Correlation Analysis-Based Neural Network Self-Organizing Genetic Evolutionary Algorithm

Chai, Zenghao; Yang, Xu; Liu, Zhilin; Lei, Yunlin; Zheng, Wenhao; Ji, Mengyao; Zhao, Jinfeng

doi:10.1109/access.2019.2942035

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The topology of neural networks influences two of the most important evaluation criteria of neural network training: generalization and training time [28]. Improper topology of the neural network causes many redundancies, which makes the neural network fall into local optimization and considerably prolongs the training time [29]. Therefore, determining the optimal neural network topology is important.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Yang

Zheng

Vidyarthi

et al. 2023

Foods

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This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51–3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities.

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Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Yang

Zheng

Vidyarthi

et al. 2023

Foods

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“…Due to the approached separation process complexity and due to the necessity of the future implemented control systems (for controlling the 18 O isotope concentration) to gentableerate high control performances (both from technological and from economic reasons), the intelligent control figure usage becomes feasible and necessary. In practice, many intelligent control strategies are based on using the neural networks [67][68][69][70]. Consequently, the necessity to train neural controllers occurs.…”

Section: Proof Of Neural Model Feasibility In Future Applicationsmentioning

confidence: 99%

AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison

et al. 2021

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In the paper, the comparison between the efficiency of using artificial intelligence methods and the efficiency of using classical methods in modelling the industrial processes is made, considering as a case study the separation process of the 18O isotope. Firstly, the behavior of the considered isotopic separation process is learned using neural networks. The comparison between the efficiency of these methods is highlighted by the simulations of the process model, using the mentioned modelling techniques. In this context, the final part of the paper presents the proposed model being simulated in different scenarios that can occur in practice, thus resulting in some interesting interpretations and conclusions. The paper proves the feasibility of using artificial intelligence methods for industrial processes modeling; the obtained models being intended for use in designing automatic control systems.

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“…The GA was proposed by John Hollander of the university of Michigan and is commonly used to solve search algorithms for optimization. It was originally developed by drawing on a number of phenomena in evolutionary biology [27], including genetics, mutation, natural selection, and hybridization [28]. The proposed method in this study: MSCGA has the advantage of reducing the computing time while maintaining the original computing power compared to conventional GA.…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

Lee

Hung

2023

IEEE Access

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The main objective of this study is to propose a motor fault diagnosis model based on machine learning. Compared with the traditional motor fault diagnosis model, the proposed model can reduce the computation time. This model can be divided into three steps: feature extraction, feature selection, and classification. In the feature extraction step, the original signal is extracted by Hilbert-Huang transform (HHT), envelope analysis (EA), and variational mode decomposition (VMD) methods. A feature selection method based on memory space computation genetic algorithm (MSCGA) is proposed and applied in the feature selection step. The advantage of MSCGA is that it eliminates the need to compute data fitness values, saving unnecessary computation time repeatedly. The classifiers use k-nearest neighbor (KNN) and support vector machines (SVM). In order to verify the stability and efficiency of the model, the university of California Irvine (UCI) benchmark dataset, the current signal of motor fault datasets, and case western reserve university (CWRU) were used. The UCI dataset is used to test the efficiency and computation time of the feature selection method. Other datasets are used to compare with traditional motor fault diagnosis models. The simulation results of the proposed model have demonstrated the effectiveness in reducing the computation time without affecting the computation results compared to the traditional motor fault diagnosis model. Furthermore, the performance of MSCGA is proven to be better than that of the other algorithm.INDEX TERMS Rotating machine, bearing fault diagnosis model, feature selection method, genetic algorithm.

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Correlation Analysis-Based Neural Network Self-Organizing Genetic Evolutionary Algorithm

Cited by 6 publications

References 19 publications

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison

A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

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