http://popups.lib.uliege.be/1373-5411/index.php?id=80 Full text issues fr Mon, 13 May 2024 16:28:58 +0200 Tue, 18 Jun 2024 13:45:23 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=80 0 http://popups.lib.uliege.be/1373-5411/index.php?id=3556 Why anticipatory processes correspond to cognitive abilities of living systems? To be adapted to an environment, behaviours need at least i) internal representations of events occurring in the external environment; and ii) internal anticipations of possible events to occur in- the external environment. Interactions of these two opposite but complementary cognitive properties lead to various patterns of experimental data on semantic processing. How to investigate dynamic semantic processes? Experimental studies in cognitive psychology offer several interests such as: i) the control of the semantic environment such as words embedded in sentences; ii) the methodological tools allowing the observation of anticipations and adapted oculomotor behaviour during reading; and iii) the analyse of different anticipatory processes within the theoretical framework of semantic processing. What are the different types of semantic anticipations? Experimental data show that semantic anticipatory processes involve i) the coding in memory of sequences of words occurring in textual environments; ii) the anticipation of possible future words from currently perceived words; and iii) the selection of anticipated words as a function of the sequences of perceived words, achieved by anticipatory activations and inhibitory selection processes. How to modelize anticipatory semantic processes? Localist or distributed neural networks models can account for some types of semantic processes, anticipatory or not. Attractor neural networks coding temporal sequences are presented as good candidate for modelling anticipatory semantic processes, according to specific properties of the human brain such as i) auto-associative memory; ii) learning and memorization of sequences of patterns; and iii) anticipation. of memorized patterns from previously perceived patterns. Thu, 26 Sep 2024 09:35:52 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3556 http://popups.lib.uliege.be/1373-5411/index.php?id=3565 Genetic Algorithms (GAs) are robust probabilistic algorithms for optimization, relying strongly on parallel computation. Their power comes from multi-point exploiting of the searching space, avoiding the stagnation in local optima. First we review the state of art in GA theory. Next, two illustrative original applications highlight the efficiency of GA on multi-parameter optimization tasks: on solving systems of fuzzy relational equations, and on optimizing the parameters involved in an economic forecasting task. Thu, 26 Sep 2024 09:59:36 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3565 http://popups.lib.uliege.be/1373-5411/index.php?id=3572 The robotic inverse kinematic problem can be rightly classified as a very felt theme in the field of robotics. Many studies have been carried out in order to find new methods for the solution of the problem as alternatives to the traditional ones. In particular, every method able to improve the calculation speed is more and more appreciated. In the present paper an innovative method for the numerical inversion of non linear equations sets is shown. The approach is based on some procedures typical of the soft-computing area. In particular, the inverse kinematic problem is solved by a Neural Network optimised by means of a Genetic Algorithm acting inside an Hyperincursive scheme. After the introduction of the methodology developed, the paper shows some results obtained on a SCARA robot; they appear very good in terms of computational speed, even if the solution precision is not high near the boundaries of the working area. Thu, 26 Sep 2024 10:01:39 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3572 http://popups.lib.uliege.be/1373-5411/index.php?id=3579 This paper presents how can one generate a rich enough set of chaotic signals or random sequences with adequate, for CDMA communications, correlation properties using neural network based chaotic associative memories. Thu, 26 Sep 2024 10:04:23 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3579 http://popups.lib.uliege.be/1373-5411/index.php?id=3586 Fuzzy relational equations are without doubt the most important inverse problems arising from fuzzy set theory, and in particular from fuzzy relational calculus. Indeed, the calculus of fuzzy relations is a powerful one, with applications in fuzzy control and fuzzy systems modelling in general, approximate reasoning, relational databases, clustering, etc. In this paper, fuzzy relational equations are approached from an order-theoretical point of view. It is shown how all inverse problems can be reduced to systems of polynomial lattice equations. The exposition is limited to the description of exact solutions of systems of sup-T equations, and analytical ways are presented for obtaining the complete solution set when working in a broad and interesting class of distributive lattices. Thu, 26 Sep 2024 10:05:26 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3586 http://popups.lib.uliege.be/1373-5411/index.php?id=3592 The present paper includes a survey of notions in probability theory, carried over to the ground of the theory of bifuzzy sets. At the same time, it shows a possible combination of randomness and bifuzziness and signals other relationships of both the theories. Thu, 26 Sep 2024 10:10:48 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3592 http://popups.lib.uliege.be/1373-5411/index.php?id=3595 Recent advances in the neurosciences are leading to an understanding of the structures and processes in neural networks as electric activation patterns, consisting of oscillation fields and logical relation structures of neuronal assemblies, treated formally as coupled dynamic systems and neuronal attractors. These are specifically characterized by their space-time-dynamics. In the present context, these phenomena are also called neuronal resonance patterns, and as higher-order hierarchical aggregates, patterns of patterns: metapatterns, as Gregory Bateson would have termed it. The term pattern is suited equally well for the spatial as for the temporal domain, and thus allows to formulate an abstract conceptual system of the neuronal computation processes of organisms. In reformulation of Goethe's original ideas, such a systematics of metapatterns is called meta-morphology, in an effort to account especially for their dynamic, time-relevant aspects. The fundamental properties of such a system display a strong resemblance to a very ancient thought system that was known as Pythagoreanism in the Western tradition. The present contribution will show some of the parallels between the ancient system and the meta-morphology as outlined here. Thu, 26 Sep 2024 10:11:39 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3595 http://popups.lib.uliege.be/1373-5411/index.php?id=3601 We propose an operational research contribution to clinical psychopharmacology ; the key problems of drug selection and outcome prediction are tackled in a retrospective study about Mood and Anxiety Disorders focusing on sertraline, a selective inhibitor of serotonin reuptake. A three-step approach to data coding, clinical modeling and rule extraction is proposed, based on topographical techniques (Kohonen's Self-Organizing Maps) and information theory (Shannon entropy and mutual information). Clinical data are bitwise sampled, allowing an unbiased definition of system metrics. Uncertainty measures are introduced for a real-world sized approach to clinical practice; top-down induction decision trees (TDIDT) for drug administration are proposed, and a default logic of prescription is analyzed in the light of direct clinical experience and available literature data. Thu, 26 Sep 2024 10:13:22 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3601 http://popups.lib.uliege.be/1373-5411/index.php?id=3616 Fuzzy Set Theory has used many auxiliary methods into the trials of solutions of some medical problems. One of the attempts was the finding of the optimal level of the drug action in the case when the clinical symptoms disappeared completely after the treatment (Gerstenkorn and Rakus, 1994; Rakus, 1991). However, there can occur such a morbid process in which the symptoms do not disappear after the treatment. The medication improves too high or too low level of the quantitative symptom but it still indicates the presence of the disease. It sometimes makes some problems to choose the medicine which acts best because it can happen that most of them influence the same symptoms while they do not improve the others. A fuzzy decision making model tries to make easier to find such a drug which affects most of the symptoms in the highest degree. To solve this problem I propose the using of discrete values of the membership degrees in the model instead of the continuous ones which were tested in the paper of Rakus-Andersson and Gerstenkorn (1997). It should improve the thoroughness of the method and heighten the reliability of the accepted decision. It is also considered how to choose the best medicine in the circumstances when some decision-makers have different opinions about the priority of the tested drugs. Thu, 26 Sep 2024 10:24:13 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3616 http://popups.lib.uliege.be/1373-5411/index.php?id=3623 It becomes more important to construct an information retrieval system that can give flexible responses to variable user's requests in accordance with the evolution of the internet. We proposed a document retrieval system based on the maximum entropy principle on a Bayesian network (Saito, et al. 1998). We can claim two advantages for this document retrieval system. The first is that this system can produce several candidates of keywords which will help a user in retrieving a useful document, even if he can not recall a group of adequate keywords. The second is that this system can be customized corresponding to an individual user by tuning the parameters of a Bayesian network. But there is a problem for this system that it does not produce a retrieval result for some combinations of keywords. We have solved this problem, in this paper, by introducing fuzzy requests. Thu, 26 Sep 2024 10:26:27 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3623 http://popups.lib.uliege.be/1373-5411/index.php?id=3630 The paper describes specific current dynamic approaches to cognitive modelling alternative to the computational approach. Then a more general modelling framework is suggested that could accommodate both the computational and the dynamic approach, at least for what concerns the formalisms used. Thu, 26 Sep 2024 10:27:47 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3630 http://popups.lib.uliege.be/1373-5411/index.php?id=3637 It is explained how a cellular automaton can grow patterns that correspond to trained networks. Since a pattern corresponds to a map between an n-dimensional and an m-dimensional space, such a pattern can be called a 'meta-pattern'. The problems solved by connectionist multi-layered networks can be solved by the automaton too. In addition, it allows for a straightforward representation of patterns with internal bindings, even if such bindings are organized at several, hierarchically related levels. Further, if a problem has symmetry, then the form corresponding to its solution usually is aesthetically attractive. This contrasts with the black-box nature attributed to the classical connectionist approach. Since problems with symmetry are often called 'beautiful problems', the present system gives beautiful solutions for beautiful problems. Thu, 26 Sep 2024 10:28:56 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3637 http://popups.lib.uliege.be/1373-5411/index.php?id=3644 This paper outlines four major topics of tantamount importance to computing anticipatory systems. Section 1 introduces the reader to several historical facts regarding Daniel Dubois' hyperincursive modeling approach and its relationship with Gérard Langlet's work and the author's conception of parity logic systems. It provides the connection of Dubois' hyperincursivity theory and fractal machines with parity logic engines, a special class of binary integro-differential cellular automata. Section 2 on modeling anticipatory systems recalls first the essence of Dubois' anticipatory systems approach by comparing briefly recursivity, incursivity, and self-referentiality. Their impact on modeling cognitive anticipations is then discussed by rendering Piaget's recursive concept of anticipatory schemata into incursive schemata. Section 2 closes with an unresolved problem regarding anticipatory conflicts. Section 3 exhibits in a more formal way the difference between recursivity and incursivity by explicating Dubois' most important digital equations, how they apply to hyperincursive fractal machines, and how they are related to parity logic engines. This includes self-organized processing of parity intergrals and differentials, self-organized development of binary transforms, and several group theoretic implications of transforming parity matrices generated with fractal machines or parity logic engines. Finally, in section 4, further perspectives are outlined for the advancement of anticipatory systems by considering causal predictor systems in terms of fuzzy cognitive maps. This includes the law of concomitant variation, non-Aristotelian causality, the relationship between fuzzy causality, fuzzy subsethood and fuzzy causal cross-impact analysis. Thu, 26 Sep 2024 10:30:05 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3644 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 http://popups.lib.uliege.be/1373-5411/index.php?id=3654 http://popups.lib.uliege.be/1373-5411/index.php?id=3661 The soft tissue contrast provided by magnetic resonance imaging frequently makes it the modality of choice in diagnostic imaging. The paper describes the imagiIJ.g modality of magnetic resonance tomography in terms of harmonic analysis on the Heisenberg Lie group. Thu, 26 Sep 2024 10:32:26 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3661 http://popups.lib.uliege.be/1373-5411/index.php?id=3667 This paper shows that non-locality property occurs in simple diffusion neural equation: space local incursive discrete equation system transforms to a space non-local recursive equation system. The cable equation used for modelling the potential in neural membrane is similar to the Schrödinger quantum equation with a complex diffusion coefficient. Thu, 26 Sep 2024 10:33:13 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3667 http://popups.lib.uliege.be/1373-5411/index.php?id=3672 We are at a point now where the overall profile of Cosmic evolution is becoming discernible. In what follows, its broad outline is suggested. It is made as generic as possible, in order to encompass the processes of energy transformation from the birthing event of the cosmos to the present. To this effect, the paper is presented from the most general point of view, i.e. taking an energy stance, on the grounds that energy is the ultimate substrate of all there is. At the core of it are two distinguishable elements: the elementary processes of energy transformation that result in the emergence of new systems, and their modal character which governs their diversification. Thu, 26 Sep 2024 10:41:30 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3672 http://popups.lib.uliege.be/1373-5411/index.php?id=3677 The concept of information is not yet perfectly integrated into our modern science. As a tentative approach the concept of information space is proposed here. An information space consists of elementary semantically meaningful statements (or interpretable structures) together with specific relations between these elements. Relations can either be seen in analogy to the links known from hypertext systems, or they are defined by similarity relations between the elements. Both the elements and the relations can be modified in the course of time, and possible paths through such a space (navigation) are discussed together with their effects on the underlying structures. The mathematics of similarity relations and their context dependence are studied in some detail. The structures proposed here can be modeled on a standard PC; possible applications are sketched. Thu, 26 Sep 2024 10:42:40 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3677 http://popups.lib.uliege.be/1373-5411/index.php?id=3683 Neuronal schemes realizing operations with continuous quantities are presented. These schemes are based on an analog neuron as a "diode-like" summator of continuous quantities (spike frequencies). Analog (continuous) logic, non-linear feedback, and neuronal structures, which can realize the complex features of information filtration, are discussed. Special attention is paid to factorial switches and synthesis of neuronal structures. Neuronal structures processing n-dimensional continuous vectors by non-linear feedbacks can realize the factorial switch, which stores and reproduces information about decreasing order of the components of the vectors. Thu, 26 Sep 2024 10:44:03 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3683 http://popups.lib.uliege.be/1373-5411/index.php?id=3688 At the end of the last century, Planck opened the door onto the quantum mechanical world. Yet, ever since, despite enormous advances, science has resisted the precept implicit in the quantum mechanical formalism now increasingly confirmed by the experimental evidence, that reality is fundamentally non-local and reducible only locally to the classical Newtonian understanding that subsumed science before Planck's discovery. Even now such advanced ideas for the unification of physics such as string theory begin by quantizing an essentially classical model. Yet the expanding science and emerging technology of quantum cybernetics and information processing will, I am now convinced, change this. In particular, quantum holography/holochory offers such quantum non-local modelling such as quantum neural information processing or the completion began by Einstein, for Riemann's programme for the geometrization of physics, where the local classical models emerge as invariants. It is not therefore that neural networking or Einstein's general relativity are wrong, but that they are of limited application to modelling the reality in which we live, and that quantum models offer a new category of explanatory power, as this paper attempts to demonstrate, the breadth of which has yet to be fully appreciated. Thu, 26 Sep 2024 10:45:32 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3688 http://popups.lib.uliege.be/1373-5411/index.php?id=3692 This paper presents a hypothesis for integrating into the scientific framework phenomena of consciousness which frequently have been considered beyond scientific description. Intuition, telepathy, clairvoyance and many similar information phenomena seem to be easily explained by means of the non-local quantum hologram. It is further postulated that from the point of view of evolution, quantum non-locality is the basis from which self organizing cosmological processes have produced the common phenomenon of perception in living organisms. Thu, 26 Sep 2024 10:46:49 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3692 http://popups.lib.uliege.be/1373-5411/index.php?id=3697 Described is a "neural" operating system based on Holographic/Quantum Neural Technology (HNeT). The core of the HNeT technology applies Hilbert space operations in both the updating of cortical memory and generation of response recall, similar in form to the QM wave function. Within HNeT, information is represented by sets of complex scalars, leading to a natural predilection towards frequency domain representations of stimuli. Conversion of real valued information sets to frequency domain representation leads to a number of desirable qualities, such as orthogonalization of highly asymmetric or non-orthogonal pattern sets, a distributed representation of information, as well as an effective means for data reduction (i.e. Fourier quantization). Higher order frequency domain representations facilitate extraction of invariants that define discriminating features, often intractable using conventional pattern classification methods. This is performed utilizing a form of neural plasticity that scans the set of higher order harmonics for discovery of such invariants. One of the most salient operational aspects of holographic/ quantum neural technology is the reduction in computational complexity over more traditional neural networks (NN). For instance, HNeT requires only binary cell structures in quite advanced application areas. Holographic/quantum neural technology also provides a dramatic increase in speed of learning and learning accuracy over traditional NN methods. The HNeT process facilitates real-time learning, in which large data sets may be learned to high accuracies following one training epoch. The HNeT core processes have been extended considerably over the past few years to incorporate a number of auxiliary features. These features include application of higher order combinatorics for pre-process of input stimuli, the application and advanced control of neural plasticity, the use of cell assemblies that facilitate "super-cell" structures similar in form to neo-cortical assemblies, and unsupervised learning structures that facilitate hyperincursive and spatio-temporal learning paradigms, among others. Current work is directed towards structures that facilitate temporal accumulation of spatial patterns at the preprocess level, prior to entry into cortical cell structures. These accumulative structures possess certain analogous features to the thalamus, permitting synthetic neural systems to learn spatio-temporal patterns such as speech. The HNeT system is biologically motivated, possessing an application programming interface (API) that allows the user to allocate specific cell types based on the granule, pyramidal, stellate, and Purkinje cells of the cerebellum and neo-cortex. This operating system permits the user to flexibly configure cell assemblies, and build cortical structures comprised of anywhere from 2 to several thousand cells. Thu, 26 Sep 2024 10:48:15 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3697 http://popups.lib.uliege.be/1373-5411/index.php?id=3708 Models exist in many forms; many of these go beyond the level of words, or communicable systems. The recognition of models goes even beyond what we consciously know. The basis of modelling can be sought in Languaging, as many authors have done (Grinder & Bandier, 1975). They can even be identified in the dynamic organisation of our neurones, as more recent work has shown (Maturana & Varela, 1980). That being the case, the inherent cross-interaction of the models we make, and the system in which we make them, is an issue to be considered. It calls for an understanding of our body (and neurones) not as a computing machine, but as information processor in a more 'rarefied' (and extended) sense than science can yet show, within the limitations that Cultural Consensus imposes. As long as these human limits to human-made models are ignored, we can't get beyond to perceive reality as it is: our models, conditioning our mind, will be (culturally conditioned) unconsciously in our way. This paper proposes reconsideration from first principles, by showing a relationship between Space, Time, Energy and Consciousness which help to get to the basis of the models we make, beyond the forms they can take. Thu, 26 Sep 2024 10:54:02 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3708 http://popups.lib.uliege.be/1373-5411/index.php?id=3564 Thu, 26 Sep 2024 09:37:03 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3564