Top

Soft Computing

Published in:

29-05-2017 | Methodologies and Application

Model NOx emission and thermal efficiency of CFBB based on an ameliorated extreme learning machine

Authors: Peifeng Niu, Yunpeng Ma, Guoqiang Li

Published in: Soft Computing | Issue 14/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Extreme learning machine (ELM) is a novel single hidden layer feed-forward network, which has become a research hotspot in various domains. Through in-depth analysis on ELM, there are four factors mainly affect its model performance, such as the input data, the input weights, the number of hidden layer nodes and the hidden layer activation function. In order to enhance the performance of ELM, an ameliorated extreme learning machine, namely AELM, is proposed based on the aforementioned four factors. The proposed method owns new way to generate input weights and bias of hidden layer and has a new-type hidden layer activation function. Simulations on many UCI benchmark regression problems have demonstrated that the AELM generally outperforms the original ELM as well as several variants of ELM. Simultaneously, the AELM is adopted to build thermal efficiency model and NOx emission model of a 330MW circulating fluidized bed boiler. The results demonstrate the AELM is a useful machine learning tool.

previous article Qualitative modeling of catastrophe in group opinion

next article Unsupervised classification of erroneous video object trajectories

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Akusok A, Bjork KM, Miche Y et al (2015) High-performance extreme learning machines: a complete toolbox for big data applications. Access IEEE 3:1011–1025CrossRef

An L, Bhanu B. (2012) Image super-resolution by extreme learning machine. IEEE international conference on image processing. IEEE, pp 2209–2212

Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–55CrossRefMATH

Buche D, Stoll P, Dornberger R et al (2002) Multiobjective evolutionary algorithm for the optimization of noisy combustion processes. IEEE Trans Sys Man Cybern Part C 32(4):460–473CrossRef

Cai W, Chen S, Zhang D (2007) Robust fuzzy relational classifier incorporating the soft class labels. Pattern Recogn Lett 28(16):2250–2263CrossRef

Cao J, Lin Z, Huang GB et al (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77MathSciNetCrossRef

Cao F, Liu B, Park DS (2013) Image classification based on effective extreme learning machine. Neurocomputing 102:90–97CrossRef

Cass R, Radl B (1996) Adaptive process optimization using functional-link networks and evolutionary optimization. Control Eng Pract 4(11):1579–1584CrossRef

Chen FL, Ou TY (2011) Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Syst Appl 38(3):1336–1345CrossRef

Choi K, Toh KA, Byun H (2012) Incremental face recognition for large-scale social network services. Pattern Recogn 45(8):2868–2883CrossRef

Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314MathSciNetCrossRefMATH

Demsǎr J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH

Fu H, Vong CM, Wong PK et al (2016) Fast detection of impact location using kernel extreme learning machine. Neural Comput Appl 27(1):121–130CrossRef

Havlena V, Findejs J (2005) Application of model predictive control to advanced combustion control. Control Eng Pract 13(6):671–680CrossRef

Horata P, Chiewchanwattana S, Sunat K (2013) Robust extreme learning machine. Neurocomputing 102:31–44CrossRef

Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13:415–425CrossRef

Huang GB, Siew CK (2004) Extreme learning machine: RBF network case. In: Control, automation, robotics and vision conference, 2004. Icarcv 2004.,Vol 2, pp 1029–1036

Huang GB, Zhu QY, Siew CK (2006a) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRef

Huang G-B, Chen L, Siew C-K (2006b) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892CrossRef

Huang GB, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern A Publ IEEE Syst Man Cybern Soc 42(42):513–529CrossRef

Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468CrossRef

Jaeger H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 304:78–80CrossRef

Kan EM, Meng HL, Ong YS et al (2013) Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Comput Appl 22(3):469–477CrossRef

Krzywanski J, Blaszczuk A, Czakiert T, et al (2014) Artificial intelligence treatment of NOx emission from CFBC in air and oxy-fuel conditions. In: The 11-th international conferences on fluidized bed technology, Cfb

Kusiak A, Song Z (2006) Combustion efficiency optimization and virtual testing: a data-mining approach. IEEE Trans Industr Inf 2(3):176–184CrossRef

Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2(4):436–438MathSciNetMATH

Li MB, Huang GB, Saratchandran P et al (2005) Fully complex extreme learning machine. Neurocomputing 68(1):306–314CrossRef

Li G, Niu P, Zhang W et al (2013) Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching-learning-based optimization. Chemom Intell Lab Syst 126(8):11–20CrossRef

Li G, Niu P, Duan X et al (2014a) Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput Appl 24(7–8):1683–1695CrossRef

Li G, Niu P, Ma Y et al (2014b) Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency. Knowl-Based Syst 67:278–289CrossRef

Li G, Niu P (2013) An enhanced extreme learning machine based on ridge regression for regression. Neural Comput Appl 22(3–4):803–810CrossRef

Liu X, Wang L, Huang GB (2013) Multiple kernel extreme learning machine. Neurocomputing 149:253–264CrossRef

Niu P, Ma Y, Li M et al (2016) A kind of parameters self-adjusting extreme learning machine. Neural Process Lett 44(3):813–830CrossRef

Qi D (1998) On design of the BP neural network. Comput Eng Des 19(2):48–49

Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, New YorkMATH

Rong HJ, Ong YS, Tan AH et al (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(1):359–366CrossRef

Shrivastava NA, Panigrahi BK (2014) A hybrid wavelet-ELM based short term price forecasting for electricity markets. Int J Electr Power Energy Syst 55:41–50CrossRef

Soria-Olivas E, Gómez-Sanchis J, Martín JD et al (2011) BELM: Bayesian extreme learning machine. Neural Netw IEEE Trans 22(3):505–509CrossRef

Suresh S, Babu RV, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9(2):541–552CrossRef

Tian HX, Mao ZZ (2010) An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace. Autom Sci Eng IEEE Trans 7(1):73–80CrossRef

Vincent P, Larochelle H, Lajoie I et al (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371–3408MathSciNetMATH

Vincent P, Larochelle H, Bengio Y, et al (2008) Extracting and composing robust features with denoising autoencoders. In: International conference, pp 1096–1103

Xiang J, Westerlund M, Sovilj, D, et al (2014) Using extreme learning machine for intrusion detection in a big data environment. In: The workshop on artificial intelligent and security workshop, pp 73–82

Xin J, Wang Z, Qu L et al (2015) Elastic extreme learning machine for big data classification. Neurocomputing 149(PA):464–471CrossRef

Yang ZR (2006) A novel radial basis function neural network for discriminant analysis. IEEE Trans Neural Networks 17(3):604–12CrossRef

Zhang J, Haghighat F (2010) Development of artificial neural network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy Build 42(4):435–440CrossRef

Zhao J, Park DS, Lee J et al (2012) Generalized extreme learning machine acting on a metric space. Soft Comput 16(9):1503–1514CrossRefMATH

Zhu QY, Qin AK, Suganthan PN et al (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763CrossRefMATH

Title: Model NOx emission and thermal efficiency of CFBB based on an ameliorated extreme learning machine
Authors: Peifeng Niu
Yunpeng Ma
Guoqiang Li
Publication date: 29-05-2017
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 14/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2653-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 14/2018

Neutrosophic number linear programming method and its application under neutrosophic number environments

Some results on fuzzy -continuous(algebraic) poset

Distance-based nonlinear programming models to identify and adjust inconsistencies for linguistic preference relations

A new method to evaluate risk in failure mode and effects analysis under fuzzy information

Approximate bisimulation relations for fuzzy automata

Solving fully interval linear systems of equations using tolerable solution criteria

Premium Partner