FRACTAL SET-THEORETIC ANALYSIS OF PERFORMANCE LOSSES FOR TUNING TRAINING DATA IN LEARNING-SYSTEMS

Date

1992-08-28

Author

Erkmen, Aydan Müşerref

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

181
views

0
downloads

This paper focuses on the evaluation of learning performance in intelligent dynamic processes with supervised learning. Learning dynamics are characterized by basins of attraction generated by state transitions in control space (statespace + parameter space). State uncertainty is modelled as a cellular control space, namely the cell space. Learning performance losses are related to nonseparable basins of attractions with fuzzy boundaries and to their erosions under parameter changes. Basins erosions are analyzed as fingering regions which quickly loose their compactness yielding regions of fractional dimensions and degeneracies due to bifurcation phenomena. We therefore claim that “learning” quality of intelligent dynamic processes should be measured by fractal set theoridc methods. To this end, we generate in this paper learning patterns as convergence maps using the cell to cell mapping concept. We then evaluate predictability of these patterns based on Lyapunov exponents. Performance measures in training are generated based on box counting fractal dimensions and the lose of reliability is detected by bifurcation phenomena. Illustrative results are reported for a collision free intelligent path planner of a planar robot manipulator.

Subject Keywords

Cell to cell mapping, Bifurcation phenomena, Intelligent sensorimotor robot control, Learning predictability, Complexity measures

URI

https://hdl.handle.net/11511/52764

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Intelligent learning system for online learning Serçe, Fatma Cemile; Alpaslan, Ferda Nur; Jain, Lakhmi (2008-10-01) The paper presents an Adaptive Intelligent Learning System (AILS) designed to be used with any Learning Management System (LMS). The adaptiveness provides uniquely identifying and monitoring the learner's learning process according to the learner's profile. AILS has been implemented as a multi-agent system. The agents were developed as JADE agents. The paper presents the learning model, system components, agent behavior in learner scenarios, the ontologies used in agent communications, and the adaptive stra...
Trust attribution in collaborative robots: An experimental investigation of non-verbal cues in a virtual human-robot interaction setting Ahmet Meriç, Özcan; Şahin, Erol; Acartürk, Cengiz; Department of Bioinformatics (2021-6) This thesis reports the development of non-verbal HRI (Human-Robot Interaction) behaviors on a robotic manipulator, evaluating the role of trust in collaborative assembly tasks. Towards this end, we developed four non-verbal HRI behaviors, namely gazing, head nodding, tilting, and shaking, on a UR5 robotic manipulator. We used them under different degrees of trust of the user to the robot actions. Specifically, we used a certain head-on neck posture for the cobot using the last three links along with the gr...
Hierarchical behavior categorization using correlation based adaptive resonance theory Yavaş, Mustafa; Alpaslan, Ferda Nur; Department of Computer Engineering (2011) This thesis introduces a novel behavior categorization model that can be used for behavior recognition and learning. Correlation Based Adaptive Resonance Theory (CobART) network, which is a kind of self organizing and unsupervised competitive neural network, is developed for this purpose. CobART uses correlation analysis methods for category matching. It has modular and simple architecture. It can be adapted to different categorization tasks by changing the correlation analysis methods used when needed. Cob...
Pattern recognition in bistable networks VLADIMIR, CHINAROV; Halıcı, Uğur; Leblebicioğlu, Mehmet Kemal (1999-04-08) Present study concerns the problem of learning, pattern recognition and computational abilities of a homogeneous network composed from coupled bistable units. An efficient learning algorithm is developed. New possibilities for pattern recognition may be realized due to the developed technique that permits a reconstruction of a dynamical system using the distributions of its attractors. In both cases the updating procedure for the coupling matrix uses the minimization of least-mean-square errors between the ...
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...

Citation Formats

A. M. Erkmen, “FRACTAL SET-THEORETIC ANALYSIS OF PERFORMANCE LOSSES FOR TUNING TRAINING DATA IN LEARNING-SYSTEMS,” 1992, vol. 114, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52764.