http://popups.lib.uliege.be/1373-5411/index.php?id=641 Index terms fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=1674 Activities of living beings are presented as bioinformation procedures of closed-looped coding-decoding. In the process of coding, dynamic states of real dynamical structures of matter and energy are reflected in states of memory structures (DNA, hormones, neuronets). In the process of decoding the activated states of the structures of the memory are re-reflected in the dynamic states of the real structures of matter and energy. This is the essence of control. Biological evolution is interpreted as formation of hierarchically organized dynamic structures of closed-looped coding-decoding and reproduction of them. Increased complexitv of these structures determines a higher level of control. Mon, 15 Jul 2024 16:47:08 +0200 Tue, 08 Oct 2024 14:42:48 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1674 http://popups.lib.uliege.be/1373-5411/index.php?id=1337 All man made models are reflection of our own functioning, origin, and involvement in life. 'Reality' is in fact a Realisation, totally dependent on, and conditioned by, the way, we relate to our environment. This Involvement can be described in terms of a Boundary Transition; relating the perspectives of an Insider, Inter-actor, Reactor and Outsider. In our body and being, these are simultaneous modes of being (and determinant for our experience of life, and health). These changes in degree of involvement are in essence the same as the changes in degree of recursion, as seen in the relationship between, e.g. solids, liquids, gas and plasma; all of which can be unified in a description of relations of Phase. By using Phase Space as the common denominator' for our perception of reality, we can describe the characteristics of our realisations in the same terms. The relationships between Options & Choices, Doubts & Decisions then are clarifiers for the changes in involvement, perspective, realisation, and thus the 'reality' (life health) that we live (individually and collectively). This pattern of involvement (with its dynamics and conditions, and fundamental basis) needs to be explicitly described, and accounted for, because all the models we make are based on our own functioning (including the shortcomings in our own self-understanding). By explicitly describing the changes in involvement, we can reconcile difference between our own experience, and those with of others, which helps reconcile fundamental issues of (mis)understanding. It also makes it possible to relate seemingly different models, which is again relevant for resolving issues such as the difference between Subjective and Objective observation, which again can be expressed in terms of Crossing a Boundary; for which our shift in involvement (thus Locus of Control) is the key concept. The ([4D]) logical relationship between our Options & Choices, Doubts & Decisions are the our most immediate 'handle' on the reality and life that we live. Wed, 10 Jul 2024 10:55:44 +0200 Tue, 08 Oct 2024 13:58:26 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1337 http://popups.lib.uliege.be/1373-5411/index.php?id=3654 We first discuss the importance of making a controller interpretable and give an overview of the existing models and structures for that purpose. We then propose an approach to designing fuzzy controllers based on the B-spline model by learning. Unlike other normalised parametrised set functions for defining fuzzy sets, B-splines do not necessarily span membership values from zero to one but possess the property of "partition of unity". B-splines can be automatically determined after each input is partitioned. Learning of a fuzzy controller based on B-splines is then equivalent to the adaptation of a B-spline interpolator. Parameters of the controller output of each rule can be rapidly adapted by gradient descent. Optimal placements of the non-uniform B-splines for specifying each input can be found by Genetic Algorithms. Through comparative examples of function approximation we show that training of such a fuzzy controller generally provides results with minimal error. The approach can be extended to the problems of high-dimensional input by combining neural networks with a fuzzy control model. Thu, 26 Sep 2024 10:31:08 +0200 Thu, 26 Sep 2024 10:31:30 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3654 http://popups.lib.uliege.be/1373-5411/index.php?id=2055 In this paper Inertia as a global parameter of system and process is considered. Beginning with the mechanical and electrical processes where a time-delay phenomena appears, the authors give a new definition of Inertia : The Inertia is the system global internal parameter, which produces the entropy increase, as the effect of input variation. The definition is based on system information measurement, to appreciate the level of organization against the entropy. This definition allows analyzing the different systems and determining the major effect on the quality of the processes. Fri, 26 Jul 2024 16:15:30 +0200 Fri, 26 Jul 2024 16:15:41 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2055 http://popups.lib.uliege.be/1373-5411/index.php?id=1953 The given paper investigates some strong anticipation characteristics, inherent to Automata Theory Problems. It is extracted anticipation's role in linear automaton's Controllability/Observability analysis. Via decision-making process presentation in terms of some special walks on some directed labelled multigraph, There is characterized strong anticipation for Problems of weakly initialized finite automaton's internal states identification, as well as of maximal supervisor's design for any discrete event automata-based system. Presentation of winning strategy's design for any Two-Players Game on a graph in terms of design of multi-headed Turing Machine with some arbiter and independently controlled heads outlines some general anticipatory characteristics, inherent to distributed computing. Wed, 17 Jul 2024 15:28:41 +0200 Wed, 17 Jul 2024 15:28:48 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1953 http://popups.lib.uliege.be/1373-5411/index.php?id=952 The concept of an organized system and the historical review of the problem are presented. The concept explains biological phenomena and cybernetics systems in a point of view of functional organization. Cybernetics and information theory consider physical and chemical transformations of energy and matter in organized systems only as signals and a means for realization of some purposive informational control programs. Life (or Artificial Life) is considered to be an adaptive system that stores information. The more such a biological (or artificial) system (species) is effective in storing and using information, the fitter the programs are. Evolution of systems from non-organized and dissipative transformations through elementary organized-regulators, programmed controllers, servo-controllers up to anticipatory systems is discussed. The elementary functional components of a biological cell, a multicellular organism, and an animal are analysed as organized system Mon, 01 Jul 2024 15:28:37 +0200 Mon, 01 Jul 2024 15:28:49 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=952 http://popups.lib.uliege.be/1373-5411/index.php?id=637 Neural networks are structures consisting of highly interconnected elementary computational units. The network map input patterns into output patterns. These devices are called neural not because they model the nervous systems but because they are inspired by them. Due to their features (adaptive behaviour, learning process, robustness,...) , artificial neural networks have interested the anticipatory systems field of research. In particular, we will examine their interests in the field of time series prediction and system identification. Fri, 28 Jun 2024 15:44:31 +0200 Fri, 28 Jun 2024 15:44:46 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=637