18278319
A two steps method: non linear regression and pruning neural network for analyzing multicomponent mixtures
This work deals with the use of pruning ANNs in conjunction with genetic algorithms for resolving nonlinear multicomponent systems based on oscillating chemical reactions. The singular analytical response provides by this chemical system after its perturbation was tted to a gaussian curve by least-square regression and the estimates were used as inputs to the ANNs. The proposed methodology was validated by the simultaneous determination of pyrogallol and gallic acid (two strong related phenol derivatives) in mixtures on the basis of their perturbation e ects on the classical Belousov-Zhabotinskii reaction. The trained network estimates concentrations of pyrogallol and gallic acid with a standard error of prediction for the testing set of ca. 4% and 5.7% respectively or 4.4%, 9% for di erent sets of train/test patterns. This result is much smaller than those provided by a classical parametric method such as non-linear regression.
34650038
Estimation of Parameters of Kinetic Compartmental Models by Use of Computational Neural Networks
A novel methodological approach to the estimation of parameters involved in multicomponent kinetic determinations from real kinetic data by use of computational or artificial neural networks (ANNs) is proposed. The ANN input data used are also estimates obtained by using the Levenberg−Marquardt method in the form of an approximate nonlinear function that is the sum of the two expressions associated with the pseudo-first-order kinetics of the two mixture components. The performance of the optimized network architecture, 2:4s:2l, was tested at variable rate constant ratios. The reduced dimensions of the network input space obtained using the Kolmogorov−Sprecher theorem result in improved limits of precision in estimating parameters at near-unity rate constant ratios. Experiments with real kinetic data provided a relative standard error of prediction of 2.47% and 4.23% for the two mixture components. These errors are much smaller than those obtained with existing alternative methods, particularly at the low ...
30523521
Correction of Temperature Variations in Kinetic-Based Determinations by Use of Pruning Computational Neural Networks in Conjunction with Genetic Algorithms
The joint use of genetic algorithms and pruning computational neural networks is shown to be an effective means for selecting the number of inputs required to correct temperature variations in kinetic-based determinations. The genetic algorithm uses a pruning procedure based on Bayesian regularization and is highly efficient as a feature selector; it provides quite good results in the generalization process without the need to use a validation set. The fitness function is defined as the sum of two subfunctions: one controls the learning ability of the network and the other its complexity. The training, pruning, and generalization processes were initially tested with simulated data in order to acquire preliminary information for the ensuing work with real data. The performance of the proposed method was assessed by applying it to the determination of the amino acid L-glycine by its classical spectrophotometric reaction with ninhydrin. A straightforward network topology including temperature as input (40+T:2:1 with 19 connections after the pruning process) was used to estimate the L-glycine concentration from kinetic curves affected by temperature variations over the range 60-75 degrees C, using kinetic data acquired up to only 1.5 half-lives. The trained network estimates this concentration with a standard error of prediction for the testing set of ca. 8%, which is much smaller than those provided by a classical parametric method such as nonlinear regression (even if kinetic data acquired at longer half-lives are used). Finally, a kinetic interpretation of the pruning process is provided in order to better demonstrate its potential for kinetic analysis.
13520593
Improving the Quantification of Highly Overlapping Chromatographic Peaks by Using Product Unit Neural Networks Modeled by an Evolutionary Algorithm
This work investigates the ability of multiplicative (on the basis of product units) and sigmoidal neural models built by an evolutionary algorithm to quantify highly overlapping chromatographic peaks. To test this approach, two N-methylcarbamate pesticides, carbofuran and propoxur, were quantified using a classic peroxyoxalate chemiluminescence reaction as a detection system for chromatographic analysis. The four-parameter Weibull curve associated with the profile of the chromatographic peak estimated by the Levenberg-Marquardt method was used as input data for both models. Straightforward network topologies (one output) allowed the analytes to be quantified with great accuracy and precision. Product unit neural networks provided better information ability, smaller network architectures, and more robust models (smaller standard deviation). The reduced dimensions of the selected models enabled the derivation of simple quantification equations to transform the input variables into the output variable. These equations can be more easily interpreted from a chemical point of view than those provided by sigmoidal neural networks, and the effect of both analytes on the characteristics of chromatographic bands, namely profile, dispersion, peak height, and residence time, can be readily established.
25007978
Use of Pruned Computational Neural Networks for Processing the Response of Oscillating Chemical Reactions with a View to Analyzing Nonlinear Multicomponent Mixtures
The suitability of pruned computational neural networks (CNNs) for resolving nonlinear multicomponent systems involving synergistic effects by use of oscillating chemical reaction-based methods implemented using the analyte pulse perturbation technique is demonstrated. The CNN input data used for this purpose are estimates provided by the Levenberg-Marquardt method in the form of a three-parameter Gaussian curve associated with the singular profile obtained when the oscillating system is perturbed by an analyte mixture. The performance of the proposed method was assessed by applying it to the resolution of mixtures of pyrogallol and gallic acid based on their perturbating effect on a classical oscillating chemical system, viz. the Belousov-Zhabotinskyi reaction. A straightforward network topology (3:3:2, with 18 connections after pruning) allowed the resolution of mixtures of the two analytes in concentration ratios from 1:7 to 6:2 with a standard error of prediction for the testing set of 4.01 and 8.98% for pyrogallol and gallic acid, respectively. The reduced dimensions of the selected CNN architecture allowed a mathematical transformation of the input vector into the output one that can be easily implemented via software. Finally, the suitability of response surface analysis as an alternative to CNNs was also tested. The results were poor (relative errors were high), which confirms that properly selected pruned CNNs are effective tools for solving the analytical problem addressed in this work.
121386022
On observability in timed continuous Petri net systems
Fluidification is a classical relaxation technique for approximated performance evaluation of discrete systems. In this paper we deal with T-timed fully continuous Petri Nets working under infinite servers semantics, what leads to (deterministic) piecewise linear differential systems. Switches among dynamic linear systems are triggered by internal events through minimum operators on marking variables. The observability problem consists of estimating the (initial) marking from a partial measure. This paper is devoted to observability concepts and criteria in this particular class of systems, not to the observers design. The concept of structural observability, regarding to the possibility of estimating the marking of places for any speed of the transitions is introduced and studied for the subclass of join-free Petri nets (JF). For non join-free Petri nets, conditions to compute suitable estimates will be established.
15093494
Developing Enterprise Sponsored Virtual Communities: The Case of a SME's Knowledge Community
This paper presents a case in the development of a knowledge community support system in the context of an industrial association group in the construction sector This system is a result of the Know-Construct project which aims at providing association sponsored SME communities of the construction sector with a sophisticated information management platform and community building tools for knowledge sharing The paper begins by characterizing the so-called construction industry knowledge community The Know-Construct system concept and the its general architecture are described, focusing on the semantic resources, in particular the ontologies structure The final part of the paper depicts the approach to the actual introduction of the system in the community An action-research approach was planned to obtain research results regarding the social acceptance of semantic resources such as the ontologies and technical classifications used in system.
28633247
B-Fairness and Structural B-Fairness in Petri Net Models of Concurrent Systems
Abstract Fairness properties are very important for the behavior characterization of distributed concurrent systems. This paper discusses in detail a bounded-fairness (or B-fairness) theory applied to Petri Net (PN) models. For a given initial marking two transitions in a Petri Net are said to be in a B-fair relation (BF-relation) if the number of times that either can fire before the other fires is bounded. Two transitions are in a structural B-fair relation (SF-relation) if they are in a B-fair relation for any initial marking. A (structural) B-fair net is a net in which every pair of transitions is in a (structural) B-fair relation. The above B-fairness concepts are further extended to groups (or subsets) of transitions, and are called group B-fairness. This paper presents complete characterizations of these B-fairness concepts. In addition, algorithms are given for determining B-fairness and structural B-fairness relations. It is shown that structural B-fairness relations can be computed in polynomial time.
800062
Words avoiding repetitions in arithmetic progressions
Carpi constructed an infinite word over a 4-letter alphabet that avoids squares in all subsequences indexed by arithmetic progressions of odd difference. We show a connection between Carpi's construction and the paperfolding words. We extend Carpi's result by constructing uncountably many words that avoid squares in arithmetic progressions of odd difference. We also construct infinite words avoiding overlaps and infinite words avoiding all sufficiently large squares in arithmetic progressions of odd difference. We use these words to construct labelings of the 2-dimensional integer lattice such that any line through the lattice encounters a squarefree (resp. overlapfree) sequence of labels.
30131271
A Simple and Fast Algorithm to Obtain All Invariants of a Generalized Petri Net
After a linear algebraic characterization of the minimal support invariant concept, it is proposed a very efficient algorithm to calculate all the minimal support invariants of Generalised and Capacity Petri Nets.