DAFTAR PUSTAKA
DAFTAR PUSTAKA
[1] PT. Pertamina (Persero). Buku Panduan Optimasi Boiler dan Furnace Kilang. Jakarta, April 2010.
[2] American Petroleum Institute. API Standard 560: Fired Heaters for General Refinery Service. Washington, Febuari 2016.
[3] Peter Mullinger dan Barrie Jenkins. Industrial and Process Furnaces.
Elsevier, 2008.
[4] David Lindsley. Power-Plant Control and Instrumentation The Control of Boilers and HSRG System. Institution of Electrical Engineers, United Kingdom, 2005.
[5] Philippe Lambinet. Why use RBF Learning rather than Deep Learning in an industrial environment. Diakses dari laman https://iiot- world.com/machine-learning pada tanggal 18 Oktober 2019.
[6] Pietruschka U. dan R.W. Brause. Using Growing RBF-Nets in Rubber Industry Process Control. Journal of Neural Computing & Applications.
Springer. London, UK. 1999.
[7] Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, dan Sung-Bae Cho. Radial Basis Function Neural Networks : A Topical State- of-the-art Survey. ResearchGate Publications. Januari 2016.
[8] Surajdeen A. Iliyas, Moustafa Elshafei, Mohamed A. Habib, dan Ahmed A. Adeniran. RBF Neural Network Inferential Sensor for Process Emission Monitoring. Journal of Control Engineering Practice. 18 April 2013.
[9] Lei Wang, Cheng Shao, Hai Wang, dan Hong WU. Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process:
Hydrogen Recovery from Refinery Gases. Journal of Natural Gas Chemistry. 15 Mei 2006.
[10] Wei Zhoung dan Jinshou Yu. MIMO Soft Sensors for Estimating Product Quality with Online Correction. Journal of Institution of Chemical Engineers. Vol 78, Part A. Mei 2000.
[11] Gancho V, Nikolinka C, dan Mangdalena Valova. Optimization Strategies for Tunning the Parameters of Radial Basis Function Network Models.
Journal of International Conference on Simulation and Modeling Methodologies, Technologies, and Applications. Agustus 2014.
[12] J: K. Sing, D. K. Basu ,M. Nasipuri, dan M. Kundu. Improved K-means Algorithm in the Design of RBF Neural Networks. Journal of IEEE. Vol.
2. 2003
[13] Wetterschereck D., Dietterich T. Improving the Performance of Radial Basis Function Networks by Learning Center Locations. Advances in Neural Information Processing Systems. Vol. 4, pp. 1133–1140. United State of Amerika.1992.
[14] Ziyang Z. , Zhisheng W. ,Yong H., dan Mingzhi G. Learning Method of RBF Network Based on FCM and ACO. In Proceedings of Control and Decision Conference (CCDC). pp.102-105. 2008.
[15] Yue Wu, Hui Wang, Biaobiao Zhang, dan K. L. Du. Using Radial Basis Function Networks for Function Approximation and Classification.
International Scholarly Research Network. 5 Desember 2011.
[16] C.C. Chuang, J.T.Jeng, dan P.T. Lin. Annealing Robust Radial Basis Function Networks for Function Approximation with Outliers.
Neurocomputing. Vol. 56, no. 1–4, pp. 123–139. 2004.
[17] K.L. Du dan M. N. S. Swamy. Neural Networks in a Softcomputing Framework. Springer. London, UK. 2006.
[18] Yuan J.L, Li X.Y, dan Zhong L. Optimized Grey RBF Prediction Model Based on Genetic Algorithm. Proceeding of International Conference on Computer Science and Software Engineering. Vol.1, pp. 74-77. 2008.
[19] Zhiqiang Geng, Jie Chen, dan Yongming Han. Energy Efficiency Prediction Based on PCA-FRBF Model: A Case Study of Ethylene Industries. IEEE Transactions on Systems, Man, and Cybernetics:
Systems. Vol. 47, No. 8. Agustus 2017.
[20] Ridong Zhang, Qiang Lv, Jili Tao, dan Furong Gao. Data Driven Modeling Using an Optimal Principle Component Analysis Based Neural Network and Its Application to a Nonlinear Coke Furnace. Industrial &
Engineering Chemistry Research Article, 57, 6344-6352. 2018.
[21] K. L. Du. Clustering: a neural network approach. Neural Networks, Vol.
23, No. 1, pp. 89–107. 2010.
[22] Choirul Anam, Jones Victor Tuapetel, M. Kurniadi Rasyid, Putu M.
Santika, dan Dwita Suastiyanti. Analisis Peningkatan Efisiensi Direct- Fired Heater. Jurnal Teknik Mesin, Vol. 2, Agustus 2018.
[23] American Petroleum Institute. API Standard 535: Burners for Fired Heaters in General Refinery Service.. Washington, Juli 1995.
[24] Luigi Fortuna, Alessandro Rizzo, Salvatore Graziani, dan Maria G.
Xibilia. Soft Sensor for Monitoring and Control of Industrial Processes.
Springer, 2007.
[25] Peter Kadlec, Bodgan Gabrys, Sibylle Strandt. Data-driven Soft Sensor in the Process Industry. Journal of Computer and Chemical Engineering, 33, 795-814, 2019.
[26] Luthfi M. Rambe. Klasifikasi Citra Sel Darah Putih Limfoblas dan Non Limfoblas Pada Kasus Acute Lymphoblastic Leukemia Tipe L1 Dengan Menggunakan Metode K-Nearest Neighbor. Universitas Gadjah Mada, Yogyakarta. 2019
[27] Rani R. H. dan Victoire T. AA. Training Radial Basis Function Networks for Wind Speed Prediction Using PSO Enhanced Differential Search Optimizer. PLOS ONE Publishing. 12 Januari 2018.
[28] C.O.S. Sorzano, J. Vargas, dan A Pascual Montano. A Survey of Dimensionality Reduction Tehcniques. Research Gate Publishing. 2014.
[29] Shireen Elhabian dan Aly Farag. Principal Component Analysis Theoretical Discussion. University of Louisville, November 2008.
[30] T. Poggio dan F. Girosi. Networks for Approximation and Learning.
Proceedings of the IEEE. Vol. 78, no. 9, pp. 1481–1497.1990.
[31] Y. Liao, S.C. Fang, dan H.L.W. Nuttle. Relaxed Conditions for Radial- Basis Function Networks to be Universal Approximators. Neural Networks. Vol. 16, no. 7, pp. 1019–1028,.2003.
[32] C.A. Micchelli. Interpolation of Scattered Data Distance Matrices and Conditionally Positive Definite Functions. Constructive Approximation.
Vol. 2, no. 1, pp. 11–22. 1986.
[33] Shiva Kumar, P. Srinivasa Pai, dan B.R. Shrinivasa Rao. Radial Basis Function Network Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Runon WCO Ester. Hindawi Publishing Corporation. 21 September 2012.
[34] Dietrich W. dan Thomas D. Improving the Performance of Radial Basis Function Networks by Learning Center Locations. 1991.
[35] John A. Bullinaria. Radial Basis Function Networks Algorithms. 2015.
[36] A. V. D. Sanchez. Robustization of a learning method for RBF networks.
Neurocomputing. Vol. 9, No. 1, pp. 85–94. 1995.
[37] C.C. Aggarwal. Chapter 5: Radial Basis Function Networks. Springer International Publishing. 2018.
[38] Silvia Vateni, M. Vannucci, M. Vannoci, dan V. Colla. Chapter 6:
Variable Selection and Feature Extraction Through Artificial Intelligence Techniques. Research Gate Publishing, Januari 2013.
[39] Kurt Erik Häggblom. Basics of Multivariate Modelling and Data Analysis.
Abo Akademi University. 2012.
[40] Jon Shlens. A Tutorial on Principal Component Analysis Derivation, Discussion and Singular Value Decomposition. 25 Maret 2003.
[41] Orumie, Ukamaka Cynthia1, dan Ogbonna Onyinyechi. Principal Component Analysis and Its Derivation From Singular Value Decomposition. International Journal of Statistics and Probability. Vol. 8, No. 2. Maret 2019
[42] Trevor Hastie, Robert Tibshirani, dan Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer. 2016
[43] Christopher M. Bishop. Pattern Recognition and Machine Learning.
Springer. 6 April 2011.
[44] Mario Anggara, Herry Sujiani, dan Helfi Nasution. Pemilihan Distance Measure Pada K-Means Clustering Untuk Pengelompokkan Member Di Alvaro Fitness. Jurnal Sistem dan Teknologi Informasi (JUSTIN). Vol. 1, No. 1. 2016.
[45] Tahta Alfina, Budi Santosa, dan Ali Ridho Barakbah. Analisa Perbandingan Metode Hierarchical Clustering, K-means, dan Gabungan Keduanya dalam Membentuk Cluster Data. Jurnal Teknik POMITS. Vol.
1, No. 1. 2012.
[46] Aishwarya V Srinivasan. Stochastic Gradient Descent. Diakses dari laman https://towardsdatascience.com/stochastic-gradient-descent-clearly- explained-53d239905d31 pada tanggal 6 Desember 2019.
[47] Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton. On the Importance of Initialization and Momentum in Deep Learning.
Proceedings of the 30th International Conference on Machine Learning.
USA. 2013.
[48] L. Fortuna, S. Graziani, A. Rizzo, Maria G. Xibilia. Soft Sensors for Monitoring and Control of Industrial Processes. Springer. 2007
[49] Peter Kadlec, R. Grbic, B. Gabrys. Data-driven Soft Sensors in Process Industry. Review of Adaptation Mechanisms for Data-driven Soft Sensors.
Journal of Computer and Chemical Engineering. 18 Agustus 2010.
[50] Pearson, R. K. Outliers in Process Modelling and Identification. IEEE Transactions on Control System Technology. 10(1), pp. 55-63. 2002.
[52] Lin B, Recke B, Knudsen J, dan Jergensen S.B. A Systematic Approach for Soft Sensor Develompment. Journal of Computer and Chemical Engineering. 31(5) 419-425. 2007.
[53] Amazouz M dan Pantea R. Use of Multivariate Data Analysis for Lumber Drying Process Monitoring and Fault Detection. Proceeding of the International Conference on Data Mining. pp 329-332. 2006
[54] Peter Kadlec, B. Gabrys, S. Strandt. Review of Adaptation Mechanisms for Data-driven Soft Sensors. Journal of Computer and Chemical Engineering. 20 Januari 2009.
[55] Dwinanto Rizky W. Klasifikasi Citra Sel Darah Putih Menggunakan Metode Naïve Bayes Dengan Kernel Density Estimastion. Universitas Gadjah Mada, Yogyakarta. 2019
[56] Jollife, I.T. Principal Component Analysis. Springer. 2002.
[57] Joel Feldman. Finding Maxima and Minima. Diakses dari laman https://www.math.ubc.ca/~feldman/m105/maxmin.pdf pada tanggal 12 Desember 2019.
[58] Moke Xu, Yu Liang, dan Wenjum Wu. Predicting Honors Student Performance Using RBFNN and PCA Method. DASFAA 2017 International Workshops, pp.364-375. China. Maret 2017.
[59] Friedhelm Schwenker, Hans A. Kestler, dan Gunther Palm. Three Learning Phases for Radial Basis Function Networks. Journal of Neural Network 14, pp 439-458. Elsevier. 18 Desember 2000.
