Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificial neural network

2021 ◽  
Author(s):  
Plaban Deb ◽  
Sujit Kumar Pal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nonlinear Regression ◽  
Load Sharing ◽  
Piled Raft ◽  
Interaction Behavior ◽  
Artificial Neural ◽  
Lm Algorithm

Prediction of Surface SO2 Concentration in Shanghai Using Artificial Neural Network

2014 ◽  
Vol 522-524 ◽  
pp. 44-47 ◽  
Author(s):  
Dan Xue ◽  
Qian Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Daily Precipitation ◽  
Correlation Coefficients ◽  
Ann Model ◽  
Sea Level Pressure ◽  
Neuron Number ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm

Air quality has been deteriorated seriously in Shanghai as a result of urbanization and modernization. A three-layer Artificial Neural Network (ANN) model was developed to forecast the surface SO2 concentration. The subsequent SO2 concentration being the output parameter of this study was estimated by six input parameters such as preceding SO2 concentrations, average daily temperature, sea-level pressure, relative humidity, average daily wind speed and average daily precipitation. Levenberg-Marquarde (LM) backpropagation was tested as the best algorithm and the optimal neuron number for the LM algorithm was found to be eight. ANN testing outputs were proven to be satisfactory with correlation coefficients of about 0.765.

BP Artificial Neural Network and its Application Based on LM Algorithm

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Luo Qiu ◽  
Dexian Zhang ◽  
Hao Huang ◽  
Qiming Xiong ◽  
Gui Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Artificial Neural Network ◽  
Artificial Neural ◽  
Lm Algorithm

scholarly journals Comparative investigation of artificial neural network learning algorithms for modeling solar still production

2015 ◽  
Vol 5 (4) ◽  
pp. 480-493 ◽  
Author(s):  
Ahmed F. Mashaly ◽  
A. A. Alazba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithms ◽  
Ambient Air ◽  
Feed Water ◽  
Solar Still ◽  
Neural Network Learning ◽  
Network Learning ◽  
Artificial Neural ◽  
Lm Algorithm

Three artificial neural network learning algorithms were utilized to forecast the productivity (MD) of a solar still operating in a hyper-arid environment. The learning algorithms were the Levenberg–Marquardt (LM), the conjugate gradient backpropagation with Fletcher–Reeves restarts, and the resilient backpropagation. The Julian day, ambient air temperature, relative humidity, wind speed, solar radiation, temperature of feed water, temperature of brine water, total dissolved solids (TDS) of feed water, and TDS of brine water were used in the input layer of the developed neural network model. The MD was located in the output layer. The developed model for each algorithm was trained, tested, and validated with experimental data obtained from field experimental work. Findings revealed the developed model could be utilized to predict the MD with excellent accuracy. The LM algorithm (with a minimum root mean squared error and a maximum overall index of model performance) was found to be the best in the training, testing, and validation stages. Relative errors in the predicted MD values of the developed model using the LM algorithm were mostly in the vicinity of ±10%. These results indicated that the LM algorithm is the most ideal and accurate algorithm for the prediction of the MD with the developed model.

scholarly journals Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions

2017 ◽  
Vol 12 (3) ◽  
pp. 155892501701200 ◽  
Author(s):  
Kenan Yıldirimm ◽  
Hamdi Ogut ◽  
Yusuf Ulcay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Nonlinear Regression ◽  
Fine Tuning ◽  
Draw Force ◽  
Non Linear ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Production Conditions

In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the nonlinear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R2=0.97 vs. R2=0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.

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