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A Learning System Prediction Method Using Fuzzy Regression

A Learning System Prediction Method Using Fuzzy Regression

... by fuzzy regression analysis in multivariate ...mainly fuzzy linear functions though literatures have suggested that the concept could be extended to non-linear and intrinsically linear function ... See full document

4

Forecasting Short Term Electricity Price Using Artificial Neural Network and Fuzzy Regression

Forecasting Short Term Electricity Price Using Artificial Neural Network and Fuzzy Regression

... 288 www.hrmars.com/journals be trained, this training process is the procedure to obtain the weights of each connection and the neurons threshold value. There are many training algorithms were developed, including the ... See full document

8

Optimization of Multiple Responses in the Taguchi Method Using Desirability Function and Fuzzy Regression

Optimization of Multiple Responses in the Taguchi Method Using Desirability Function and Fuzzy Regression

... inherent system fuzziness; such as, fluctuation of process pressure and temperature due to environmental ...Taguchi method using fuzzy regression ... See full document

6

Sistema fuzzy para a predição da temperatura cloacal de frangos de corte

Sistema fuzzy para a predição da temperatura cloacal de frangos de corte

... its prediction can be used as decision support to turn on acclimatization ...a system using the fuzzy set theory for CT prediction of broiler ...The fuzzy system was ... See full document

6

Momentum Analysis based Stock Market Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Momentum Analysis based Stock Market Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS)

... Prediction of stock market returns is an important issue in ...as Regression models and ARIMA models [2] are also used for stock price ...market prediction during the last ... See full document

6

Neuro-fuzzy modeling in bankruptcy prediction

Neuro-fuzzy modeling in bankruptcy prediction

... of using expert systems for bankruptcy prediction was the one of Messier and Hansen ...'data-driven' method was to take firms of known classes (bankrupt/non-bankrupt) described by a fixed set of ... See full document

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AADT prediction using support vector regression with data-dependent parameters

AADT prediction using support vector regression with data-dependent parameters

... the system, which means that it is important that the AADT should be accurately ...for regression (SVR) to forecast AADT is presented. The proposed method- ology computes the SVR prediction ... See full document

8

Arq. NeuroPsiquiatr.  vol.66 número2A

Arq. NeuroPsiquiatr. vol.66 número2A

... different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic ...events. Method: A neuro-fuzzy system was developed using the NEFCLASS ... See full document

5

PREDICTING KAPPA NUMBER IN A KRAFT PULP CONTINUOUS DIGESTER: A COMPARISON OF FORECASTING METHODS

PREDICTING KAPPA NUMBER IN A KRAFT PULP CONTINUOUS DIGESTER: A COMPARISON OF FORECASTING METHODS

... number using different data-driven ...number prediction model and fault diagnostics of continuous digesters using clustering ...hybrid system in the monitoring of the process and the kappa ... See full document

14

Yarn Strength Modelling Using Fuzzy Expert System

Yarn Strength Modelling Using Fuzzy Expert System

... the prediction accuracy of mathematical models is not very encouraging due to the assumptions or simplifications used while building these ...Statistical regression models proposed by Hafez [6], Hunter [7], ... See full document

8

A Fuzzy Newton-Cotes method For Integration of Fuzzy Functions

A Fuzzy Newton-Cotes method For Integration of Fuzzy Functions

... numerical method for integration of fuzzy functions is considered. Fuzzy Newton-Cotes formula, such as fuzzy trapezoidal method and fuzzy Simpson method are calculated by ... See full document

8

J. Braz. Chem. Soc.  vol.22 número8

J. Braz. Chem. Soc. vol.22 número8

... of system parameters, such as MF (membership function) parameters, linear coeficients, and so ...by using RMSE as a criterion for the test ...produced using MATLAB 7.0 Fuzzy Logic Toolbox ... See full document

7

Using Recommendation System for E-learning Environments at degree level

Using Recommendation System for E-learning Environments at degree level

... of learning objects, such as practices or ...the learning objects are classified by categories, he decides to begin with the basic ...those learning objects related with an specific ... See full document

4

Prediction of pKa values using the PM6 semiempirical method

Prediction of pKa values using the PM6 semiempirical method

... the method systematically underestimates the pKa of secondary and tertiary aliphatic amines by ca 1 and 2 pH units, respectively, so an additional empirical correction is added for these two molecule ...types. ... See full document

16

Prediction Based Object Recovery Using Sequential Monte Carlo Method

Prediction Based Object Recovery Using Sequential Monte Carlo Method

... a method known as SMCM to localize the missing object, when the object is not found by the sensor nodes during object tracking and at last we compare the simulated results of SMCM with the multilatertion and ... See full document

8

Packet Classification using Support Vector Machines with String Kernels

Packet Classification using Support Vector Machines with String Kernels

... A kernel is a function that enables the support vector machine to linearly separate the data in a higher-dimensional space . Using a kernel function is similar to adding a trivial feature to the already existing ... See full document

5

Using the Comfortability-in-Learning Scale to Enhance Positive Classroom Learning Environments

Using the Comfortability-in-Learning Scale to Enhance Positive Classroom Learning Environments

... st udy w it h m ult iple pr ogr am s m ay r ev eal differences in how pr ogr am s v iew lear ning env ir onm ent s as a m eans t o incr ease lear ning. I t w ould also be int er est ing t o invest igat e how inst r ... See full document

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Dissolved oxygen prediction using a possibility-theory based fuzzy neural network

Dissolved oxygen prediction using a possibility-theory based fuzzy neural network

... Following previous research for this river, two abiotic inputs (daily mean water tem- perature, T and daily mean flow rate, Q) will be used to predict daily minimum DO. An advantage of using these factors is that ... See full document

66

Numerical Solution of PDE’s Using Deep Learning

Numerical Solution of PDE’s Using Deep Learning

... solutions, which is the usual form to obtain a solution without explicitly considering the phenomenon called wavebreaking, in which past a certain point the peak of the wave moves faster, creating a multiple valued ... See full document

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ANFIS Modelling of Flexible Plate Structure

ANFIS Modelling of Flexible Plate Structure

... An ANFIS model characterizing a flexible plate structure has been presented and discussed. The best feature of ANFIS is that it pre-processes all the data into several membership functions before mapping the data into an ... See full document

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