27896987
Computational Neural Networks in Conjunction with Principal Component Analysis for Resolving Highly Nonlinear Kinetics
A method based on the use of an orthogonal linear filter, principal component analysis (PCA), for preprocessing data used as input for a feed-forward neural network is proposed. The method analyzes the significance of the eigenvalues of the correlation matrix associated with the first principal components of the data in order to select the subset of principal components for the sample that provides the optimum generalization value. The generalization error was estimated by using the leave-one-out method, because it provides the most reliable results for the fairly small data set used. The performance of the proposed method was assessed by applying it to the resolution of mixtures of species exhibiting a very similar kinetic behavior in the presence of a mutual kinetic (synergistic) effect. In addition, use of the continuous-addition-of-reagent (CAR) technique, a second-order approach, increased the nonlinearity of the system studied. Based on the results, the proposed designs provide accurate estimates in...
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.
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.
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 ...
9142441
Understanding Users' Response To Ontology Based Systems In The Context Of An Enterprise Sponsored Virtual Community
This paper aims at presenting the preliminary results of a research work that seeks to understand the users’ response to semantic based technologies, in the context of enterprise sponsored virtual communities. The research follows a qualitative methodology based on an action research approach. It particularly focuses on the socio-cognitive processes that underlie users’ learning and acquisition methods when training and interacting with a new knowledge management approach based on semantically enabled technologies in a collaborative, and sometimes virtual, learning/working environment. The outcomes of this research are expected to provide an assessment framework for a deeper level understanding of the cognition process in what concerns the evolution of individual’s knowledge, opinions, beliefs, and thoughts about ontology based systems.
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.
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.
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.
42437646
Interleaving Functional and Performance Structural Analysis of Net Models
The design of parallel and distributed systems requires considering both functional and performance specifications and properties. The use of a single family of models to deal with both aspects, plus appropriate analysis techniques, are very important in practice. Petri Nets are one of the formal description techniques able to capture both functional and performance aspects. We illustrate here, after our research experience, how the interleaving of the functional and the performance analysis theories produces a synergetic situation, in which each contributes to the development of the other.