Volume 1, Issue 1 (11-2009)
Abstract
This paper is focused on fuzzy theory of democracy. Here, it has been argued that the theory of democracy provides a two-valued description of political order (democratic or non democratic).However, this account of democracy is not consistent with the scientific truth. Democracy is characterized by fuzzy truth. Thus, following a critical discussion on the epistemology of critical rationality, a fuzzy epistemic apparatus has been formulated. Considering the fuzzy epistemology, it has been claimed that on this epistemic horizon, every thing is relatively calibrated and truth is something between zero and one. Also, in the mentioned apparatus, the black and white truth transforms into a gray truth and all follow the principle of uncertainty. Consequently, in the fuzzy epistemology, the membership function of zero and one will be generalized to a fuzzy membership based on a range of zero and one. To support the above mentioned epistemic apparatus, the writer of the paper has provided an empirical argument. The evidences the writer provides indicate that democracy as a truth represents values in the range of zero to one. The value of zero represents a completely non democratic country and the value of one represents a full democratic one. Any other value between zero to one is an indicator of a mixed category consisting of both democratic and non democratic aspects. Finally, based on the empirical evidences, it has been concluded that the U.S.A, England, Japan, Turkey, India, France, and Iran (1998-2003) are of democratic countries, albeit the kind and degree of democracies vary
Saeed Hashemnia, Masoud Shariat Panahi,
Volume 15, Issue 10 (1-2016)
Abstract
In the present article, an improved Learning Classifier Systems (LCS) is proposed to control the balance of a moving unmanned bicycle. Significant characteristics of learning classifier systems is that they can learn through a set of system actions in the real world (similar to intelligent creatures) while no dynamic model of the system is needed. Contrary to studies reported in the literature where action domain of the controller is discrete and accordingly such controller cannot be used in real world applications, in the present study efficacy of the classifier system is enhanced by definition of continuous domain for the outputs, and then is used to control the balance of unmanned bicycle. A scheme based upon fuzzy membership functions is proposed which makes it possible for the domain of actions to be continuous. The proposed LCS features a dynamic reward assignment mechanism which is invented to cope with the bicycle’s delayed response due to its mass inertias. This allows the rapid calculation of the reward and hence enables the controller to be used in such real time applications as the balance control of unmanned vehicles. A standard 2 degree of freedom (2-DOF) bicycle model is employed to demonstrate the efficiency of the enhanced LCS. Simulation results show that the proposed classifier system outperforms traditional classifier system as well as some of the more common balance-control strategies reported in the literature.
