scholarly journals SOFT COMPUTING IN MANAGEMENT: MANAGEMENT OF COMPLEX MULTIVARIATE SYSTEMS BASED ON FUZZY ANALOG CONTROLLERS

2017 ◽  
pp. 42-51 ◽  
Author(s):  
S. O. Kramarov ◽  
L. V. Sakharova ◽  
V. V. Khramov
Keyword(s):  
Complex System ◽  
Soft Computing ◽  
The State ◽  
Fuzzy Classifiers ◽  
Multi Level ◽  
Multivariate Systems

The proposed method of management of complex system based on soft computing, which includes newly developed mathematical tools of fuzzy analog controllers. Model of analog controllers based on the previously proposed apparatus of the multi-level fuzzy classifiers to assess the state of the system based on the integration of complex heterogeneous indicators.

scholarly journals Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics

2021 ◽  
Vol 209 ◽  
pp. 107469
Author(s):  
Lechang Yang ◽  
Pidong Wang ◽  
Qiang Wang ◽  
Sifeng Bi ◽  
Rui Peng ◽  
...  
Keyword(s):  
Complex System ◽  
Reliability Analysis ◽  
Hybrid Structures ◽  
Multi Level

How Macroeconomics Lost the Complexity Vision

2016 ◽  
Author(s):  
David Colander ◽  
Roland Kupers
Keyword(s):  
Complex System ◽  
Standard Model ◽  
The State ◽  
Macro Level ◽  
Formal Models ◽  
Separate Branch ◽  
The Standard Model ◽  
Micro Level ◽  
The Difference ◽  
The Individual

This chapter tells the story of how macroeconomics developed as a separate field in an attempt to add aspects of complexity to the standard model with the aim of improving policy advice, but how those aspects of complexity were quickly lost it again. Instead of dealing with the macro economy as a complex system, macro economists focused on dotting is and crossing ts. The chapter begins by clarifying the difference between macroeconomics and microeconomics. Microeconomics builds a theory up from the individual elements—from the micro level to the macro level. It starts from assumptions of rational individuals and then analyzes how they would coordinate their actions, and what role the state should play in that coordination. Macroeconomics developed as a separate branch of economics when J. M. Keynes’s work was integrated into formal models in the 1930s and 1940s.

Multi-Level Control System for an Intelligent Robot that is Part of a Group

2021 ◽  
Vol 22 (11) ◽  
pp. 610-615
Author(s):  
V. I. Rubtsov ◽  
K. J. Mashkov ◽  
K. V. Konovalov
Keyword(s):  
Control System ◽  
Mobile Robotics ◽  
The State ◽  
Control Mode ◽  
Intelligent Robot ◽  
Level Control ◽  
Level Control System ◽  
Initial Location ◽  
Group Control ◽  
Multi Level

The article is devoted to the application of a group of robotic complexes for military purposes. The current state of control systems of single robotic complexes does not allow solving all the tasks assigned to the robot. The analysis of methods of controlling a group of robots in combat conditions is carried out. The necessity of using a multi-level control system for an intelligent combat robot is justified. A multi-level control system for an intelligent robot is proposed. Such a system assumes the possibility of controlling the robot in one of four modes: remote, supervisory, autonomous and group. Moreover, each robot, depending on the external conditions and its condition, can be in any control mode. The application of the technique is shown by the example of the movement of a group of robots with an interval along the front. The problem of the movement of slave robots behind the leader is considered. When forming the robot control algorithm, the method of finite automata was used. The algorithm controls the movement of the RTK in various operating modes: group control mode and autonomous movement mode. In the group control mode, the task is implemented: movement for the leader. For the state of "Movement in formation", an algorithm for forming the trajectory of the movement of guided robots was implemented. An algorithm for approximating the Bezier curve was used. It allows you to build a trajectory for the slave robot. On the basis of the obtained trajectory, the angular and linear velocity were calculated. In the autonomous control mode, two tasks are solved: moving to a given point and avoiding obstacles. Vector Field Histogram was used as an algorithm for detouring an obstacle, which determines the direction of movement without obstacles. The state of "Movement to a given point" is based on Pure Pursuit as a simple and reliable algorithm for solving such problems. A computer model of the movement of a group of robots was developed. The model is implemented in the MATLAB program using the Simulink and Mobile Robotics Simulation Toolbox libraries. Several different variants of the movement of the RTK group are modeled, which differ from each other in the initial location of the robots and the position of obstacles. The conducted computer simulation showed the efficiency and effectiveness of the proposed method of RTC control.

scholarly journals Using Fisher information to track stability in multivariate systems

2016 ◽  
Vol 3 (11) ◽  
pp. 160582 ◽  
Author(s):  
Nasir Ahmad ◽  
Sybil Derrible ◽  
Tarsha Eason ◽  
Heriberto Cabezas
Keyword(s):  
Complex System ◽  
Fisher Information ◽  
Regime Shift ◽  
Sustainability Science ◽  
Global Mean Temperature ◽  
Simple Step ◽  
Multiple Variables ◽  
Multivariate Systems ◽  
Global Mean

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) can be applied to sustainability science problems and used in data mining applications by analysing patterns in data. FI was developed as a measure of information content in data, and it has been adapted to assess order in complex system behaviour. The main advantage of the approach is the ability to collapse multiple variables into an index that can be used to assess stability and track overall trends in a system, including its regimes and regime shifts. Here, we provide a brief overview of FI theory, followed by a simple step-by-step numerical example on how to compute FI. Furthermore, we introduce an open source Python library that can be freely downloaded from GitHub and we use it in a simple case study to evaluate the evolution of FI for the global-mean temperature from 1880 to 2015. Results indicate significant declines in FI starting in 1978, suggesting a possible regime shift.

Notstandskontrolle

2019 ◽  
Author(s):  
Felix Weber
Keyword(s):  
Human Rights ◽  
Case Law ◽  
The State ◽  
European Convention ◽  
Level System ◽  
State Of Emergency ◽  
Sovereign State ◽  
Judicial Control ◽  
States Of Emergency ◽  
Multi Level

Between 2015 and 2017, France, Turkey and Ukraine, as member states of the European Convention on Human Rights, declared a state of emergency according to Art. 15 ECHR. The events associated with the suspension of Convention rights show the current significance of the legal standardisation of political and social states of emergency. In the end it is all about the question of who ultimately controls the state of emergency: the sovereign state, the state community with a supranational judicial control, or both in terms of a horizontal overlapping of powers in the European multi-level system? Art. 15 ECHR still leaves unanswered questions to which the Strasbourg organs have responded over the years with a differentiated jurisprudence and with the granting of a certain margin of discretion. The book deals with these issues in the light of ECtHR case law and case studies on France, Turkey and Ukraine.

scholarly journals State Feedback of Complex Systems Using Fuzzy Cognitive Maps

2018 ◽  
Vol 6 (3) ◽  
pp. 1-6 ◽  
Author(s):  
Vassiliki Mpelogianni ◽  
Ioannis Arvanitakis ◽  
Peter Groumpos
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
State Space ◽  
State Vector ◽  
State Feedback ◽  
Mathematical Formulation ◽  
Cognitive Maps ◽  
The State ◽  
Fuzzy Cognitive Maps ◽  
System Model

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems.

Sign in / Sign up

Export Citation Format

Share Document