ANN and ANFIS performance prediction models for hydraulic impact hammers

Iphar, Melih

doi:10.1016/j.tust.2011.06.004

Cited by 51 publications

(15 citation statements)

References 30 publications

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“…All the nodes of a lower layer are connected to all the nodes of an upper layer through links called weights. For MLP, the procedure used to perform the learning process is called the learning algorithm, the function of which is to modify the synaptic weights of the network in an orderly fashion to attain a desired design objective [46]. The BP algorithm, a generalized steepest descent algorithm, is the most popular learning technique used to train the MLP.…”

Section: Mlpmentioning

confidence: 99%

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Wei

2017

Energies

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Seasonal typhoons provide energy into the wave field in summer and autumn in Taiwan. Typhoons lead to abundant wave energy near the coastal area and cause storm surges that can destroy offshore facilities. The potential for wave energy can be obtained from analyzing the wave height. To develop an effective model for predicting typhoon-induced wave height near coastal areas, this study employed various popular data mining models-namely k-nearest neighbors (kNN), linear regressions (LR), model trees (M5), multilayer perceptron (MLP) neural network, and support vector regression (SVR) algorithms-as forecasting techniques. The principal component analysis (PCA) was then performed to reduce the potential variables from the original data at the first stage of data preprocessing. The experimental site was the Longdong buoy off the northeastern coast of Taiwan. Data on typhoons that occurred during 2002-2011 and 2012-2013 were collected for training and testing, respectively. This study designed four PCA cases, namely EV1, TV90, TV95, and ORI: EV1 used eigenvalues higher than 1.0 as principal components; TV90 and TV95 used the total variance percentages of 90% and 95%, respectively; and ORI used the original data. The forecast horizons varying from 1 h to 6 h were evaluated. The results show that (1) in the PCA model' cases, when the number of attributes decreases, computing time decreases and prediction error increases; (2) regarding classified wave heights, M5 provides excellent outcomes at the small wavelet wavelet level; MLP has favorable outcomes at the large wavelet and small/moderate wave levels; meanwhile, SVR gives optimal outcomes at the long wave and high/very high wave levels; and (3) for performance of lead times, MLP and SVR achieve more favorable relative weighted performance without consideration of computational complexity; however, MLP and SVR might obtain lower performance when computational complexity is considered.

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Section: Mlpmentioning

confidence: 99%

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Wei

2017

Energies

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“…This method has become a popular tool in rock and soil engineering as well as engineering geology in recent years (Gokceoglu et al, 2004;Yilmaz and Yuksek, 2009;Kucuck et al, 2011;Iphar, 2012). The purpose of ANFIS is to find a model which can properly associate the input parameters with the objective parameter.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Salimi

Rostami

Moormann

et al. 2016

Tunnelling and Underground Space Technology

144

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“…The fuzzy inference system (FIS) is a knowledge representation, in that each fuzzy rule describes a local behavior of the system. ANFIS has been successfully applied to predict water quality index (Sahu et al 2011), stock market return (Abbasi and Abouec 2008;Boyacioglu and Avci 2010), crop yield (Naderloo et al 2012), compressive strength of concrete and geopolymers (Sobhani et al 2010;Nazari and Khalaj 2012), impact hammer performance (Iphar 2012), soil erosion estimation (Akbarzadeh et al 2009), heat transfer and fluid flow characteristics in heat exchangers (Mehrabi et al 2011), landslide susceptibility analysis to produce their maps (Pradhan et al 2010) and photovoltaic power supply system (Mellit and Kalogirou 2011) etc. The objective of the present work is to predict CPT, which is taken as a measure of spontaneous heating tendency of coals in India using ANFIS, based on laboratory tests.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Spontaneous Heating Susceptibility of Indian Coals Using Neuro Fuzzy System

Sahu

Mahapatra

2013

Geotech Geol Eng

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Coal mine fires due to spontaneous heating have been a great concern both for the industry and researchers worldwide. They start in a very small scale and gradually expand in size resulting in environmental, safety and economic concerns. Most of these fires could be averted if suitable preventive measures are taken. Since the spontaneous heating potential of all types of coals are not the same, its accurate forecasting is essential to plan efficient preventive measures, production schedule and storage capabilities of a mine. This study presents a neuro-fuzzy approach for forecasting the auto-oxidation of coals based on laboratory test results on fifty cone coal samples collected from different coalfields of India. Using the constituents of proximate analysis as input parameters, crossing point temperature (CPT) is predicted using adaptive neurofuzzy inference system (ANFIS) model. Gaussian membership function has been adopted for describing the input variables. The results indicate that ANFIS model can predict CPT values with reasonable accuracy.

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ANN and ANFIS performance prediction models for hydraulic impact hammers

Cited by 51 publications

References 30 publications

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Forecasting Spontaneous Heating Susceptibility of Indian Coals Using Neuro Fuzzy System

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