The application of Improved Back Propagation Neural Network on the determination of river longitudinal dispersion coefficient

2011 ◽  
Author(s):  
Hai-Bo Ma ◽  
Guang-bai Cui ◽  
Wen-Juan Chang
Keyword(s):  
Neural Network ◽  
Dispersion Coefficient ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient

A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers

2011 ◽  
pp. 358-374
Author(s):  
Jianhua Yang ◽  
Evor L. Hines ◽  
Ian Guymer ◽  
Daciana D. Iliescu ◽  
Mark S. Leeson ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dispersion Coefficient ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Artificial Neural ◽  
Input Variables ◽  
Training Examples ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural network structure and makes the training process more efficient. Once input variables are determined, GNMM processes the data using an MLP with the back-propagation algorithm. The MLP is presented with a series of training examples and the internal weights are adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study data, which has previously been explored by other authors using various methods. By comparing the results generated by GNMM to those presented in the literature, the effectiveness of this methodology is demonstrated.

Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining

2014 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Rajesh Kumar Porwal ◽  
Vinod Yadava ◽  
J. Ramkumar
Keyword(s):  
Neural Network ◽  
Removal Rate ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Point Of View ◽  
Operating Conditions ◽  
Process Performance

Determination of material removal rate (MRR), tool wear rate (TWR) and hole taper (Ta) is a challenging task for manufacturing engineers from the productivity and accuracy point of view of the symmetrical and nonsymmetrical holes due to hole sinking electro discharge micro machining (HS-EDMM) process. Thus, mathematical models for quick prediction of these aspects are needed because experimental determinations of process performances are always tedious and time consuming. Not only prediction but determination of optimum parameter for optimization of process performance is also required. This paper attempts to apply a hybrid mathematical approach comprising of Back Propagation Neural Network (BPNN) for prediction and Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) for optimization with multiple responses of HS-EDMM of Invar-36. Experiments were conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters MRR, TWR and Ta. The results indicate that the hybrid approach is capable to predict process output and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different material.

Determination of Gamma point source efficiency based on a back-propagation neural network

2018 ◽  
Vol 29 (5) ◽  
Author(s):  
Hong-Long Zheng ◽  
Xian-Guo Tuo ◽  
Shu-Ming Peng ◽  
Rui Shi ◽  
Huai-Liang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Point Source ◽  
Back Propagation ◽  
Back Propagation Neural Network
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