Fuzzy层次神经网络

本文提出和归纳了Ｆｕｚｚｙ前馈神经网络模型，并提出了Ｆｕｚｚｙ前馈神经网络的反向传播学习算法，模拟结果表明利用ＦｕｚｚｙＢＰ学习算法训练的Ｆｕｚｚｙ前馈神经网络具有较好的非逻辑归纳能力和Ｆｕｚｚｙ规则表达能力。

Postharvest porometer to study transpiration and to measure vapor pressure deficit.

O pôrometro de pós-colheita é um instrumento para medir transpiração, resistência difusiva e déficit de pressão de vapor por manometria a volume constante e por volumetria a pressão constante. É constituído de uma câmara de transpiração com fechamento hermético contendo um suporte de amostras sobre um ventilador e externamente um manômetro de coluna de água com pipeta graduada móvel. Sob temperatura constante, o déficit de pressão de vapor (.P), e o volume de vapor de água (.V) foram proporcionais aos volumes de água vaporizados. Com o uso de um excesso de água este aumento da pressão de vapor iguala-se, em módulo, ao déficit de pressão de vapor do ar na câmara de transpiração. Para amostras do ar atmosférico no laboratório, o déficit de pressão de vapor foi calculado a partir das temperaturas de bulbo seco e úmido em um psicrômetro ventilado e por manometria. A correlação entre estes dois métodos foi de 0,976. A acurácia das medidas de transpiração é tal que o produto do volume morto da câmara (V) pela declividade (.P/ .V) determinada pelo vapor de água liberado no processo iguala-se à pressão barométrica, com erro inferior a 1%. Um exemplo experimental do uso do porômetro de pós-colheita em cenoura é apresentado juntamente e os detalhes para obter a resistência difusiva e espessura da camada laminar. O porômetro de pós-colheita é um porômetro de difusão simples, robusto, que poderá ser usado em estudos de efeito de cêras, na seleção de cultivares e em variados outros estudos de fisiologia de pós-colheita.

Anthropometry of the Breast Region: How to Measure?

Breast region measurements are important for research, but they may also become significant in the legal field as a quantitative tool for preoperative and postoperative evaluation. Direct anthropometric measurements can be taken in clinical practice. the aim of this study was to compare direct breast anthropometric measurements taken with a tape measure and a compass.Forty women, aged 18-60 years, were evaluated. They had 14 anatomical landmarks marked on the breast region and arms. the union of these points formed eight linear segments and one angle for each side of the body. the volunteers were evaluated by direct anthropometry in a standardized way, using a tape measure and a compass.Differences were found between the tape measure and the compass measurements for all segments analyzed (p > 0.05).Measurements obtained by tape measure and compass are not identical. Therefore, once the measurement tool is chosen, it should be used for the pre- and postoperative measurements in a standardized way.This journal requires that authors assign a level of evidence to each article. for a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.

Towards Fuzzy Compositional Modelling

X. Fu, Q. Shen and R. Zhao. 'Towards fuzzy compositional modelling,' In Proceedings of the 16th International Conference on Fuzzy Systems, 2007, pp. 1233-1238. Sponsorship: EPSRC

Knowledge representation for fuzzy model composition

X. Fu and Q. Shen. 'Knowledge representation for fuzzy model composition', in Proceedings of the 21st International Workshop on Qualitative Reasoning, 2007, pp. 47-54. Sponsorship: EPSRC

Fuzzy-Rough Data Reduction with Ant Colony Optimization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Data Reduction with Ant Colony Optimization,' Fuzzy Sets and Systems, vol. 149, no. 1, pp. 5-20, 2005.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring.

Q. Shen and R. Jensen, 'Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring,' Pattern Recognition, vol. 37, no. 7, pp. 1351-1363, 2004.

Survey of Rough and Fuzzy Hybridization.

P. Lingras and R. Jensen, 'Survey of Rough and Fuzzy Hybridization,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 125-130, 2007.

Tolerance-based and Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007.

Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection,' Proceedings of the Fifth International Conference on Rough Sets and Current Trends in Computing (RSCTC 2006), LNAI 4259, pp. 147-156, 2006.

Fuzzy interpolative reasoning via scale and move transformation

Z. Huang and Q. Shen. Fuzzy interpolative reasoning via scale and move transformation. IEEE Transactions on Fuzzy Systems, 14(2):340-359.

Data-driven fuzzy rule generation and its application for student academic performance evaluation

K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006.