FUZZY PREDICTION STRATEGIES FOR GENE-ENVIRONMENT NETWORKS - FUZZY REGRESSION ANALYSIS FOR TWO-MODAL REGULATORY SYSTEMS

2016-04-01
Kropat, Erik
Ozmen, Ayse
Weber, Gerhard Wilhelm
Meyer-Nieberg, Silja
DEFTERLİ, ÖZLEM
Target-environment networks provide a conceptual framework for the analysis and prediction of complex regulatory systems such as genetic networks, eco-finance networks or sensor-target assignments. These evolving networks consist of two major groups of entities that are interacting by unknown relationships. The structure and dynamics of the hidden regulatory system have to be revealed from uncertain measurement data. In this paper, the concept of fuzzy target-environment networks is introduced and various fuzzy possibilistic regression models are presented. The relation between the targets and/or environmental entities of the regulatory network is given in terms of a fuzzy model. The vagueness of the regulatory system results from the (unknown) fuzzy coefficients. For an identification of the fuzzy coefficients' shape, methods from fuzzy regression are adapted and made applicable to the bi-level situation of target-environment networks and uncertain data. Various shapes of fuzzy coefficients are considered and the control of outliers is discussed. A first numerical example is presented for purposes of illustration. The paper ends with a conclusion and an outlook to future studies.
RAIRO-OPERATIONS RESEARCH

Suggestions

Intuitionistic, 2-way adaptive fuzzy control
Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (1999-05-15)
Our objective in this paper is to develop a 2-way adaptive fuzzy control system that makes use of the intuitionistic fuzzy sets for modeling expert knowledge bearing uncertainty. Adaptive fuzzy control systems are fuzzy logic systems whose rule parameters are automatically adjusted through training. The training of such system was applied until now, to supports of rule propositions with single distribution such that they can be termed 1-way adaptive. In our system, all supports to propositions have interval...
Optimizing the Performance of Rule-Based Fuzzy Routing Algorithms in Wireless Sensor Networks
Sert, Seyyit Alper; Yazıcı, Adnan (2019-06-01)
Effective data routing is one of the crucial themes for energy-efficient communication in wireless sensor networks (WSN). In the WSN research domain, fuzzy approaches are in most cases superior to well-defined methodologies, especially where boundaries between clusters are unclear. For this reason, a significant number of studies have recently proposed fuzzy-based solutions for the problems encountered in WSNs. Rule-based fuzzy systems are part of these widespread fuzzy-based solutions that often involve so...
Genetically tuned fuzzy scheduling for flexible manufacturing systems.
Erkmen, Aydan Müşerref; Anlagan, O; Unver, O (1997-04-25)
This paper focuses on the development and implementation of a Genetically Tuned Fuzzy Scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on time criticality in deadline and machine need, taking into account machine availability, uniformity, process time and sel...
Multi-objective decision making using fuzzy discrete event systems: A mobile robot example
Boutalis, Yiannis; Schmidt, Klaus Verner (2010-09-29)
In this paper, we propose an approach for the multi-objective control of sampled data systems that can be modeled as fuzzy discrete event systems (FDES). In our work, the choice of a fuzzy system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. Our approach consists of three basic steps that are performed in each sampling instant. First, the current fuzzy state of the system is determined by a sensor evaluation. Seco...
Intelligent student assessment and coaching interface to web-based education-oriented intelligent experimentation on robot supported laboratory set-ups
Motuk, Halil Erdem; Erkmen, Aydan Müşerref; Erkmen, İsmet; Department of Electrical and Electronics Engineering (2003)
This thesis presents a framework for an intelligent interface for the access of robotsupported remote laboratories through the Internet. The framework is composed of the student assessment and coaching system, the experimentation scenario, and the associated graphical user interface. Student assessment and coaching system is the main feature of a successful intelligent interface for use during remote experimentation with a robot-supported laboratory setup. The system has a modular structure employing artifi...
Citation Formats
E. Kropat, A. Ozmen, G. W. Weber, S. Meyer-Nieberg, and Ö. DEFTERLİ, “FUZZY PREDICTION STRATEGIES FOR GENE-ENVIRONMENT NETWORKS - FUZZY REGRESSION ANALYSIS FOR TWO-MODAL REGULATORY SYSTEMS,” RAIRO-OPERATIONS RESEARCH, pp. 413–435, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57454.