Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM

2022 ◽  
pp. 329-339
Author(s):  
Raja Das ◽  
Mohan Kumar Pradhan
Keyword(s):  
Complex Geometry ◽  
Electrical Discharge Machining ◽  
Cutting Tools ◽  
Back Propagation ◽  
Training Algorithms ◽  
Radial Overcut ◽  
Levenberg Marquardt ◽  
Resilient Back Propagation ◽  
Improved Performance ◽  
Traditional Manufacturing

This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.

Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM

2018 ◽  
pp. 140-150
Author(s):  
Raja Das ◽  
Mohan Kumar Pradhan
Keyword(s):  
Complex Geometry ◽  
Electrical Discharge Machining ◽  
Cutting Tools ◽  
Back Propagation ◽  
Training Algorithms ◽  
Radial Overcut ◽  
Levenberg Marquardt ◽  
Resilient Back Propagation ◽  
Improved Performance ◽  
Traditional Manufacturing

This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.

scholarly journals Using Feed Forward Neural Network to Solve Eigenvalue Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Luma N. M. Tawfiq ◽  
Othman M. Salih
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eigenvalue Problems ◽  
Back Propagation ◽  
Training Algorithms ◽  
Parallel Processor ◽  
Feed Forward Neural Network ◽  
Levenberg Marquardt ◽  
Quasi Newton

The aim of this paper is to presents a parallel processor technique for solving eigenvalue problem for ordinary differential equations using artificial neural networks. The proposed network is trained by back propagation with different training algorithms quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. The next objective of this paper was to compare the performance of aforementioned algorithms with regard to predicting ability.

Evaluating the Performance of Training Algorithms in Active Noise Control Using MLP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 1613-1617
Author(s):  
Mehrshad Salmasi ◽  
Homayoun Mahdavi-Nasab
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Noise Control ◽  
Active Noise Control ◽  
Back Propagation ◽  
Training Algorithms ◽  
Mlp Neural Network ◽  
Scaled Conjugate Gradient ◽  
Active Noise ◽  
Levenberg Marquardt

Passive methods are costly and ineffective in noise reduction at low frequencies. Active noise control has been suggested because of these problems. Active noise control (ANC) is based on the destructive interference between the noise source waves and a controlled secondary source. In this paper, various training algorithms are compared in active cancellation of modeled sound noise using MLP neural network. Colored noise signals are used as a model of sound noise instead of noise signals from databases. An MLP neural network with different architectures is used in simulation procedure. The effect of number of neurons on the convergence speed of various training algorithms is investigated in this paper. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), BFGS quasi-Newton (BFG), resilient back-propagation (RP) and variable learning rate back-propagation (GDX) are used for training the network. Simulation results show that Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) are the fastest training algorithms.

scholarly journals Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2322 ◽  
Author(s):  
Yi-Chun Du ◽  
Alphin Stephanus
Keyword(s):  
Arteriovenous Fistula ◽  
Back Propagation ◽  
Potential Contribution ◽  
Training Algorithms ◽  
Marquardt Algorithm ◽  
Significant Difference ◽  
Levenberg Marquardt ◽  
Before And After ◽  
Medical Diagnostic ◽  
Arteriovenous Fistula Stenosis

This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 3081-3085 ◽  
Author(s):  
Xin Ying Miao ◽  
Jin Kui Chu ◽  
Jing Qiao ◽  
Ling Han Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Field Data ◽  
Back Propagation ◽  
Earth Dam ◽  
Earth Dams ◽  
Gradient Descent Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm ◽  
Good Agreement

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

Simulation and prediction of optical characterization of casting (Adamantan-Fulgide) thin films using (CTANNs) approach

2019 ◽  
Vol 33 (11) ◽  
pp. 1950093 ◽  
Author(s):  
A. M. A. EL-Barry ◽  
D. M. Habashy
Keyword(s):  
Experimental Data ◽  
Thin Films ◽  
Back Propagation ◽  
Optical Characterization ◽  
Experimental Information ◽  
Training Algorithm ◽  
Energy Gaps ◽  
Resilient Back Propagation ◽  
Good Agreement

For reinforcement, the photochromic field and the cooperation between the theoretical and experimental branches of physics, the computational, theoretical artificial neural networks (CTANNs) and the resilient back propagation (R[Formula: see text]) training algorithm were used to model optical characterizations of casting (Admantan-Fulgide) thin films with different concentrations. The simulated values of ANN are in good agreement with the experimental data. The model was also used to predict values, which were not included in the training. The high precision of the model has been constructed. Moreover, the concentration dependence of both the energy gaps and Urbach’s tail were, also tested. The capability of the technique to simulate the experimental information with best accuracy and the foretelling of some concentrations which is not involved in the experimental data recommends it to dominate the modeling technique in casting (Admantan-Fulgide) thin films.

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