Chaos Solitons & Fractals最新文献

{"title":"A non-singleton type-3 neuro-fuzzy fixed-time synchronizing method","authors":"Hamid Taghavifar ,&nbsp;Ardashir Mohammadzadeh ,&nbsp;Chunwei Zhang","doi":"10.1016/j.chaos.2024.115671","DOIUrl":"10.1016/j.chaos.2024.115671","url":null,"abstract":"<div><div>This paper presents a synchronizing approach to chaotic systems with unknown nonlinear dynamics using a Gaussian non-singleton type-3 (NT3) fuzzy logic system (T3-FLS). The proposed method effectively addresses the challenges of parameter uncertainties and external disturbances by utilizing higher-order fuzzy approximations, thereby enhancing robustness and adaptability. By incorporating a projection operator, the control scenario ensures stability. The design includes a fixed-time adaptive synchronization technique that guarantees convergence in a predetermined time frame, independent of the initial values. The presented theoretical analysis proves the superiority of the designed synchronization approach, while simulations demonstrate significant improvements in synchronization performance and resilience against uncertainties. Specifically, the proposed method achieves root mean square errors of 0.1990 and 0.2754 for the tracking errors, representing improvements over 30% compared to the other benchmarking methods. These outcomes demonstrate the robustness of our proposed controller in handling chaotic systems under various operating conditions.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115671"},"PeriodicalIF":5.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive practical prescribed-time control for uncertain nonlinear systems with time-varying parameters","authors":"Tianping Zhang ,&nbsp;Wei Zhang","doi":"10.1016/j.chaos.2024.115677","DOIUrl":"10.1016/j.chaos.2024.115677","url":null,"abstract":"<div><div>In this paper, adaptive practical prescribed-time (PPT) control is proposed for a class of uncertain nonlinear systems with time-varying parameters and unmodeled dynamics. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT control is successfully resolved. The dynamical uncertainties resulting from unmodeled dynamics are estimated by employing an auxiliary available signal, and the unknown continuous terms are handled by the aid of radial basis function neural networks (RBFNNs). A novel adaptive control method is developed by introducing the compensating signals and dynamic surface control as well as practical prescribed-time control. All the signals involved are proved to be semi-globally uniformly ultimately bounded, and the tracking error could enter the pre-specified convergence region within a pre-specified time. The robotic manipulator system is used to demonstrate the effectiveness of the proposed control approach.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115677"},"PeriodicalIF":5.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fostering cooperative evolution through probabilistic punishment and environmental feedback in public goods game","authors":"Jiaqi Liu,&nbsp;Qianwei Zhang,&nbsp;Rui Tang","doi":"10.1016/j.chaos.2024.115693","DOIUrl":"10.1016/j.chaos.2024.115693","url":null,"abstract":"<div><div>Punishing selfish individuals is regarded as an effective method to maintain social cooperation. In reality, the corresponding punishment probability should vary with different game environments. However, most current research treats this probability as a constant or exogenously given. In this paper, based on the public goods game, we design an environmental feedback mechanism and establish a feedback evolutionary game model. The model assumes that the probability of punishing defectors will change with the proportion of cooperators, ultimately influencing individual decision-making. Through theoretical analysis and numerical simulations, we obtain three stable states of the system under different parameter conditions: a state of complete defection with low punishment probability, a state of complete cooperation with high punishment probability, and a bistable state. Our research results indicate that the environmental feedback mechanism plays a crucial role in promoting long-term social stability and sustainable development.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115693"},"PeriodicalIF":5.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of key term separated identification model for fractional input nonlinear output error systems: Auxiliary model based Runge Kutta optimization algorithm","authors":"Muhammad Aown Ali ,&nbsp;Naveed Ishtiaq Chaudhary ,&nbsp;Taimoor Ali Khan ,&nbsp;Wei-Lung Mao ,&nbsp;Chien-Chou Lin ,&nbsp;Muhammad Asif Zahoor Raja","doi":"10.1016/j.chaos.2024.115696","DOIUrl":"10.1016/j.chaos.2024.115696","url":null,"abstract":"<div><div>Fractional calculus generalizes the conventional calculus to real order and become a popular tool for efficient modeling of complex engineering problems by providing better insight to the system through involving historical information. In this study, fractional calculus concepts are incorporated into input nonlinear output error (INOE) system and is generalized to fractional INOE (FINOE) model through Grunwald-Letnikov differential operator. The key-term-separation based identification model is presented to estimate the parameters of FINOE system that avoids the burden of identifying extra parameters due to cross product terms. The parameter estimation of systems modeled by Hammerstein output error structure is a challenging task, especially with incorporation of fractional concepts. An auxiliary model based Runge Kutta (RUN) optimization methodology is proposed for viable estimation of FINOE parameters by using the estimate for unmeasurable terms of information vector. The mean-square-error based fitness function is developed that minimizes the difference between the actual and estimated responses of the FINOE system. The efficacy of the proposed scheme is investigated in terms of convergence speed, computational cost, resilience, stability and correctness in approximation of accurate weights of the FINOE system for multiple noise variations. The superiority of the RUN for FINOE is endorsed via comparative analysis with 8 states of the arts in noisy environments.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115696"},"PeriodicalIF":5.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method","authors":"Junwen Xiao,&nbsp;Yongchao Liu","doi":"10.1016/j.chaos.2024.115649","DOIUrl":"10.1016/j.chaos.2024.115649","url":null,"abstract":"<div><div>This paper presents a self-triggered consensus resilient control method for nonlinear multi-agent systems (MASs) under sensor deception attacks. A single parameter learning method is integrated into backstepping technique to simplify design procedure. The neural networks are utilized to compensate for unknown dynamics of the MASs. Moreover, a self-triggered mechanism is presented for MASs to refrain from continuously monitoring triggering conditions and conserve communication resources. The designed controller can resist sensor deception attacks and guarantee that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115649"},"PeriodicalIF":5.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integral resonant negative derivative feedback suppression control strategy for nonlinear dynamic vibration behavior model","authors":"H.S. Bauomy ,&nbsp;A.T. EL-Sayed ,&nbsp;F.T. El-Bahrawy","doi":"10.1016/j.chaos.2024.115686","DOIUrl":"10.1016/j.chaos.2024.115686","url":null,"abstract":"<div><div>One of the major problems in robotics research has been developing an actuator system for extremely dynamic-legged robots. High torque density and the capacity to control dynamic physical interactions are two design requirements for high-speed locomotion that are challenging for conventional actuators used in manufacturing applications to meet. To address this system and apply the desired control to reach the best stability position, the robot's foot was simulated with the Van der Pol equations, applied the required control, and studied that application. This work describes the actions of a new novel control mechanism known as the Integral Resonant Negative Derivative Feedback (IRNDF) controller, which reduces the vibration response of a double Van der Pol oscillator subjected to external excitations. This unique controller combines integral resonant control (IRC) and negative derivative feedback (NDF) controllers to provide a new controller effect for double Van der Pol oscillators. The multiple scale perturbation technique (MSPT) has been applied to solve the controlled system analytically. The MATLAB and MAPLE programs have been used to complete and clarify all of the numerical talks. The frequency response curves have been used to study the impact that altering the parameter values had on the amplitude. The controlled system vibration amplitude is governed by frequency-response equations (FREs), which have been constructed. In the vibration system, the IRC, NDF, and IRNDF controllers were compared to see which one was the best. Numerical results show that the unique IRNDF controller is the best at reducing oscillations and decreasing amplitude values. The effects of the effective parameters on the controlled system have been identified. The frequency-response equation that was derived has been used to plot the various response curves for the framework that show the stable and unstable zones when the controller is off and on. Lastly, excellent agreement between the derived numerical findings and the analytical ones was observed. Lastly, utilizing time histories and response curves to compare analytical and numerical solutions was fascinating and significant.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115686"},"PeriodicalIF":5.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data driven cost-sensitive boosted tree for interpretable banking systemic risk prediction","authors":"Meng Xia ,&nbsp;Zhijie Wang ,&nbsp;Wanan Liu","doi":"10.1016/j.chaos.2024.115664","DOIUrl":"10.1016/j.chaos.2024.115664","url":null,"abstract":"<div><div>Systemic risk (SR) in the banking sector poses a significant threat to both the financial system and the real economy. Its inherent characteristics of nonlinearity, non-equilibrium, and interconnectedness make it challenging to analyze using conventional statistical methods. In this paper, a cost-sensitive gradient boosting tree algorithm, FLXGBoost, is proposed for predicting SR. FLXGBoost considers the boosted tree, XGBoost as the base framework, boosting trees as the fundamental framework, guaranteeing the robustness of SR prediction. Additionally, to tackle the challenge of extreme data imbalance prevalent in SR prediction tasks, a cost-aware loss function, focal loss, is embedded into the boosted tree to enable FLXGBoost a risk-aware fashion. Moreover, a tree-derived interpretable algorithm SHAP is incorporated into this cost-sensitive solution, making FLXGBoost an accurate and interpretable risk-aware model. Experimental results on a financial risk prediction dataset pertaining to banking SR evince the capacity of FLXGBoost to significantly reduce the misclassification rate of risk banks, thereby mitigating substantial losses attributed to erroneous predictions of risky scenarios. Moreover, compared with classical imbalanced machine learning-based SR prediction approaches, the diverse evaluation metrics of FLXGBoost show that it is a competitive solution for accurate SR prediction. Besides, the explanatory analysis further demonstrates that FLXGBoost is a promising solution to address the issue of biased predictions in imbalanced banking SR in the interpretation perspective.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115664"},"PeriodicalIF":5.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic characteristics and conversion process of solitons in a Mamyshev oscillator","authors":"Yuhe Dong,&nbsp;Xingliang Li,&nbsp;Mengmeng Han,&nbsp;Shumin Zhang,&nbsp;Chaoran Wang","doi":"10.1016/j.chaos.2024.115667","DOIUrl":"10.1016/j.chaos.2024.115667","url":null,"abstract":"<div><div>The Mamyshev oscillator (MO) can produce not only a single pulse (SP) but also multi-pulses (MPs). However, most of the research focuses on increasing the output pulse energy and reducing the pulse width, and only some studies reveal the characteristics of MPs output. More research is needed on achieving the conversion from MPs to SP by adjusting the oscillator parameters and determining what intermediate states will be experienced in the conversion process. Here, we study the dynamic characteristics of soliton and the conversion process between different output states of ultrafast MO. By adjusting the gain saturation energy and filter interval, we obtain the relationship between the number of MPs outputs and the oscillator parameters and observe the intermediate process from MPs pulsation to SP. The research results reveal the dynamic characteristics of non-equilibrium optical solitons, assisting in optimizing MO design.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115667"},"PeriodicalIF":5.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on a (3+1)-dimensional B-type Kadomtsev–Petviashvili equation in nonlinear physics: Multiple soliton solutions, lump solutions, and breather wave solutions","authors":"Abdul-Majid Wazwaz","doi":"10.1016/j.chaos.2024.115668","DOIUrl":"10.1016/j.chaos.2024.115668","url":null,"abstract":"<div><div>In this work, we study an extended (3+1)-dimensional B-type Kadomtsev–Petviashvili (BKP) equations that appear in many nonlinear physics applications. We show that this extended equation retains its complete integrability via Painlevé analysis. We explore multiple soliton solutions by using the Hirota bilinear method. Moreover, we derive lump solutions where two numerical examples are tested. Breather wave solutions were also explored by using a variety of distinct schemes. We also determine other traveling wave solutions, rational solutions, periodic solutions, exponential solutions, ratio of trigonometric or hyperbolic functions, and others.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115668"},"PeriodicalIF":5.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations","authors":"Ye Liu ,&nbsp;Jie-Ying Li ,&nbsp;Li-Sheng Zhang ,&nbsp;Lei-Lei Guo ,&nbsp;Zhi-Yong Zhang","doi":"10.1016/j.chaos.2024.115658","DOIUrl":"10.1016/j.chaos.2024.115658","url":null,"abstract":"<div><div>Domain decomposition provides an effective way to tackle the dilemma of physics-informed neural networks (PINN) which struggle to accurately and efficiently solve partial differential equations (PDEs) in the whole domain, but the lack of efficient tools for dealing with the interfaces between two adjacent sub-domains heavily hinders the training effects, even leads to the discontinuity of the learned solutions. In this paper, we propose a symmetry group based domain decomposition strategy to enhance the PINN for solving the forward and inverse problems of the PDEs possessing a Lie symmetry group. Specifically, for the forward problem, we first deploy the symmetry group to generate the dividing-lines having known solution information which can be adjusted flexibly and are used to divide the whole training domain into a finite number of non-overlapping sub-domains, then utilize the PINN and the symmetry-enhanced PINN methods to learn the solutions in each sub-domain and finally stitch them to the overall solution of PDEs. For the inverse problem, we first utilize the symmetry group acting on the data of the initial and boundary conditions to generate labeled data in the interior domain of PDEs and then find the undetermined parameters as well as the solution by only training the neural networks in a sub-domain. Consequently, the proposed method can predict high-accuracy solutions of PDEs which are failed by the vanilla PINN in the whole domain and the extended PINN in the same sub-domain. Numerical results of the Korteweg–de Vries equation with a translation symmetry and the nonlinear viscous fluid equation with a scaling symmetry show that the accuracies of the learned solutions are improved largely.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"189 ","pages":"Article 115658"},"PeriodicalIF":5.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}