Modeling a Delayed Coking Process with GRNN and Double-Chain Based DNA Genetic Algorithm

2010 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiao Chen ◽  
Ning Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cross Validation ◽  
Training Data ◽  
Generalized Regression Neural Network ◽  
Genetic Operators ◽  
Double Chain ◽  
Computer Prediction ◽  
Delayed Coking ◽  
Smoothing Parameters

For characterization or optimization process, a computer prediction model is in demand. This paper describes an approach for modeling a delayed coking process using generalized regression neural network (GRNN) and a double-chain based DNA genetic algorithm (dc-DNAGA). In GRNN, the smoothing parameters have significant effect on the performance of the network. This paper presents an improved GA, dc-DNAGA, to optimize the smoothing parameters in GRNN. The dc-DNAGA is inspired by the biological DNA, where the smoothing parameters are coded in the double-chain chromosomes and modified genetic operators are employed to improve the global search ability of GA. To test the performance of the constructed model, it is used to predict the output of the test data which is not included in the training data. Compared with other reported methods, eight cross validation results show the advantage of the proposed technique that it predicts the new data more accurately.

scholarly journals Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

2017 ◽  
Vol 44 (11) ◽  
pp. 945-955 ◽  
Author(s):  
Mansour Fakhri ◽  
Ershad Amoosoltani ◽  
Mona Farhani ◽  
Amin Ahmadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
Crumb Rubber ◽  
Concrete Pavement ◽  
Genetic Operators ◽  
Efficiency Criterion ◽  
Roller Compacted Concrete

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

2020 ◽  
Vol 34 (3) ◽  
pp. 430-443
Author(s):  
A. Saravanan ◽  
J. Jerald ◽  
A. Delphin Carolina Rani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Manufacturing Cost ◽  
Genetic Operators ◽  
Neural Network Models ◽  
Data Set ◽  
The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

The Distribution of Teaching Staff in Colleges and Universities based on Neuropsychology

2021 ◽  
Vol 7 (5) ◽  
pp. 4682-4692
Author(s):  
Ruolin Yang ◽  
Dan Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Colleges And Universities ◽  
Quality Education ◽  
Generalized Regression Neural Network ◽  
Teaching Staff ◽  
Actual Situation ◽  
Teacher Resources ◽  
Calculation Process ◽  
Generalized Regression

Objectives: At present, quality education has gradually been recognized by the whole society, and a consensus has been reached on its importance, which has put forward stricter requirements for the distribution of faculty in universities. Methods: In this paper, based on neuropsychology, the distribution of teaching staff in colleges and universities was studied, and the model of talent evaluation and distribution was constructed. Results: Firstly, the generalized regression neural network was optimized by genetic algorithm. Then, the genetic algorithm’s generalized regression neural network calculation process was designed. Conclusion: Finally, with the example of teacher resources in a university, the algorithm in this paper was tested. The results show that the results of the generalized regression neural network optimized by genetic algorithm can match the actual situation very well, and the method is feasible with certain advantages.

Soil Moisture Retrieval Based on ASAR Data and Genetic Neural Networks

2012 ◽  
Vol 500 ◽  
pp. 198-203
Author(s):  
Chang Lin Xiao ◽  
Yan Chen ◽  
Lina Liu ◽  
Ling Tong ◽  
Ming Quan Jia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Soil Moisture ◽  
Training Data ◽  
Neural Network Training ◽  
Practical Test ◽  
Network Training

Genetic Algorithm can further optimize Neural Networks, and this optimized Algorithm has been used in many fields and made better results, but currently, it have not been used in inversion parameters. This paper used backscattering coefficients from ASAR, AIEM model to calculate data as neural network training data and through Genetic Algorithm Neural Networks to retrieve soil moisture. Finally compared with practical test and shows the validity and superiority of the Genetic Algorithm Neural Networks.

Research on the Genetic Algorithm Simulating Human Reproduction Mode and its Blending Application with Neural Network

2012 ◽  
Vol 532-533 ◽  
pp. 1785-1789
Author(s):  
Tai Shan Yan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Human Reproduction ◽  
Genetic Operators ◽  
Reproduction Mode ◽  
Evolutionary Neural Network ◽  
Selection Operator

In this study, a genetic algorithm simulating human reproduction mode (HRGA) is proposed. The genetic operators of HRGA include selection operator, help operator, crossover operator and mutation operator. The sex feature, age feature and consanguinity feature of genetic individuals are considered. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. Based on this genetic algorithm, an improved evolutionary neural network algorithm named HRGA-BP algorithm is formed. In HRGA-BP algorithm, HRGA is used firstly to evolve and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. Then, training samples are used to search for the optimal solution by the evolutionary neural network. HRGA-BP algorithm is used in motor fault diagnosis. The illustrational results show that HRGA-BP algorithm is better than traditional neural network algorithms in both speed and precision of convergence, and its validity in fault diagnosis is proved.

scholarly journals Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map

2018 ◽  
Vol 28 (2) ◽  
pp. 411-424 ◽  
Author(s):  
Serkan Kartal ◽  
Mustafa Oral ◽  
Buse Melis Ozyildirim
Keyword(s):  
Neural Network ◽  
Training Data ◽  
General Regression Neural Network ◽  
Training Dataset ◽  
Generalized Regression Neural Network ◽  
Self Organizing Map ◽  
Performance Loss ◽  
Pattern Layer ◽  
Benchmark Test Functions ◽  
Self Organizing

Abstract In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly, when a new test record is applied to the network, the most similar map units are detected, and the training data that have the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies the GRNN’s structure without any performance loss.

Vision Intelligence for Mobile Agro-Robotic System

1999 ◽  
Vol 11 (3) ◽  
pp. 193-199 ◽  
Author(s):  
Noboru Noguchi ◽  
◽  
John F. Reid ◽  
Qin Zhang ◽  
Lei Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Vision System ◽  
Field Studies ◽  
Growth Cycle ◽  
Training Data ◽  
Crop Height ◽  
Artificial Neural Network Ann

We developed an intelligent vision system for mobile robot field operations. Fuzzy logic was used to classify crops and weeds. A genetic algorithm (GA) was used to optimize and tune fuzzy logic membership rules. Field studies confirmed that our method accurately classified crops and weeds throughout their growth cycle. After separating out weeds, an artificial neural network (ANN) was used to estimate crop height and width. The r2 for estimating crop height was 0.92 for training data and 0.83 for test data. A geographic information system (GIS) was used to create a crop growth map.

scholarly journals Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network

2021 ◽  
Vol 5 (2) ◽  
pp. 72-82
Author(s):  
Zahrina Aulia Adriani ◽  
Irma Palupi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Standard Deviation ◽  
Student Performance ◽  
Cross Validation ◽  
Training Data ◽  
Tracer Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Fold Cross Validation

In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.

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