Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.

An exploratory study of the growth of the Accountable Care Organization and its impact on physician groups’ profit: a complex adaptive system approach

The emergence of Accountable Care Organizations(ACOs)in the landscape of the U.S.healthcare system marks a paradigm shift in healthcare operations.The potential impact of ACOs has been a topic of intense debate.Traditional analytical approaches do not lend themselves to examining the complex phenomenon of the emergence and growth of ACOs in the healthcare network.We adopt a complex adaptive system lens to examine the growth of ACOs among physician groups and explore factors that influence this growth.We also discuss the impact of ACOs on the profit of physician groups.An agent-based model was built to simulate physician groups'ACO entrance and exit based on a set of simple rules and their complex interactions with other agents.Based on the simulation results,we derive patterns of ACO expansion and contraction,following four stages of wait-and-see,rollercoaster,fast growth,and stabilizing.Findings suggest that the growth of ACOs is sensitive to the initial state of ACO membership.When the initial size of ACO membership increases,it helps to eliminate the rollercoaster stage.In addition,the growth of the ACO varies depending on the cost–quality tradeoff.When both cost and quality objectives can be met simultaneously,the growth of ACO membership follows wait-and-see and fast growth stages followed by a different stage that we term sticky state.The impact of ACOs on physician groups’cumulative profit varies by the service quality level of the physician group.Physician groups affiliated with insurance companies charging the lowest or the highest level of health insurance premiums are worse off with the ACO option.However,the ACO benefits physician groups affiliated with an insurance company charging a moderate level of premiums.

An Intelligent Agency Framework to Realize Adaptive System Management

In order to realize the required scalable and adaptive system management, an interactive intelligent agency framework, iSMAcy （intelligent System Management Agency） , is proposed as an integrated solution to realize distributed autonomoas system management. Firstly, it is a multiagent platform that supports inter-agent communication and cooperation. Secondly, the functional agents are based on intentional agent architecture that achieves balance between goal-directed behavior and situated reactive action. An example of applying the iSMAcy system to a network management environment has been described to illustrate and validate the scalable and adaptive management capability of the intelligent agency framework.

Understanding leukemic hematopoiesis as a complex adaptive system

Normal and abnormal hematopoiesis is working as a complex adaptive system. From this perspective, the development and the behavior of hematopoietic cell lineages appear as a balance between normal and abnormal hematopoiesis in the setting of a functioning or malfunctioning microenvironment under the control of the immune system and the influence of hereditary and environmental events.

A Comment on Language Is a Complex Adaptive System: Position Paper

Language not only functions as a communication tool,it has fundamental functions.People’s social interaction and their past experience can affect people’s choice of language,as language is a complex,adaptive system.The paper tries to comment on"A comment on Language Is a Complex Adaptive System:Position Paper"from several aspects to conclude that Language Is a Complex Adaptive System:Position Paper is a comprehensive,creative and influential academic paper which is characteristic of high originality,well-compact organization,detailed literature review.

Stability Analysis of q-axis Rotor Flux Based Model Reference Adaptive System Updating Rotor Time Constant in Induction Motor Drives

q-axis rotor flux can be chosen to form a model reference adaptive system(MRAS)updating rotor time constant online in induction motor drives.This paper presents a stability analysis of such a system with Popov’s hyperstability concept and small-signal linearization technique.At first,the stability of q-axis rotor flux based MRAS is proven with Popov’s Hyperstability theory.Then,to find out the guidelines for optimally designing the coefficients in the PI controller,acting as the adaption mechanism in the MRAS,small-signal model of the estimation system is developed.The obtained linearization model not only allows the stability to be verified further through Routh criterion,but also reveals the distribution of the characteristic roots,which leads to the clue to optimal PI gains.The theoretical analysis and the resultant design guidelines of the adaptation PI gains are verified through simulation and experiments.

Linguistic Reflection on the Online Catchword of Appreciation —From the Perspective of Usage-based Language Model and Complex Adaptive System Theory

Language is a special social phenomenon and is always on the changing process with the development of society. During the evolving process of language, new language varieties will continuously emerge due to the changes of some social and cultural factors. Cyber language is universally accepted as one type of the social language varieties. Basically, cyber language can be treated as a complex adaptive system which is influenced by the interaction between users’ cognition, social culture and the surrounding environments. Thus it is safe to say that cyber language is always undergoing a dynamic evolving process. With the usage-based language model as the theoretical foundation, this paper proposes a Complex Adaptive System (CAS) approach to analyze the expression of Appreciation to explore the complex, dynamic and nonlinear development of cyber language from the angle of meaning construction, grammaticalization and functional adaption respectively. It is found that the expression of Appreciation is experiencing adaptively a semantic connotations development and a process of grammatical functions expansion as well. This paper suggests that the emergence and development of cyber language is a novel and trendy social language phenomenon. Network language can achieve its process and evolution under the huge impact of social changes and social promotions. When faced with the changing surroundings, cyber language itself enjoys a timely adaption and responsive development to keep up with the new environments, which reflects the basic principle of language development, namely, language changes with the development of society.

Multi-point Layout Planning for Multi-energy Power System Based on Complex Adaptive System Theory

Aiming at the problem of multi-point layout planning of a multi-energy power system,the output characteristics of a multi-energy power system composed of wind power generation,photovoltaic power generation,hydropower generation,traditional thermal power generation and solar thermal power generation are comprehensively analyzed.Combining power optimization planning with complex adaptive system theory,a multi-point layout planning model of multi-energy sources based on complex adaptive system theory is proposed.The model takes the minimum construction step size of each new energy source as the agent.Through the interaction between the agents and the accumulation of experience,the behavior rules are constantly changed,the installed positions of various types of power sources are adjusted,and the optimal layout scheme of various power capacities of each node is obtained.Moreover,an agent modeling method based on a simple rule emerging complex phenomena is proposed,which reveals the core idea of complex adaptive system theory—adaptability makes complexity.Taking an actual power grid in a certain region of China as an example,it is verified that the proposed method has a significant effect on improving the consumption of new energy,and has certain guiding significance for the actual engineering construction.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed topology.The MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,respectively.Firstly,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected formation.Secondly,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,respectively.Subsequently,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the group.Next,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior.Finally,a numerical simulation is intro-duced to confirm the validity of the proposed results.

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniques in terms of accuracy,precision,and recall.This superiority stems from ACIDS-PELT’s ability to overcome limitations associated with approximation and imprecision in change point detection while offering a more accurate and precise approach to detecting security threats in dynamic cloud environments.

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Robust adaptive leaderless consensus of unknown non-minimum phase linear multi-agent systems subject to disturbances and/or unmodeled dynamics

This article investigates the problem of robust adaptive leaderless consensus for heterogeneous uncertain nonminimumphase linear multi-agent systems over directed communication graphs. Each agent is assumed tobe of unknown nominal dynamics and also subject to external disturbances and/or unmodeled dynamics. Anovel distributed robust adaptive control strategy is proposed. It is shown that the robust adaptive leaderlessconsensus problem is solved with the proposed control strategy under some sufficient conditions. Two examplesare provided to demonstrate the efficacy of the proposed control strategy.

Solar adaptive optics systems for the New Vacuum Solar Telescope at the Fuxian Lake Solar Observatory

Adaptive optics(AO)is essential for high-quality ground-based observations with large telescopes because it counters the impact of wavefront aberrations caused by atmospheric turbulence.The new vacuum solar telescope(NVST)is one of the most important high-resolution solar observation instruments in the world.Three sets of solar adaptive optics systems have been developed and installed on this telescope:conventional adaptive optics,ground layer adaptive optics,and multi-conjugate adaptive optics.These have been in operation from 2018 to 2023.This paper details the development and application of solar adaptive optics on the NVST and discusses the newest instrumentation.

A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.