scholarly journals Development and comparison of Extreme Learning machine and multi-layer perceptron neural network models for predicting optimum coagulant dosage for water treatment

2018 ◽  
Vol 1123 ◽  
pp. 012032 ◽  
Author(s):  
CD Jayaweera ◽  
N. Aziz
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Extreme Learning Machine ◽  
Network Models ◽  
Multi Layer Perceptron ◽  
Neural Network Models ◽  
Coagulant Dosage ◽  
Learning Machine

scholarly journals Two-Dimensional Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Jia ◽  
Dong Li ◽  
Zhisong Pan ◽  
Guyu Hu
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Studies ◽  
Back Propagation ◽  
Image Data ◽  
Network Models ◽  
Support Vector ◽  
Two Dimensional ◽  
Neural Network Models ◽  
Efficiency And Effectiveness ◽  
Learning Machine

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

Recursive Assembly of Multi-Layer Perceptron Neural Networks

2014 ◽  
Author(s):  
Eliot Motato ◽  
Clark Radcliffe
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Multi Layer Perceptron ◽  
Neural Network Models ◽  
Standard Format ◽  
Volterra Models ◽  
Assembly Method ◽  
Hidden Layer ◽  
Function Models

The objective of this paper is to present a methodology to modularly connect Multi-Layer Perceptron (MLP) neural network models describing static port-based physical behavior. The MLP considered in this work are characterized for an standard format with a single hidden layer with sigmoidal activation functions. Since every port is defined by an input-output pair, the number of outputs of the proposed neural network format is equal to the number of its inputs. This work extends the Model Assembly Method (MAM) used to connect transfer function models and Volterra models to multi-layer perceptron neural networks.

Application of ELM to predict the coagulant dosing in water treatment plants

2016 ◽  
Vol 17 (4) ◽  
pp. 1053-1061 ◽  
Author(s):  
Xiaoyan Deng ◽  
Canguang Lin
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Back Propagation ◽  
Purification Process ◽  
Mathematical Methods ◽  
Network Algorithms ◽  
Water Treatment Plants ◽  
Coagulant Dosage ◽  
Learning Machine ◽  
Short Time

Predicting the coagulant dosage is especially crucial to the purification process in water treatment plants, directly affecting the quality of the purified water. Nowadays, several mathematical methods have been adopted for the purification process, but their predictive precision and speed still need to be improved. This study applies a novel neural network called the extreme learning machine (ELM) to predict the coagulant dosage based on certain signification factors of the raw water. Performances are compared between ELM and back-propagation neural networks in this paper. The results show that both neural network algorithms perform well in this application and ELM can realize online prediction due to its short time consumption.

Coagulant Dosage Determination in a Water Treatment Plant Using Dynamic Neural Network Models

2015 ◽  
Vol 14 (03) ◽  
pp. 1550013 ◽  
Author(s):  
Leonaldo Silva Gomes ◽  
Francisco Alexandre A. Souza ◽  
Ricardo Silva Thé Pontes ◽  
Tobias R. Fernandes Neto ◽  
Rui Alexandre M. Araújo
Keyword(s):  
Neural Network ◽  
Water Treatment ◽  
Treatment Plant ◽  
Network Models ◽  
Water Treatment Plant ◽  
Raw Water ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Input Variables

A common step in most of water treatment plants is the chemical coagulation. The chemical coagulation is the process of destabilizing the colloidal particles suspended in raw water by the addition of coagulants. Generally, the determination of the quantity of coagulant to be added to water is made manually by jar tests. However, the manual control has slow response to changes of raw water and it requires intensive laboratory analysis. To reduce the manual effort and to improve the response to change in raw water quality, this work proposes the determination of the coagulant dosage using dynamic neural network modeling using the available sensors as input of the model. The case of study is a large scale water treatment plant in Ceará, Brazil, where the kinds of coagulants added to water are the aluminum sulphate (AS) and poly aluminum chloride (PAC). Several dynamic neural network models with different combinations of the input variables have been evaluated. The best solution found is composed by a nonlinear autoregressive with exogenous input (NARX) model having three input variables, the pH in raw and coagulated water, and the turbidity in the coagulated water, with coefficient of determination of R2 = 0.95 and R2 = 0.91 for the AS and PAC dosage prediction, respectively.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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