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 ...
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.
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.
37094625
On periodic schedules for deterministically timed Petri net systems
Introduces a Petri net based perspective to periodic schedules. These are classified, according to the time interpretation in multiple-server semantics and/or transitions firing periodicity constraints, into strict and general periodic schedules. Using a net transformation rule, the computation of the general schedule class can be done through techniques for the strict subclass. A necessary and sufficient condition for the feasibility of a strict periodic schedule is given. Estimating the truncation error at initial firing time epoch tau /sub 1/ allows to get linear systems of inequalities as necessary conditions for a schedule to be feasible. An linear programming (LP) based heuristic approach is proposed to deal with the computation of 'optimum' (i.e. minimal time) strict periodic schedules under the single-server semantics.<<ETX>>
26738657
Genetic algorithm for sex-fair stable marriage problem
In this paper we consider a sex-fair matching in the stable marriage problem. The sex-fair stable matching defined in this paper has a property that the sum of the partner's rank of each man in his preference list is as close as possible to the sum of the partner's rank of each woman in her preference list. We transform the sex-fair marriage problem into a graph problem which is suitable for a genetic algorithm. We propose a genetic algorithm for the sex-fair stable matching. The transformation into the graph problem makes application of the GA easier and effective. Computer experiments show the effectiveness of the proposed genetic algorithm.