scholarly journals A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Measurement Data ◽  
Estimation Method ◽  
Cuckoo Search ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

scholarly journals A Real-time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Vol 9 (3) ◽  
pp. 523 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Improved Cuckoo Search Algorithm

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

scholarly journals A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qinwei Fan ◽  
Tongke Fan
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Classification Problems ◽  
Neural Network Learning ◽  
Practical Applications ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM), as a new simple feedforward neural network learning algorithm, has been extensively used in practical applications because of its good generalization performance and fast learning speed. However, the standard ELM requires more hidden nodes in the application due to the random assignment of hidden layer parameters, which in turn has disadvantages such as poorly hidden layer sparsity, low adjustment ability, and complex network structure. In this paper, we propose a hybrid ELM algorithm based on the bat and cuckoo search algorithm to optimize the input weight and threshold of the ELM algorithm. We test the numerical experimental performance of function approximation and classification problems under a few benchmark datasets; simulation results show that the proposed algorithm can obtain significantly better prediction accuracy compared to similar algorithms.

Reproducing Polynomial Kernel Extreme Learning Machine

2017 ◽  
Vol 21 (5) ◽  
pp. 795-802 ◽  
Author(s):  
Yibo Li ◽  
Chao Liu ◽  
Senyue Zhang ◽  
Wenan Tan ◽  
Yanyan Ding ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Reproducing Kernel ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Fast Training ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.

scholarly journals A hybrid control strategy based on CSA and ELM for uncertain nonlinear chaotic system

2020 ◽  
Vol 14 ◽  
pp. 174830262092250
Author(s):  
Yan Li ◽  
Yigang He ◽  
Wenxin Yu
Keyword(s):  
Extreme Learning Machine ◽  
Chaotic System ◽  
Control Strategy ◽  
Chaotic Systems ◽  
Hybrid Control ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Nonlinear Chaotic System ◽  
Learning Machine

The study of nonlinear chaotic systems and their control is an important topic. In this paper, a hybrid control strategy based on cuckoo search algorithm and extreme learning machine is proposed. Cuckoo search algorithm is used in a hybrid control strategy in order to optimise the weights and biases in extreme learning machine leading to the improvement of its performance. Simulations indicate that the proposed method is able to fit nonlinear chaotic systems and control chaotic systems effectively. Data used in the nonlinear chaotic system are also tested for uncertainty and unknown systems. Simulation results confirm that the proposed method shows robustness for noisy data and perturbed parameters.

A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine

2015 ◽  
Vol 03 (04) ◽  
pp. 267-275
Author(s):  
Liang Dai ◽  
Yuesheng Zhu ◽  
Guibo Luo ◽  
Chao He ◽  
Hanchi Lin
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Visual Tracking ◽  
Computational Cost ◽  
Real Time Tracking ◽  
Sparse Autoencoder ◽  
Selection For ◽  
Learning Machine ◽  
Hidden Layer ◽  
Online Process

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

scholarly journals Fiber Bragg Grating Dynamic Calibration Based on Online Sequential Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1840 ◽  
Author(s):  
Qiufeng Shang ◽  
Wenjie Qin
Keyword(s):  
Fiber Bragg Grating ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Sensor Calibration ◽  
Bragg Grating ◽  
Dynamic Calibration ◽  
Calibration Model ◽  
Bp Network ◽  
Fbg Sensor ◽  
Learning Machine

The fiber Bragg grating (FBG) sensor calibration process is critical for optimizing performance. Real-time dynamic calibration is essential to improve the measured accuracy of the sensor. In this paper, we present a dynamic calibration method for FBG sensor temperature measurement, utilizing the online sequential extreme learning machine (OS-ELM). During the measurement process, the calibration model is continuously updated instead of retrained, which can reduce tedious calculations and improve the predictive speed. Polynomial fitting, a back propagation (BP) network, and a radial basis function (RBF) network were compared, and the results showed the dynamic method not only had a better generalization performance but also had a faster learning process. The dynamic calibration enabled the real-time measured data of the FBG sensor to input calibration models as online learning samples continuously, and could solve the insufficient coverage problem of static calibration training samples, so as to improve the long-term stability, accuracy of prediction, and generalization ability of the FBG sensor.

scholarly journals Hybrid Artificial Neural Network with Extreme Learning Machine Method and Cuckoo Search Algorithm to Predict Stock Prices

2020 ◽  
Vol 1 (2) ◽  
pp. 131
Author(s):  
Piping Prabawati ◽  
Auli Damayanti ◽  
Herry Suprajitno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Extreme Learning Machine ◽  
Stock Prices ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Training Process ◽  
Validation Test ◽  
Learning Machine

This thesis aims to predict the stock prices, using artificial neural network with extreme learning machine (ELM) method and cuckoo search algorithm (CSA). Stock is one type of investment that is in great demand in Indonesia. The portion ownership of stock is determined by how much investment is invested in the company. In this case, stock is an aggressive type of investment instrument, because stock prices can change over time. In this case, ELM is used to determine forecasting values, while CSA is applied to compile and optimize the values of weights and biases to be used in the forecasting process. After obtaining the best weights and biases, the validation test process is then carried out to determine the level of success of the training process. The data used is the daily data of the stock price of PT. Bank Mandiri (Persero) Tbk. the total is 291 data. Furthermore, the data is divided into 70% for the training process is as many as 199 data and 30% for the validation test as many as 87 data. Then compiled pattern of training and validation test patterns is 198 patterns and 82 patterns. Based on the implementation of the program, with several parameter obtained the result of  MSE training is 0.001304353, with an MSE of validation test is 0.0031517704. Because the MSE value obtained is relatively small, this indicates that the ELM-CSA network is able to recognize data patterns and is able to predict well.

A New DROS-Extreme Learning Machine With Differential Vector-KPCA Approach for Real-Time Fault Recognition of Nonlinear Processes

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Yuan Xu ◽  
Liang-Liang Ye ◽  
Qun-Xiong Zhu
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Detection Sensitivity ◽  
Fault Recognition ◽  
Differential Operation ◽  
Input Sample ◽  
The Common ◽  
The Difference ◽  
Learning Machine ◽  
Hidden Layer

In this paper, a new dynamic recurrent online sequential-extreme learning machine (DROS-ELM) OS-ELM with differential vector-kernel based principal component analysis (DV-KPCA) fault recognition approach is proposed to reconstruct the process feature and detect the process faults for real-time nonlinear system. Toward this end, the differential vector plus KPCA is first proposed to reduce the dimension of process data and enlarge the feature difference. In DV-KPCA, the differential vector is the difference between the input sample and the common sample, which is obtained from the historical data and represents the common invariant properties of the class. The optimal feature vectors of input sample and the common sample are obtained by KPCA procedure for the difference vectors. Through the differential operation between the input vectors and the common vectors, the reconstructed feature is derived by calculating the two-norm distance for the result of differential operation. The reconstructed features are then utilized to detect the process faults that may occur. In order to enhance the accuracy of fault recognition, a new DROS-ELM is developed by adding a self-feedback unit from the output of hidden layer to the input of hidden layer to record the sequential information. In the DROS-ELM, the output weight of feedback layer is updated dynamically by the change rate of output of the hidden layer. The DV-KPCA for feature reconstruction is exemplified using UCI handwriting (UCI handwriting recognition data: Database, using “Pen-Based Recognition of Handwritten Digits” produced in the Department of Computer Engineering Bogazici University, Istanbul 80815, Turkey, 1998), which the classification accuracy is obviously enhanced. Meanwhile, the DROS-ELM for process prediction is tested by the sunspot data from 1700 to 1987, which also shows better prediction accuracy than common methods. Finally, the new joint DROS-ELM with DV-KPCA method is exemplified in the complicated Tennessee Eastman (TE) benchmark process to illustrate the efficiencies. The results show that the DROS-ELM with DV-KPCA shows superiority not only in detection sensitivity and stability but also in timely fault recognition.

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