Thermal Error Modeling of Feed Axis in Machine Tools Using Particle Swarm Optimization-Based Generalized Regression Neural Network

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
Guolong Li ◽  
Hao Ke ◽  
Chuanzhen Li ◽  
Biao Li
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Machine Tools ◽  
Particle Swarm ◽  
Thermal Error ◽  
Generalized Regression Neural Network ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Generalized Regression ◽  
Feed Axis

Abstract This paper demonstrates the development of a thermal error model that is applied on the feed axis of machine tools and based on the neural network. This model can accurately predict the value of the axial thermal error that appears on machine feed axis. In principle, there is the generalized regression neural network (GRNN), which has the good nonlinear mapping ability and serves to construct the error model. About variables, the data of temperature and axial thermal error of machine feed axis are the inputs and outputs, respectively. The particle swarm optimization (PSO) is a component of this model, which serves to optimize the smoothing factor in GRNN, and the particle swarm optimization-based generalized regression neural network (PSO-GRNN) model is built. From experiment, the datum is acquired from a machining centre in four different feed rates. Thereafter, the back propagation (BP) neural network model, the traditional GRNN model, and the PSO-GRNN model were established, and the data collected from the experimentation are input in three models for prediction. Compared with the other two models used in this paper, the PSO-GRNN model can maintain higher prediction accuracy at different feed speed, and the prediction accuracy of it changes less in different feed rates. The proposed model solved the problem of generalization ability of the neural network at different feed rate, which shows good performance and lays a good foundation for further research like thermal error compensation.

Modeling and Optimisation of Xylose Production by Enzymatic Hydrolysis using Neural Network and Particle Swarm Optimization

2015 ◽  
Vol 10 (3) ◽  
pp. 173-178 ◽  
Author(s):  
Nur Atiqah Nurhalim ◽  
Mashitah Mat Don ◽  
Zainal Ahmad ◽  
Dipesh S. Patle
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Enzymatic Hydrolysis ◽  
Particle Swarm ◽  
Agitation Speed ◽  
Process Conditions ◽  
Optimum Conditions ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Hydrolysis Process

Abstract Particle swarm optimization (PSO) method is used for the optimization of an enzymatic hydrolysis process for the production of xylose from rice straw. The enzymatic hydrolysis process conditions such as temperature, agitation speed and concentration of enzyme were optimized by using PSO to obtain the optimum yield of xylose. Data collected from an experimental design using response surface methodology were necessitated to develop the neural network modeling. The neural network model is used as a model in objective function of PSO to predict the optimum conditions, which involved in the enzymatic hydrolysis process. The optimum value is obtained from the performance of the best particle swarm among the optimum conditions in PSO. The predicted optimum values were validated through the experiment of the enzymatic hydrolysis process. The optimum temperature, agitation speed and xylanase concentration is observed to be 50.3°C, 132 rpm and 1.6474 mg/ml, respectively. The optimal yield of xylose is predicted as 0.1845 mg/ml using PSO.

scholarly journals SELEKSI FITUR ALGORITMA NEURAL NETWORK MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI KELAHIRAN PREMATUR

Kilat ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 106-111
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Preterm Birth ◽  
Sensitivity And Specificity ◽  
Particle Swarm ◽  
Data Set ◽  
Swarm Optimization ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

Premature birth, defined as delivery in pregnant women with gestation age 20 - 36 weeks. Research related to preterm birth has been done by the researchers by using the neural network method. However such research only showcase about the results of the sensitivity and specificity. The results of research using the method of neural network in predicting preterm birth has a value of the resulting accuracy is still less accurate and only limited to presenting the results of the sensitivity and specificity. In this study produced a model of the neural network algorithm and model of neural network algorithm based on particle swarm optimization to get the architecture in predicting preterm birth and gives a more accurate value for accuracy on a data set of RSUPN Cipto Mangunkusumo , RS Sumber Waras and in its entirety. After you are done testing with two models of neural network algorithms and neural network algorithm based on particle swarm optimization and the results obtained are the neural network algorithm generates value accuracy of 94,60%, 96,40%, 91,33%, and AUC values of 0,973, 0,982, 0,953, however, after the addition of the neural network algorithm based on particle swarm optimization value accuracy of 95,20%, 96,80%, 92,40% and AUC values of 0,979 , 0,987, 0,965. So both of these methods has the distinction of accuracy which amounted to 0.60%, 0.40%, 1.07% and AUC value difference of 0.006, 0.005, 0.012.

scholarly journals Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN

2021 ◽  
Author(s):  
Kaiqiang Ye ◽  
Jianbin Wang ◽  
Hong Gao ◽  
Liu Yang ◽  
Ping Xiao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Particle Swarm ◽  
Process Parameter ◽  
Optimal Process ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Cp Ti ◽  
Optimal Process Parameter

Abstract This work aims to improve the surface quality of commercially pure titanium (CP-Ti) with free alumina lapping fluid and establish the relationship between the main process parameters of lapping and roughness. On this basis, the optimal process parameters were searched by performing particle swarm optimization with mutation. First, free alumina lapping fluid was used to perform an L9(33) orthogonal experiment on CP-Ti to acquire data samples to train the neural network. At the same time, a BP neural network was created to fit the nonlinear functional relation among the lapping pressure P, spindle speed n, slurry flow Q and roughness Ra. Then, the range of the node numbers in the hidden layer of the neural network was determined by empirical formulas and the Kolmogorov theorem. On this basis, particle swarm optimization with mutation was used to search for the optimal process parameter configurations for lapping CP-Ti. The optimal process parameter configurations were used in the neural network to calculate the prediction value. Finally, the accuracy of the prediction was verified experimentally. The optimum process parameter configurations found by particle swarm optimization were as follows: the lapping pressure was 5 kPa, spindle speed was 60 r·min− 1 and slurry flow was 50 ml·min− 1. Then, the configurations were applied to a neural network to simulate prediction: the roughness was 0.1127 µm. The roughness obtained by experiments was 0.1134 µm. The error was 0.62%, which indicates that the well-trained neural network can achieve a good prediction when experimental data are missing. Applying the particle swarm optimization (PSO) algorithm with mutation to a neural network will obtain the optimal process parameter configurations, which can effectively improve the surface quality of CP-Ti lapped with free abrasive.

Optimization Algorithms

2014 ◽  
pp. 182-199 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Learning Enhancement ◽  
Swarm Optimization ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Intelligent Behavior ◽  
Definition Of

In this chapter, the basic definition of Genetic Algorithm (GA) and some of the main operations applied in GA are explained. In addition, Swarm Intelligence (SI) is briefly explained as the new branch of intelligent behavior of nature phenomena. Although PSO has been explained in past chapters, this chapter explains PSO in detail and an example of the way PSO works is provided for better understanding. Some of the differences of Particle Swarm Optimization (PSO) and GA are provided and readers will learn how to use GA and PSO for training the neural network. The experiments and contents in this chapter are from the study by Nuzly (2006) in her thesis entitled “Particle Swarm Optimization for Neural Network Learning Enhancement”.

scholarly journals Determination of Eligibility Standards for Teacher Certification Using the Particle Swarm Optimization (PSO) Method and Neural Network Classification Algorithm (NN)

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ahmad bahtiar Bahtiar Efendi ◽  
Agus Alwi Mashuri
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neural Network Method ◽  
National Education ◽  
Regulatory Policies ◽  
The Neural Network ◽  
Network Method ◽  
The Government

To improve the quality of national education, the government through the Ministry of Education issued a certification policy. This is of course attractive for the community to be part of this program, many of whom choose to become teachers, even though they are not from higher education based education. One of the factors that attracts it is the allowances that will be obtained for teachers who have passed the certification exam. The government, through the Teacher Law, issues regulatory policies which later can be used as the basis for determining the eligibility of teachers as professionals, so that their profession is entitled to an allowance. However, conditions in the field were found that some teachers were not yet eligible to hold certification, because not a few scored below the standard Teacher Compotency Test (UKG). Therefore, in this study a system is proposed to be built using the Neural Network method and optimized with the Particle Swarm Otimation algorithm, to determine the feasibility of giving certification so that similar cases do not happen again. This study provides an overview that not all certified teachers deserve this predicate. The application of the Neural Network method which is optimized with the Particle Swarm Optimization algorithm, provides a higher accuracy with an accuracy rate of 99.70% compared to the neural network algorithm model of 99.60%.

The Research of Quantum-Behaved Particle Swarm Optimization in Gear Steel Hardenability Prediction

2014 ◽  
Vol 912-914 ◽  
pp. 479-482
Author(s):  
Yu Ping Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Optimization Algorithm ◽  
Model Simulation ◽  
Particle Swarm ◽  
Rolling Process ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Gear Steel

Problems of uncertainty for gear steel hardenability control in rolling process, this article will applied improved Quantum-behaved Particle Swarm Optimization algorithm to the uncertainty, using the optimization algorithm to train the neural network by improving quantum groups, build optimized gear steel quenching permeability control neural network model. Simulation results show that this algorithm is an effective solution to the problem of gear steel hardenability predictive control. Keywords: Quantum-behaved Particle Swarm Optimization, gear steel, Hardenability

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