps to generate new solutions. The variations, called Fuzzy-Chaotic Multi-verse Optimizer (FCMVO), replace random values with chaotic maps from literature for certain parameters in the original algorithm. Fuzzy Logic is also used for dynamic parameter adaptation in these new variants, along with the analysis of the improvement over the Fuzzy MVO. The objective is to compare the performance of MVO algorithm with the best-performing chaotic maps and Fuzzy Logic in benchmark mathematical functions before exploring other case studies.
Article
Mathematics
Njud S. Alharbi, Hadi Jahanshahi, Qijia Yao, Stelios Bekiros, Irene Moroz
Summary: This study introduces an ensemble model combining LSTM and CNN models for the classification of ECG signals. The model utilizes LSTM's sequential data learning capability and CNN's intricate pattern recognition strength, along with advanced signal processing methods. Experimental results demonstrate that the proposed model outperforms other deep learning models, showcasing its potential in cardiovascular disease diagnosis.
Article
Mathematics
Naif D. Alotaibi, Hadi Jahanshahi, Qijia Yao, Jun Mou, Stelios Bekiros
Summary: This study introduces a novel ensemble neural network approach for accurately classifying upper limb electromyography (EMG) signals. The proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, achieving high accuracy through preprocessing and feature extraction of the signals.
Article
Computer Science, Artificial Intelligence
Oscar Castillo, Juan R. Castro, Patricia Melin
Summary: In this article, a design methodology for Mamdani interval and general type-2 fuzzy systems with center-of-sets type reduction is presented. The methodology utilizes descriptive statistics, fuzzy c means clustering, and granular computing theory to define the justifiable footprint of uncertainty (JFOU) of the fuzzy granules. The design methodology is presented in three general steps, focusing on building a diagram of the justifiable information granule, characterizing and parameterizing the asymmetric type-2 membership functions, and obtaining all the justifiable information fuzzy granules for the fuzzy model. Experiments were conducted to evaluate the reliability of the proposed methodology.
JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
(2023)
Article
Engineering, Multidisciplinary
Deepshikha Sarma, Amrit Das, Oscar Castillo, Uttam Kumar Bera
Summary: This research proposes a mathematical model for the transportation of relief materials in disaster response, aiming to minimize total cost and maximize coverage of affected people. The model considers both certain and uncertain environments, and uses interval-coefficient decomposition, expected value operator, and rough chance constraint programming to handle uncertainty. A real-life problem of Assam flood is used to evaluate the efficiency of the model, and a comparative study is conducted. The model provides optimal results in crisp form in certain environment, and rough interval, crisp, and interval forms in uncertain environment for better decision-making.
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2023)
Article
Mathematics, Interdisciplinary Applications
Mengjiao Wang, Jiwei Peng, Shaobo He, Xinan Zhang, Herbert Ho-Ching Iu, Antonio Lopes
Summary: This study investigates the firing dynamics and phase synchronization behavior of a heterogeneous coupled network and reveals its complex dynamic behavior, as well as an abnormal synchronization behavior that differs from existing findings. The findings contribute to the understanding of brain activity mechanisms and the development of bionic systems.
FRACTAL AND FRACTIONAL
(2023)
Article
Automation & Control Systems
Longxiang Fu, Xianming Wu, Shaobo He, Huihai Wang, Kehui Sun
Summary: This article proposes an improved memristive Henon map by using the state variable difference as the input of a discrete memristor. The system exhibits rich dynamical behaviors as shown by bifurcation diagrams, Lyapunov exponent spectrums, and sample entropy complexity analysis results. In addition, analog circuits of the discrete memristor and memristive chaotic map are designed and validated through simulations and experiments, demonstrating the physical realizability of the discrete memristor and laying the foundation for its applications.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Bo Li, Tian Huang
Summary: This paper proposes an approximate optimal strategy based on a piecewise parameterization and optimization (PPAO) method for solving optimization problems in stochastic control systems. The method obtains a piecewise parameter control by solving first-order differential equations, which simplifies the control form and ensures a small model error.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Guram Mikaberidze, Sayantan Nag Chowdhury, Alan Hastings, Raissa M. D'Souza
Summary: This study explores the collective behavior of interacting entities, focusing on the co-evolution of diverse mobile agents in a heterogeneous environment network. Increasing agent density, introducing heterogeneity, and designing the network structure intelligently can promote agent cohesion.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Gengxiang Wang, Yang Liu, Caishan Liu
Summary: This investigation studies the impact behavior of a contact body in a fluidic environment. A dissipated coefficient is introduced to describe the energy dissipation caused by hydrodynamic forces. A new fluid damping factor is derived to depict the coupling between liquid and solid, as well as the coupling between solid and solid. A new coefficient of restitution (CoR) is proposed to determine the actual physical impact. A new contact force model with a fluid damping factor tailored for immersed collision events is proposed.
CHAOS SOLITONS & FRACTALS
(2024)
