Advanced Taguchi-Neural Network Prediction Model for Wire Electrical Discharge Machining Process

Pattnaik, Sarojrani; Sutar, Mihir Kumar

doi:10.1007/s41660-020-00148-1

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…Pattnaik's team developed a prediction model based on the artificial neural network and Taguchi to predict the response. By using a lower level Taguchi orthogonal array to conduct a small number of experiments in the process of wire laying out, the results show that the modified model can predict the response more accurately [13].…”

Section: Related Workmentioning

confidence: 99%

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Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.

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Section: Related Workmentioning

confidence: 99%

“…In order to meet the randomness of sampling, the sampling mode of roulette can be selected. The probability formula of Roulette is shown in equation (13). Genetic operation includes the process of selection, crossover, and mutation.…”

mentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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“…Because the model is based on chaos theory and artificial neural networks, it is referred to as an advanced artificial neural network model. It gets around the restrictions imposed by conventional artificial neural networks [6]. Ma et al determine the energy usage of each gesture to create and train a prediction model.…”

Section: Related Workmentioning

confidence: 99%

Financial Default Risk Prediction Algorithm Based on Neural Network under the Background of Big Data

Xie¹,

Zhang²

2022

Mobile Information Systems

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With the macroeconomy entering a new normal, many new problems are exposed in all walks of life, and the risk of default in the financial sector is also being exposed at an accelerated pace. In the context of big data, internet finance, as an important part of the financial market, also faces many risks in the process of its rapid development. Reasonable, scientific, and effective prediction and prevention of financial default risk have become a key link in the process of risk management practice in the financial industry. Based on the powerful prediction function of the neural network, this paper combined neural network and chaos theory to construct a chaotic RBF neural network. It was applied to financial default risk prediction, which made the prediction accuracy and efficiency higher. The chaotic neural network solves the shortcomings of unstable prediction in the basic neural network and can comprehensively and accurately predict the financial default risk, so as to take measures to prevent risks. The experimental results of this paper show that the accuracy rate of the chaotic RBF neural network reaches 95%, while the accuracy rates of the BP neural network and the RBF neural network are 67% and 78%, respectively. Although the prediction accuracy of these two methods is also high, it is still not as high as the chaotic RBF neural network. Therefore, it is very meaningful to choose the chaotic RBF neural network to predict financial default risk in this paper.

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“…e model he developed is based on the artificial neural network (ANN) model, which overcomes the limitation of traditional artificial neural network that requires a large amount of experimental data to accurately predict the response. It can more accurately predict the response at any point in between and at selected parameter levels [9]. At present, artificial neural network prediction model has become a new and widely recognized method in prediction.…”

Section: Related Workmentioning

confidence: 99%

Macroeconomic Forecast Model System Based on Digital Information and Blockchain Technology

2022

Mobile Information Systems

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Since the reform and opening up, China’s economy has developed rapidly, becoming the second largest economy in the world. The state of macroeconomic development has a great influence on the government’s policy introduction and the investment decisions of individual institutions. Therefore, forecasting the macroeconomy is of great significance to the country. This paper aims to study the macroeconomic forecasting model system based on digital information and blockchain technology. This paper proposes an artificial neural network prediction algorithm based on digital information and blockchain technology, and the artificial neural network and particle swarm algorithm are combined to become the hybrid artificial neural network algorithm, and the conception of the establishment of the macroeconomic forecast model is proposed. The experimental results of this paper show that according to the prediction results of artificial neural network, the prediction and actual error in 2010 is 0.10, while the new method proposed in this paper predicts the error of 0.056. In 2016, the prediction error based on artificial neural network was 0.14, while the prediction result of the new method proposed in this paper showed an error of 0.008. In each year’s economic forecast, the neural network model of the new method proposed in this paper has higher prediction accuracy and smaller error. It can be seen that the neural network model based on artificial neural network and PSO algorithm proposed in this paper is beneficial to macroeconomic forecasting.

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Advanced Taguchi-Neural Network Prediction Model for Wire Electrical Discharge Machining Process

Cited by 15 publications

References 21 publications

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Financial Default Risk Prediction Algorithm Based on Neural Network under the Background of Big Data

Macroeconomic Forecast Model System Based on Digital Information and Blockchain Technology

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