Modeling Connectionist Neuro-Fuzzy network and Applications

Shalinie, S. Mercy

doi:10.1007/s00521-004-0452-x

Cited by 5 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the cycloid pin wheel backlash is mainly proposed. Factors of influencing RV reducer backlash are listed in Table 1 [2,8]. ∆ is respectively backlash of cycloid curve modification, pin centre hole error, wheel pin radius error, wheel pin and hole interval, cycloid wheel circular runout error, position error, accumulate error of cycloid wheel circular pitch and modification and eccentricity error.…”

Section: Factors Of Influencing Rv Reducer Backlashmentioning

confidence: 99%

RV Reducer Backlash Analysis Based on Robust Design

2011

AMM

View full text Add to dashboard Cite

The RV reducer backlash is well controlled by means of robust design. Some factors influencing backlash are analyzed and divided into controllable factors and uncontrollable (noise) factors. The requirements for experimental factors are met for robust design. The experiment analysis is proposed by using orthogonal experiment and G.Taguchi robust design for each of effecting backlash factors. The optimum parameters are obtained for maximum signal noise ratio (SNR). The contribution rate for each factor influencing backlash is presented. Finally, RV reducer’s backlash is well decreased and achieved ideal result.

show abstract

Section: Factors Of Influencing Rv Reducer Backlashmentioning

confidence: 99%

RV Reducer Backlash Analysis Based on Robust Design

2011

AMM

View full text Add to dashboard Cite

show abstract

“…Tsakonas and Dounias [32] proposed a combination of chaos analysis, neuro-fuzzy systems and evolutionary training for stock exchange daily trading. Shalinie [33] used a neuro-fuzzy for three benchmark applications.…”

Section: An Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system

Mellit¹,

Kalogirou²,

Shaari³

et al. 2008

Renewable Energy

139

View full text Add to dashboard Cite

In this paper, a suitable adaptive neuro-fuzzy inference system (ANFIS) model is presented for estimating sequences of mean monthly clearness index (K t ) and total solar radiation data in isolated sites based on geographical coordinates. The magnitude of solar radiation is the most important parameter for sizing photovoltaic (PV) systems. The ANFIS model is trained by using a multi-layer perceptron (MLP) based on fuzzy logic (FL) rules. The inputs of the ANFIS are the latitude, longitude, and altitude, while the outputs are the 12-values of mean monthly clearness indexK t . These data have been collected from 60 locations in Algeria. The results show that the performance of the proposed approach in the prediction of mean monthly clearness indexK t is favorably compared to the measured values. The root mean square error (RMSE) between measured and estimated values varies between 0.0215 and 0.0235 and the mean absolute percentage error (MAPE) is less than 2.2%. In addition, a comparison between the results obtained by the ANFIS model and artificial neural network (ANN) models, is presented in order to show the advantage of the proposed method. An example for sizing a stand-alone PV system is also presented. This technique has been applied to Algerian locations, but it can be generalized for any geographical position. It can also be used for estimating other meteorological parameters such as temperature, humidity and wind speed. r

show abstract

“…In 1993, a hybrid ANFIS algorithm based on the Sugeno system, which was improved by Jang, was used on acquiring optimal output data in the study [11]. The algorithm, which consists of the least-squares method optimizing the consequent parameters and the back-propagation algorithm in relation to fuzzy sets, was employed to arrange the premise parameters [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Online Forecast of Fatigue Life in Welded Grider of Bridge Crane

Liu

Huang

et al. 2008

AMR

View full text Add to dashboard Cite

It is very important to forecast the fatigue crack development upon welded girder of bridge crane during use. When it happens, it will have a lot of clout with performance of enterprises. For security purpose in use, ANFIS model is built to predict the length of the fatigue crack extension about welded girder of bridge crane. Membership function is adjusted according to inputs and outputs of fuzzy reasoning, and appropriate fuzzy regulation numbers are chosen. The model which is closest to the real development of fatigue crack is built with the help of the If-Then rules. According to the result from the fatigue experiment of the welded girder of bridge crane, ANFIS model, ANN model and Three Order Spline Interpolation model are respectively built. After analysis and verification, it is obvious that ANFIS model is more accurate and feasible.

show abstract

Modeling Connectionist Neuro-Fuzzy network and Applications

Cited by 5 publications

References 17 publications

RV Reducer Backlash Analysis Based on Robust Design

RV Reducer Backlash Analysis Based on Robust Design

Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system

Online Forecast of Fatigue Life in Welded Grider of Bridge Crane

Contact Info

Product

Resources

About