Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Zhang, Zhuo; Sun, Fayu; Li, Qingling; Wang, Weiqiang; Hu, Dedong; Li, Shuangchun

doi:10.3390/pr8121631

Cited by 5 publications

(2 citation statements)

References 40 publications

(50 reference statements)

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“…The neural networks would produce greater accuracy in identification if there was more data available. More data over time will only strengthen the model [10].…”

Section: Weaknessesmentioning

confidence: 99%

Prediction model of Wordle report based on GA-BP neural network

Wang,

Liu

2024

HSET

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Natural Language Processing (NLP) plays a vital role in artificial intelligence, enabling machines to understand and generate human language. This paper proposes a novel approach to improve NLP tasks by integrating a Genetic Algorithm-based BP neural network model. The model combines the strengths of genetic algorithms and neural networks. Genetic algorithms offer effective search capabilities, while the BP neural network provides a flexible learning framework. By integrating these techniques, the proposed model aims to overcome limitations of traditional NLP models. It effectively handles the complexity of natural language data, provides efficient training by avoiding local optima, and exhibits enhanced generalization capabilities for unseen data. Extensive experiments on benchmark NLP datasets validate the effectiveness of the proposed model. Results demonstrate its superiority over state-of-the-art NLP models in terms of accuracy, efficiency, and robustness. This paper presents a novel approach to enhance NLP tasks by integrating a genetic algorithm-based BP neural network model. The model shows promising results in various NLP applications and offers advantages over traditional approaches. This research contributes to the advancement of NLP techniques, facilitating more accurate and efficient language processing systems.

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“…The neural networks would produce greater accuracy in identification if there was more data available. More data over time will only strengthen the model [10].…”

Section: Weaknessesmentioning

confidence: 99%

Prediction model of Wordle report based on GA-BP neural network

Wang,

Liu

2024

HSET

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“…To minimize the input error between the value generated by the network and the desired value (the goal value) for a given collection of training examples, the direction in which weights should be modified needs to be computed. The Backpropagation (BP) algorithm uses the chain rule to compute the gradient of the error for each unit with respect to its weights [20]. Then, an algorithm is needed to optimise the weights.…”

Section: The Nns Learning Mechanismmentioning

confidence: 99%

Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process

et al. 2020

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This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.

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Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties

Qiao

Zhu

2021

Adv Eng Mater

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Herein, attempts have been made to design and develop pearlitic steels for application in heavy‐haul rails. The hardness plays a vital role in studying the mechanical and tribological properties, which is theoretically related to the alloying composition of steel. With aid of machine learning (ML) method, the particle swarm optimization (PSO) improved generalized regression neural network (GRNN) is utilized to model the relationship between composition and hardness of pearlitic steel. The results show that the designed steel exhibits superior hardness and mechanical properties with fine pearlite lamellar microstructure. In addition, the wear behavior of the steel and its wear mechanism are systematically studied by tribological testing and electron probe microanalysis (EPMA) observations of worn surface and wear particles. With composition optimization, the wear resistance has further improved as evidenced by the lower friction coefficient and reduction of wear volume. The pearlitic steels exhibit a combined wear mechanism including adhesive wear, abrasion, delamination, and plastic deformation. As a result, the designed steels offer high hardness with very good mechanical and tribological properties which are far superior to previously reported pearlitic steels. This work may assist in developing the appropriate composition to create the desired hardness, mechanical, and tribological properties.

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Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Cited by 5 publications

References 40 publications

Prediction model of Wordle report based on GA-BP neural network

Prediction model of Wordle report based on GA-BP neural network

Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process

Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties

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