Roberto L. Marichal

RETRACTED: Hopf bifurcation stability in Hopfield neural networks

In this paper we consider a simple discrete Hopfield neural network model and analyze local stability using the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, the Hopf bifurcation must be determined. For the case of two neurons, we find one necessary condition that yields the Hopf bifurcation. In addition, we determine the stability and direction of the Hopf bifurcation by applying normal form theory and the center manifold theorem. An example is given and a numerical simulation is performed to illustrate the results. We analyze the influence of bias weights on the stability of the quasi-periodic orbit and study the phase-locking phenomena for certain experimental results with Arnold Tongues in a particular weight configuration.

Automatic analysis of signals with symbolic content

This paper presents a set of methods for helping in the analysis of signals with particular features that admit a symbolic description. The methodology is based on a general discrete model for a symbolic processing subsystem, which is fuzzyfied by means of a fuzzy inference system. In this framework a number of design problems have been approached. The curse of dimensionality problem and the specification of adequate membership functions are the main ones. In addition, other strategies, which make the design process simpler and more robust, are introduced. Their goals are automating the production of the rule base of the fuzzy system and composing complex systems from simpler subsystems under symbolic constrains. These techniques are applied to the analysis of wakefulness episodes in the sleep EEG. In order to solve the practical difficulty of finding remarkable situations from the outputs of the symbolic subsystems an unsupervised adaptive learning technique (FART network) has been applied.

ULLSIMGRAF: An educational tool with syntax control for Grafcet notation

This article presents an educational tool for the teaching of Grafcet language for programmable logic controllers (PLC) and its application in the subject “Digital Devices for Industrial Automation” in a Industrial Electronics Engineering course. Apart from a simulator with its own editor, the tool includes a syntax controller with the purpose of maintaining Grafcet standard. In this way, the tool makes the validation of the implemented scheme easier.© 2012 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:669–677, 2014; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21558

Stability of Quasi-Periodic Orbits in Recurrent Neural Networks

A simple discrete recurrent neural network model is considered. The local stability is analyzed with the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, it is necessary to determine the Neimark-Sacker bifurcation. In the case of two neurons, one necessary condition that yields the Neimark-Sacker bifurcation is found. In addition to this, the stability and direction of the Neimark-Sacker bifurcation are determined by applying normal form theory and the center manifold theorem. An example is given and a numerical simulation is performed to illustrate the results. The phase-locking phenomena are analyzed for certain experimental results with Arnold Tongues in a particular weight configuration.

MASCONTROL: a MAS for system identification and process control

In this paper, a MAS, called MASCONTROL for system identification and process control is presented, including an Ontology Agent. It implements a self- tuning regulator (STR) scheme. Defined classes, properties, axioms and individuals in the ontology are mainly related to control concepts. These definitions and other ones allow the system to make some interesting inferences from some axioms defined in the ontology.

Evenet 2000: Designing and Training Arbitrary Neural Networks in Java

In this paper, Evenet-2000, a Java-Based neural network toolkit is presented. It is based on the representation of an arbitrary neural network as a block diagram (these blocks are, for example, summing junctions or branch points) with a set of simple manipulation rules. With this toolkit, users can easily design and train any arbitrary neural network, even time-dependent ones, avoiding the complicated calculations that the means of establishing the gradient algorithm requires when a new network architecture is designed. Evenet-2000 consists of three parts: a calculation library, a user-friendly interface and a graphic network editor with all the Java advantages: encapsulation, inheritance, powerful libraries.

Tools for Acquisition, Processing and Knowledge-Based Diagnostic of the Electroencephalogram and Visual Evoked Potentials

The objective of our research is to develop computer-based tools to automate the clinical evaluation of the electroencephalogram (EEG) and visual evoked potentials (VEP). This paper describes a set of solutions to support all the aspects regarding the standard procedures of the diagnosis in neurophysiology, including: (1) acquisition and real-time processing and compression of EEG and VEP signals, (2) real-time brain mapping of spectral powers, (3) classifier design, (4) automatic detection of morphologies through supervised neural networks. (5) signal analysis through fuzzy modelling, and (6) a knowledge based approach to classifier design.

Towards a knowledge based system for the clinical evaluation of the electroencephalogram and visual evoked potentials

The objective of our research is to develop computer based tools to automate the clinical evaluation of the electroencephalogram (EEG) and visual evoked potentials (VEP). The paper describes a set of solutions to support all the aspects regarding the standard procedures of the diagnosis in neurophysiology. More specifically, the paper focuses on the description of an approach to automate the process carried out by the physician when an EEG register has to be used to obtain a diagnostic of a patient. Two main lines have been established. The first one deals with the development of support tools to aid in the technical aspects of the process: data acquisition, data compression and brain map representation. The second line is focused on the classification and learning of neurophysiological signals. A classical approach has been successfully applied to the problem of brain maturation classification.

Ontology-based approach to Basic Grafcet formalization

Abstract In recent years, various proposals have been put forth to formalize the Grafcet graphical language. The objective of this paper is to propose an ontology-based approach to formalize this language. The authors have implemented a semi-coarse grain ontology written in Ontology Web Language and have tested it by including it in an existing educational tool for the teaching of Grafcet language for use in programmable logic controllers.

Analysis of multiple quasi-periodic orbits in recurrent neural networks

In this paper we consider a recurrent neural network model consisting of two neurons and analyze its stability using the associated characteristic model. In order to analyze the multiple quasi-periodic orbits, the strong resonance of this system, in particular that known as the R2 bifurcation, is also studied. In the case of two neurons, one necessary condition that yields the bifurcation is found. In addition, the direction of the R2 bifurcation is determined by applying normal form theory and the center manifold theorem. The simple conditions for ensuring the existence of multiple quasi-periodic orbits are given. The strong resonance phenomenon is analyzed using numerical simulations and is related with the codimension-two bifurcation of the high-iteration map.