Applied improved RBF neural network model for predicting the broiler output energies

Amini, Sherwin; Taki, Morteza; Rohani, Abbas

doi:10.1016/j.asoc.2019.106006

Cited by 33 publications

(13 citation statements)

References 21 publications

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“…Then, scattered crossover was performed to combine two parents and produce the next generation. Gaussian method was also used for mutation which some of individuals in the population would be randomly changed during the mutation process [70].…”

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

et al. 2022

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Design and fabrication of a catalyst with the highest activity can be achieved by optimising the synthesis conditions. In this study, the sol-gel synthesis conditions of citric acid concentration, gelling temperature, complex time, and calcination temperature were studied for the preparation of a novel semi-hexagonal calcium/titania-zirconia nano-catalyst used in the esterification reaction. After synthesis of around 24 samples at various conditions, their activity was tested in the esterification reaction and the results were analysed by multi-layer perceptron (MLP) and support vector machine (SVM) models. Both models predicted the actual data with high coefficients of determination, and indicated that the calcination temperature has the most influence on the activity of the prepared semi-hexagonal calcium/titania-zirconia nano-catalyst for the esterification reaction. Moreover, the genetic algorithm (GA) was utilised for optimising the preparation conditions based on the SVM model, due to its higher generalisation capability for prediction. The prepared nano-catalysts under the optimum conditions of 1.42 acid ratio, gelling temperature of 72 °C, complex time of 2.65 h, and calcination temperature of 487 °C showed good crystalline structure and metal–metal and metal–oxygen cation bonding. Finally, the fabricated catalyst had a high surface area (276.5 m2/g) with 3.5 nm pore diameter and almost uniform particle size (80–110 nm) distribution, leading to a high conversion of 97.6% in the esterification reaction, with good catalytic stability up to five times.

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Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

et al. 2022

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“…The RBF model has a strong ability to approximate complex nonlinear functions. Be cause of its fast learning speed without a mathematical hypothesis or black box character istics, the model has been extensively applied in function approximation, pattern recog nition, financial systems, signal processing, power systems, expert systems, military sys tems, image processing and computer visions, medical control, and optimization [36,37] In this paper, an RBF neural network is used to map the approximate function between the parameters of the jet pump and the objectives.…”

Section: Approximate Modelmentioning

confidence: 99%

Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Wang

Zhang

et al. 2021

JMSE

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In this study, an annular jet pump optimization method is proposed based on an RBF(Radial Basis Function) neural network model and NSGA-II(Non-Dominated Sorting Genetic Algorithm) optimization algorithm to improve the hydraulic performance of the annular jet pump applied in submarine trenching and dredging. Suction angle, diffusion angle, area ratio and flow ratio were selected as design variables. The computational fluid dynamics (CFD) model was used for numerical simulation to obtain the corresponding performance, and an accurate RBF neural network approximate model was established. Finally, the NSGA-II algorithm was selected to carry out multi-objective optimization and obtain the optimal design variable combination. The results show that the determination coefficient R2 of the two objective functions (jet pump efficiency and head ratio) of the approximate model of the RBF neural network were greater than 0.97. Compared with the original model, the optimized model's suction angle increased, and the diffusion angle, flow ratio and area ratio decreased. In terms of performance, the head ratio increased by 30.46% after the optimization of the jet pump, and efficiency increased slightly. The proposed jet pump performance optimization method provides a reference for improving the performance of other pumps.

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“…The principle of K-fold cross-validation is to divide the entire data sample set into K groups, taking turns to use K-1 groups of the data set as the training set and the remaining group (i groups) as the testing set; each time the model is trained, the corresponding score is obtained, and the final average score is used as the model evaluation criterion. 30 The K-fold cross-validation structure is shown in Figure 3.…”

Section: Modeling Techniquesmentioning

confidence: 99%

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

et al. 2021

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The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model’s performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R 2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

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Applied improved RBF neural network model for predicting the broiler output energies

Cited by 33 publications

References 21 publications

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

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