Fuzzy modeling to predict chicken egg hatchability in commercial hatchery

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Uma análise crítica dos ideários pedagógicos contemporâneos à luz da teoria de A. N. Leontiev

O presente artigo objetiva demonstrar o alcance das pedagogias do aprender a aprender (DUARTE, 2001), uma vez que elas fundamentam as propostas educacionais no âmbito nacional e no internacional, na atualidade; bem como apresentar suas principais premissas, que mantêm sua filiação ao pragmatismo e ao irracionalismo e, no limite, submetem o desenvolvimento humano à ordem do capital. Tais proposições se contrapõem ao objetivo humanizador da educação escolar pressuposto na produção teórica do psicólogo soviético Alexis Leontiev. Como resultados, temos que, dessas produções, é possível extrair subsídios para o enfrentamento do processo de esvaziamento do ensino escolar e do papel do professor engendrado pelas referidas pedagogias. Além disso, pretendese evidenciar a impossibilidade de aproximações entre as produções da psicologia histórico-cultural com outras correntes pedagógicas que estão a serviço da legitimação e do revigoramento da sociedade capitalista.

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier B.V. B.V. All rights reserved.

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Phylogenetic Relationships in Eleocharis (Cyperaceae): C-4 Photosynthesis Origins and Patterns of Diversification in the Spikerushes

Eleocharis (Cyperaceae) is a morphologically and physiologically diverse lineage of 250 + species with a cosmopolitan distribution. We here explore phylogenetic relationships in this lineage using maximum parsimony and Bayesian inference analyses of nrDNA ITS and cpDNA trnC-ycf6 and ycf6-psbM sequence data with the goals of comparing our phylogenetic hypotheses to previous classifications, morphological variation, and photosynthetic pathway variation. Our results suggest that in Eleocharis C, photosynthesis has been derived at least three times, with several cases of possible reversion to C-3-like or intermediate pathways and several additional origins of C-3-C-4 intermediate photosynthetic pathways, as inferred by carbon isotope ratio measurements. Many classification units currently recognized in Eleocharis are not monophyletic, however, E. subgenus Limnochloa and E. subgenus Scirpidium are monophyletic. Other classification units largely corresponding to clades include E. subgenus Zinserlingia, E. subseries Chaetariae, and E. series Maculosae. Problems with species circumscription and morphological variation in several groups are discussed in light of the phylogeny, particularly in the context of species membership of seven focal clades found in the analyses.

Optimization procedures in the design of continuous induction hardening and tempering installations for magnetic steel bars

An inverse problem concerning the industrial process of steel bars hardening and tempering is considered. The associated optimization problem is formulated in terms of membership functions and, for the sake of comparison, also in terms of quadratic residuals; both geometric and electromagnetic design variables have been considered. The numerical solution is achieved by coupling a finite difference procedure for the calculation of the electromagnetic and thermal fields to a deterministic strategy of minimization based on modified Flctcher and Reeves method. © 1998 IEEE.

Case-based system: Indexing and retrieval with fuzzy hypercube

In some applications with case-based system, the attributes available for indexing are better described as linguistic variables instead of receiving numerical treatment. In these applications, the concept of fuzzy hypercube can be applied to give a geometrical interpretation of similarities among cases. This paper presents an approach that uses geometrical properties of fuzzy hypercube space to make indexing and retrieval processes of cases.

Simulation of active control using fuzzy logic applied to a pulse combustor

This work analyzes an active fuzzy logic control system in a Rijke type pulse combustor. During the system development, a study of the existing types of control for pulse combustion was carried out and a simulation model was implemented to be used with the package Matlab and Simulink. Blocks which were not available in the simulator library were developed. A fuzzy controller was developed and its membership functions and inference rules were established. The obtained simulation showed that fuzzy logic is viable in the control of combustion instabilities. The obtained results indicated that the control system responded to pulses in an efficient and desirable way. It was verified that the system needed approximately 0.2 s to increase the tube internal pressure from 30 to 90 mbar, with an assumed total delay of 2 ms. The effects of delay variation were studied. Convergence was always obtained and general performance was not affected by the delay. The controller sends a pressure signal in phase with the Rijke tube internal pressure signal, through the speakers, when an increase the oscillations pressure amplitude is desired. On the other hand, when a decrease of the tube internal pressure amplitude is desired, the controller sends a signal 180° out of phase.

Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.

Trabalho de docência e comportamento micro-organizacional: A motivação do docente na área da saúde

Using the motivograma and interviews the degree of motivation of the survey participants was defined as satisfactory. On one hand there is satisfaction with the physiological needs, self-esteem and self confidence, on the other there is high dissatisfaction with the association and self-realization, justifying the request for strengthening the membership and performance improvements in the research area. Linking up security and association with the other ones group protection sentiments and work environment dependence sentiments prevail. It was concluded that: (1) Those satisfied ones declare that they are safe, protected and with good self-esteem doing their teaching work. The ones dissatisfied with the non-group belonging demonstrate dissatisfaction with the self-realization and expose their difficulties with social ties and the lack of integration in the teaching work. (3) The degree of motivation satisfactory is related to the satisfaction of basic needs, located in the first steps of Maslow's hierarchy. Accepting that the changes in higher education require motivated behaviors, the question arises: how can the motivation in teaching work be possible if the majority of organizations do not find a way to deal with the person of the Professor? Finally, the context of work is predominant in the provision of job satisfaction in teaching.

Hardware implementation of type-2 programmable fuzzifier

The design of full programmable type-2 membership function circuit is presented in this paper. This circuit is used to implement the fuzzifier block of Type-2 Fuzzy Logic Controller chip. In this paper the type-2 fuzzy set was obtained by blurring the width of the type-1 fuzzy set. This circuit allows programming the height and the shape of the membership function. It operates in current mode, with supply voltage of 3.3V. The simulation results of interval type-2 membership function circuit have been done in CMOS 0.35μm technology using Mentor Graphics software. © 2011 IEEE.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Improving the stability conditions of TS fuzzy systems with fuzzy Lyapunov functions

In this paper, the fuzzy Lyapunov function approach is considered for stabilizing continuous-time Takagi-Sugeno fuzzy systems. Previous linear matrix inequality (LMI) stability conditions are relaxed by exploring further the properties of the time derivatives of premise membership functions and by introducing a slack LMI variable into the problem formulation. The stability results are thus used in the state feedback design which is also solved in terms of LMIs. Numerical examples illustrate the efficiency of the new stabilizing conditions presented. © 2011 IFAC.

Application of an iterative method and an evolutionary algorithm in fuzzy optimization

This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain. © 2012 Brazilian Operations Research Society.