A Fuzzy ARTMAP Nonparametric Probability Estimator For Nonstationary Pattern Recognition Problem

An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is introduced. In slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In max-nodes mode, the network initially learns a fixed number of categories, and weights are then adjusted gradually.

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.

Uma abordagem da lógica fuzzy no ensino médio

O uso cada vez mais intenso de conceitos subjetivos - provenientes da lógica fuzzy - aplicados a problemas reais nos motivou a desenvolver procedimentos para a introdução destas idéias no âmbito do ensino médio. O projeto propõe inicialmente o estudo de conjuntos fuzzy que podem ser entendidos com exemplos - de variação populacional, de controle de pragas e de epidemias. Posteriormente, usar as “operações fuzzy” Sup e Inf em produtos de matrizes para realizar diagnósticos e avaliações subjetivas. As situações abordadas já estão na literatura (Barros e Bassanezi, 2006), entretanto não como fonte para o Ensino Médio. Um dos objetivos principais deste trabalho é contrapor a crença de exatidão da matemática clássica com os resultados provenientes de lógica subjetiva, utilizando conceitos apropriados para os estudantes destas séries: teoria dos conjuntos, relações e funções, matrizes, equações de diferenças e outros.

In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations.

Characterization of a thymidylate synthase (TS)-inducible cell line: a model system for studying sensitivity to TS- and non-TS-targeted chemotherapies

Thymidylate synthase (TS) is responsible for the de novo synthesis of thymidylate, which is required for DNA synthesis and repair and which is an important target for fluoropyrimidines such as 5-fluorouracil (5-FU), and antifolates such as Tomudex (TDX), ZD9331, and multitargeted antifolate (MTA). To study the importance of TS expression in determining resistance to these agents, we have developed an MDA435 breast cancer-derived cell line with tetracycline-regulated expression of TS termed MTS-5. We have demonstrated that inducible expression of TS increased the IC(50) dose of the TS-targeted therapeutic agents 5-FU, TDX, and ZD9331 by 2-, 9- and 24-fold respectively. An IC(50) dose for MTA was unobtainable when TS was overexpressed in these cells, which indicated that MTA toxicity is highly sensitive to increased TS expression levels. The growth inhibitory effects of the chemotherapeutic agents CPT-11, cisplatin, oxaliplatin, and Taxol were unaffected by TS up-regulation. Cell cycle analyses revealed that IC(50) doses of 5-FU, TDX and MTA caused an S-phase arrest in cells that did not overexpress TS, and this arrest was overcome when TS was up-regulated. Furthermore, the S-phase arrest was accompanied by 2- to 4-fold increased expression of the cell cycle regulatory genes cyclin E, cyclin A, and cyclin dependent kinase 2 (cdk2). These results indicate that acute increases in TS expression levels play a key role in determining cellular sensitivity to TS-directed chemotherapeutic drugs by modulating the degree of S-phase arrest caused by these agents. Moreover, CPT-11, cisplatin, oxaliplatin, and Taxol remain highly cytotoxic in cells that overexpress TS.

Fuzzy Coding of Genetic Algorithms

A well-cited paper suggesting fuzzy coding as an alternative to the conventional binary, grey and floating-point representations used in genetic algorithms.

An Investigation of Fuzzy Multiple Heuristic Orderings in the Construction of University Examination Timetables

In this paper, we present an investigation into using fuzzy methodologies to guide the construction of high quality feasible examination timetabling solutions. The provision of automated solutions to the examination timetabling problem is achieved through a combination of construction and improvement. The enhancement of solutions through the use of techniques such as metaheuristics is, in some cases, dependent on the quality of the solution obtained during the construction process. With a few notable exceptions, recent research has concentrated on the improvement of solutions as opposed to focusing on investigating the ‘best’ approaches to the construction phase. Addressing this issue, our approach is based on combining multiple criteria in deciding on how the construction phase should proceed. Fuzzy methods were used to combine three single construction heuristics into three different pair wise combinations of heuristics in order to guide the order in which exams were selected to be inserted into the timetable solution. In order to investigate the approach, we compared the performance of the various heuristic approaches with respect to a number of important criteria (overall cost penalty, number of skipped exams, number of iterations of a rescheduling procedure required and computational time) on twelve well-known benchmark problems. We demonstrate that the fuzzy combination of heuristics allows high quality solutions to be constructed. On one of the twelve problems we obtained lower penalty than any previously published constructive method and for all twelve we obtained lower penalty than when any of the single heuristics were used alone. Furthermore, we demonstrate that the fuzzy approach used less backtracking when constructing solutions than any of the single heuristics. We conclude that this novel fuzzy approach is a highly effective method for heuristically constructing solutions and, as such, has particular relevance to real-world situations in which the construction of feasible solutions is often a difficult task in its own right.