# FUZZY, NEURAL NETWORK AND EXPERT SYSTEMS METHODOLOGIES AND APPLICATIONS-A REVIEW

## Keywords:

Literature survey, Artificial intelligence methodologies, Fuzzy systems, Neural network, Expert systems## Abstract

The rapid growth in the field of artificial intelligence from past one decade has a significant impact on various application areas i.e. health, security, home appliances among many. In this paper we aim to review artificial intelligence methodologies and their potential applications intended for variable purposes i.e. Agriculture, applied sciences, business, engineering, finance, management etc. For this purpose articles from past one decade (from 2004 to 2013) are reviewed in order to explore the most recent research advancements in this domain. The review includes 172 articles gathered from related sources including conference proceedings and academic journals. We have categorized the selected articles into four main categories i.e. fuzzy systems, neural network based systems, neuro fuzzy systems and expert systems. Furthermore, expert systems are further classified into three categories: (i) rule based expert systems, (ii) knowledge based expert systems and (iii) intelligent agents. This review presents research implications for practitioners regarding integration of artificial intelligence techniques with classical approaches and suggestions for exploration of AI techniques in variable applications.

### Downloads

## References

B. Coppin, Artificial Intelligence Illuminated. 2004, p. 768.

R. E. King, Computational Intelligence in Control Engineering. Marcel Dekker, Inc. New

York, 1999.

S. Liao, “Expert system methodologies and applications—a decade review from 1995 to

,” Expert Syst. Appl., vol. 28, no. 1, pp. 93–103, Jan. 2005.

S. Sahin, M. R. Tolun, and R. Hassanpour, “Hybrid expert systems: A survey of current

approaches and applications,” Expert Syst. Appl., vol. 39, no. 4, pp. 4609–4617, Mar. 2012.

L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8. pp. 338–353, 1965.

J. Sun, S. Qin, and Y.-H. Song, “Fault Diagnosis of Electric Power Systems Based on Fuzzy

Petri Nets,” IEEE Trans. Power Syst., vol. 19, pp. 2053–2059, 2004.

R. Isermann, “Model-based fault-detection and diagnosis - Status and applications,” Annual

Reviews in Control, vol. 29. pp. 71–85, 2005.

P. R. Innocent and R. I. John, “Computer aided fuzzy medical diagnosis,” Inf. Sci. (Ny)., vol.

, pp. 81–104, 2004.

R. I. John and P. R. Innocent, “Modeling uncertainty in clinical diagnosis using fuzzy logic.,”

IEEE Trans. Syst. Man. Cybern. B. Cybern., vol. 35, pp. 1340–1350, 2005.

R. Seising, “From vagueness in medical thought to the foundations of fuzzy reasoning in

medical diagnosis,” Artif. Intell. Med., vol. 38, pp. 237–256, 2006.

S. Labiod and T. M. Guerra, “Adaptive fuzzy control of a class of SISO nonaffine nonlinear

systems,” Fuzzy Sets Syst., vol. 158, pp. 1126–1137, 2007.

A. Boulkroune, M. Tadjine, M. M’Saad, and M. Farza, “Fuzzy adaptive controller for MIMO

nonlinear systems with known and unknown control direction,” Fuzzy Sets Syst., vol. 161, pp.

–820, 2010.

Z. Xu and T. M. Khoshgoftaar, “Identification of fuzzy models of software cost estimation,”

Fuzzy Sets Syst., vol. 145, pp. 141–163, 2004.

J. Aroba, J. J. Cuadrado-Gallego, M.-Á. Sicilia, I. Ramos, and E. García-Barriocanal,

“Segmented software cost estimation models based on fuzzy clustering,” Journal of Systems

and Software, vol. 81. pp. 1944–1950, 2008.

L. Doitsidis, K. P. Valavanis, N. C. Tsourveloudis, and M. Kontitsis, “A framework for fuzzy

logic based UAV navigation and control,” IEEE Int. Conf. Robot. Autom. 2004. Proceedings.

ICRA ’04. 2004, vol. 4, 2004.

E. F. Carrasco, J. Rodriguez, A. Punal, E. Roca, and J. M. Lema, “Diagnosis of acidification

states in an anaerobic wastewater treatment plant using a fuzzy-based expert system,” Control

Eng. Pract., vol. 12, pp. 59–64, 2004.

M. Fiter, D. Güell, J. Comas, J. Colprim, M. Poch, and I. Rodríguez-Rodal, “Energy saving in

a wastewater treatment process: an application of fuzzy logic control.,” Environ. Technol., vol.

, pp. 1263–1270, 2005.

J. Ye, “Fuzzy decision-making method based on the weighted correlation coefficient under

intuitionistic fuzzy environment,” Eur. J. Oper. Res., vol. 205, pp. 202–204, 2010.

G. Wei, “Hesitant fuzzy prioritized operators and their application to multiple attribute

decision making,” Knowledge-Based Syst., vol. 31, pp. 176–182, 2012.

A. Sala, T. Guerra, and R. Babuska, “Perspectives of fuzzy systems and control,” Fuzzy Sets

Syst., vol. 156, pp. 432–444, 2005.

B.-J. Rhee and S. Won, “A new fuzzy Lyapunov function approach for a Takagi–Sugeno fuzzy

control system design,” Fuzzy Sets and Systems, vol. 157. pp. 1211–1228, 2006.

X. Yang, M. Moallem, and R. V. Patel, “A layered goal-oriented fuzzy motion planning

strategy for mobile robot navigation,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol.

, pp. 1214–1224, 2005.

M. A. P. Garcia, O. Montiel, O. Castillo, R. Sepúlveda, and P. Melin, “Path planning for

autonomous mobile robot navigation with ant colony optimization and fuzzy cost function

evaluation,” Applied Soft Computing, vol. 9. pp. 1102–1110, 2009.

J. Figueroa, J. Posada, J. Soriano, M. Melgarejo, and S. Rojas, “A Type-2 Fuzzy Controller for

Tracking Mobile Objects in the Context of Robotic Soccer Games,” 14th IEEE Int. Conf.

Fuzzy Syst. 2005. FUZZ ’05., 2005.

M. Y. Ju, C. Sen Ouyang, and H. S. Chang, “Mean shift tracking using fuzzy color histogram,”

in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, 2010,

vol. 6, pp. 2904–2908.

I. K. Vlachos and G. D. Sergiadis, “Intuitionistic fuzzy information - Applications to pattern

recognition,” Pattern Recognit. Lett., vol. 28, pp. 197–206, 2007.

G. Bailador and G. Trivino, “Pattern recognition using temporal fuzzy automata,” FUZZY

SETS Syst., vol. 161, pp. 37–55, 2010.

H. Liu, D. J. Brown, and G. M. Coghill, “Fuzzy qualitative robot kinematics,” IEEE Trans.

Fuzzy Syst., vol. 16, pp. 808–822, 2008.

P. K. Jamwal, S. Q. Xie, Y. H. Tsoi, and K. C. Aw, “Forward kinematics modelling of a

parallel ankle rehabilitation robot using modified fuzzy inference,” Mech. Mach. Theory, vol.

, pp. 1537–1554, 2010.

Z. Song, J. Yi, D. Zhao, and X. Li, “A computed torque controller for uncertain robotic

manipulator systems: Fuzzy approach,” Fuzzy Sets Syst., vol. 154, pp. 208–226, 2005.

M. K. Chang, “An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF

rehabilitation robot actuated by pneumatic muscle actuators,” Control Eng. Pract., vol. 18, pp.

–22, 2010.

C.-T. Chen, C.-T. Lin, and S.-F. Huang, “A fuzzy approach for supplier evaluation and

selection in supply chain management,” International Journal of Production Economics, vol.

pp. 289–301, 2006.

D. Peidro, J. Mula, R. Poler, and J. L. Verdegay, “Fuzzy optimization for supply chain

planning under supply, demand and process uncertainties,” Fuzzy Sets Syst., vol. 160, pp.

–2657, 2009.

C. S. Liu, L. R. Chen, B. Z. Li, S. K. Chen, and Z. S. Zeng, “Improvement of the twin rotor

MIMO system tracking and transient response using fuzzy control technology,” in 2006 1st

IEEE Conference on Industrial Electronics and Applications, 2006.

C. W. Tao, J. S. Taur, and Y. C. Chen, “Design of a parallel distributed fuzzy LQR controller

for the twin rotor multi-input multi-output system,” Fuzzy Sets Syst., vol. 161, pp. 2081–2103,

S. K. Nguang, P. Shi, and S. Ding, “Fault detection for uncertain fuzzy systems: An LMI

approach,” IEEE Trans. Fuzzy Syst., vol. 15, pp. 1251–1262, 2007.

M. Abadeh, J. Habibi, and C. Lucas, “Intrusion detection using a fuzzy genetics-based learning

algorithm,” J. Netw. Comput. Appl., vol. 30, pp. 414–428, 2007.

I. Dikmen, M. T. Birgonul, and S. Han, “Using fuzzy risk assessment to rate cost overrun risk

in international construction projects,” Int. J. Proj. Manag., vol. 25, pp. 494–505, 2007.

M. Takács, “Multilevel Fuzzy Approach to the Risk and Disaster Management,” vol. 7, no. 4,

pp. 91–102, 2010.

V. V. Srinivas, S. Tripathi, A. R. Rao, and R. S. Govindaraju, “Regional flood frequency

analysis by combining self-organizing feature map and fuzzy clustering,” J. Hydrol., vol. 348,

pp. 148–166, 2008.

S. J. Kalayathankal and G. Suresh Singh, “A fuzzy soft flood alarm model,” Math. Comput.

Simul., vol. 80, pp. 887–893, 2010.

T. A. Jilani and S. M. A. Burney, “A refined fuzzy time series model for stock market

forecasting,” Phys. A Stat. Mech. its Appl., vol. 387, pp. 2857–2862, 2008.

M. R. Hassan, “A combination of hidden Markov model and fuzzy model for stock market

forecasting,” Neurocomputing, vol. 72, pp. 3439–3446, 2009.

B. S. Yang, T. Han, and J. L. An, “ART-KOHONEN neural network for fault diagnosis of

rotating machinery,” Mech. Syst. Signal Process., vol. 18, pp. 645–657, 2004.

Y. Wang, Q. Li, M. Chang, H. Chen, and G. Zang, “Research on fault diagnosis expert system

based on the neural network and the fault tree technology,” in Procedia Engineering, 2012,

vol. 31, pp. 1206–1210.

J. Chen and T. C. Huang, “Applying neural networks to on-line updated PID controllers for

nonlinear process control,” J. Process Control, vol. 14, pp. 211–230, 2004.

Y. Song, Z. Chen, and Z. Yuan, “New chaotic PSO-based neural network predictive control

for nonlinear process,” IEEE Trans. Neural Networks, vol. 18, pp. 595–600, 2007.

S. S. Chiddarwar and N. Ramesh Babu, “Comparison of RBF and MLP neural networks to

solve inverse kinematic problem for 6R serial robot by a fusion approach,” Eng. Appl. Artif.

Intell., vol. 23, pp. 1083–1092, 2010.

A. T. Hasan, N. Ismail, A. M. S. Hamouda, I. Aris, M. H. Marhaban, and H. M. A. A. Al-

Assadi, “Artificial neural network-based kinematics Jacobian solution for serial manipulator

passing through singular configurations,” Adv. Eng. Softw., vol. 41, pp. 359–367, 2010.

R. Perfetti and E. Ricci, “Analog neural network for support vector machine learning.,” IEEE

transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 17.

pp. 1085–1091, 2006.

M. Jändel, “A neural support vector machine.,” Neural Netw., vol. 23, pp. 607–613, 2010.

A. Subasi and E. Ercelebi, “Classification of EEG signals using neural network and logistic

regression,” Comput Methods Programs Biomed, vol. 78, pp. 87–99, 2005.

S. M. Jadhav, S. L. Nalbalwar, and A. Ghatol, “Artificial Neural Network based cardiac

arrhythmia classification using ECG signal data,” in Electronics and Information Engineering

ICEIE 2010 International Conference On, 2010, vol. 1, pp. V1–228 –V1–231.

S. N. Huang, K. K. Tan, and T. H. Lee, “Further result on a dynamic recurrent neural-networkbased

adaptive observer for a class of nonlinear systems,” Automatica, vol. 41, pp. 2161–2162,

E. Artyomov and O. Yadid-Pecht, “Modified high-order neural network for invariant pattern

recognition,” Pattern Recognit. Lett., vol. 26, pp. 843–851, 2005.

S. L. Phung and A. Bouzerdoum, “A pyramidal neural network for visual pattern recognition,”

IEEE Trans. Neural Networks, vol. 18, pp. 329–343, 2007.

M. a Mazurowski, P. a Habas, J. M. Zurada, J. Y. Lo, J. a Baker, and G. D. Tourassi, “Training

neural network classifiers for medical decision making: the effects of imbalanced datasets on

classification performance.,” Neural Netw., vol. 21, pp. 427–36, 2008.

F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl, and J. Havel, “Artificial

neural networks in medical diagnosis,” J. Appl. Biomed., vol. 11, pp. 47–58, 2013.

K. Movagharnejad and M. Nikzad, “Modeling of tomato drying using artificial neural

network,” Comput. Electron. Agric., vol. 59, pp. 78–85, 2007.

L. Yu, K. K. Lai, and S. Wang, “Multistage RBF neural network ensemble learning for

exchange rates forecasting,” in Neurocomputing, 2008, vol. 71, pp. 3295–3302.

A. A. Philip, “Artificial Neural Network Model for Forecasting Foreign Exchange Rate,” Int.

J. Serv. Oper. Manag., vol. 1, pp. 110–118, 2011.

J. Ahmed, M. N. Jafri, J. Ahmad, and M. I. Khan, “Design and Implementation of a Neural

Network for Real-Time Object Tracking,” no. 6, pp. 1829–1832, 2007.

L. I. Perlovsky and R. W. Deming, “Neural networks for improved tracking,” IEEE Trans.

Neural Networks, vol. 18, pp. 1854–1857, 2007.

R. P. V. G. . Prasad, K. R. Sudha, S. P. Rama, and S. N. S. V. S. . Ramesh, “Software Effort

Estimation using Radial Basis and Generalized Regression Neural Networks,” J. Comput., vol.

, pp. 87–92, 2010.

I. Attarzadeh, A. Mehranzadeh, and A. Barati, “Proposing an Enhanced Artificial Neural

Network Prediction Model to Improve the Accuracy in Software Effort Estimation,” Fourth

Int. Conf. Comput. Intell., pp. 167–172, 2012.

F. Shahraki, M. A. A. Fanaei, and A. R. R. Arjomandzadeh, “Adaptive System Control with

PID Neural Networks,” Chem. Eng. Trans., vol. 17, pp. 1395–1400, 2009.

H. Hu, L. Xu, and R. Wei, “Nonlinear adaptive neuro-PID controller design for greenhouse

environment based on RBF network,” in Proceedings of the International Joint Conference on

Neural Networks, 2010.

I. Engedy and G. Horvath, “Artificial neural network based mobile robot navigation,” in 2009

IEEE International Symposium on Intelligent Signal Processing, 2009, pp. 241–246.

P. Benavidez and M. Jamshidi, “Mobile robot navigation and target tracking system,” 2011 6th

Int. Conf. Syst. Syst. Eng., pp. 299–304, 2011.

T. Sun, H. Pei, Y. Pan, H. Zhou, and C. Zhang, “Neural network-based sliding mode adaptive

control for robot manipulators,” Neurocomputing, vol. 74, pp. 2377–2384, 2011.

H. Liu and T. Zhang, “Neural network-based robust finite-time control for robotic

manipulators considering actuator dynamics,” Robot. Comput. Integr. Manuf., vol. 29, pp.

–308, 2013.

T. Dierks and S. Jagannathan, “Output feedback control of a quadrotor UAV using neural

networks.,” IEEE Trans. Neural Netw., vol. 21, no. 1, pp. 50–66, Jan. 2010.

M. Ö. Efe and S. Member, “Neural Network Assisted Computationally Simple PID Control of

a Quadrotor UAV,” IEEE Trans. Ind. Informatics, vol. 7, no. 2, pp. 354–361, 2011.

A. Rahideh, A. H. Bajodah, and M. H. Shaheed, “Real time adaptive nonlinear model

inversion control of a twin rotor MIMO system using neural networks,” Eng. Appl. Artif.

Intell., vol. 25, pp. 1289–1297, 2012.

B. Shahzad, “Selection of Suitable Evaluation Function Based on Win / Draw Parameter in

Othello,” pp. 802–806, 2012.

Y. Al-Ohali, B. Shahzad, and L. Alssum, “Selection of Efficient Evaluation Function for

Othello,” in CONTECSI - International Conference on Information Systems and Technology

Management, 2012.

J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational

Approach to Learning and Machine Intelligence. 1997, p. 614.

B. Dixon, “Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability:

A GIS-based sensitivity analysis,” J. Hydrol., vol. 309, pp. 17–38, 2005.

J. Shiri and O. Kisi, “Short-term and long-term streamflow forecasting using a wavelet and

neuro-fuzzy conjunction model,” J. Hydrol., vol. 394, pp. 486–493, 2010.

S. Chavan, K. Shah, N. Dave, S. Mukherjee, A. Abraham, and S. Sanyal, “Adaptive neurofuzzy

intrusion detection systems,” Int. Conf. Inf. Technol. Coding Comput. 2004.

Proceedings. ITCC 2004., vol. 1, 2004.

A. N. Toosi and M. Kahani, “A new approach to intrusion detection based on an evolutionary

soft computing model using neuro-fuzzy classifiers,” Comput. Commun., vol. 30, pp. 2201–

, 2007.

Y. J. Zhang, T. Y. Chai, H. Wang, J. Fu, L. Y. Zhang, and Y. G. Wang, “An Adaptive

Generalized Predictive Control Method for Nonlinear Systems Based on ANFIS and Multiple

Models,” Ieee Trans. Fuzzy Syst., vol. 18, pp. 1070–1082, 2010.

Y. Zhang, T. Chai, and H. Wang, “A nonlinear control method based on ANFIS and multiple

models for a class of SISO nonlinear systems and its application.,” IEEE Trans. Neural Netw.,

vol. 22, pp. 1783–95, 2011.

M. Khezri, M. Jahed, and N. Sadati, “Neuro-fuzzy surface EMG pattern recognition for

multifunctional hand prosthesis control,” in IEEE International Symposium on Industrial

Electronics, 2007, pp. 269–274.

Y. H. Lin and M. S. Tsai, “Application of neuro-fuzzy pattern recognition for Non-intrusive

Appliance Load Monitoring in electricity energy conservation,” in IEEE International

Conference on Fuzzy Systems, 2012.

I. Güler and E. D. Ubeyli, “Adaptive neuro-fuzzy inference system for classification of EEG

signals using wavelet coefficients.,” J. Neurosci. Methods, vol. 148, pp. 113–121, 2005.

W. Y. Hsu, “EEG-based motor imagery classification using neuro-fuzzy prediction and

wavelet fractal features,” J. Neurosci. Methods, vol. 189, pp. 295–302, 2010.

F. Samadzadegan, A. Azizi, M. Hahn, and C. Lucas, “Automatic 3D object recognition and

reconstruction based on neuro-fuzzy modelling,” ISPRS J. Photogramm. Remote Sens., vol.

, pp. 255–277, 2005.

J. Y. Kim, M. Kim, S. Lee, J. Oh, S. Oh, and H. J. Yoo, “Real-time object recognition with

neuro-fuzzy controlled workload-aware task pipelining,” IEEE Micro, vol. 29, pp. 28–43,

A. Rezoug, S. Boudoua, and F. Hamerlain, “Fuzzy Logic Control for Manipulator Robot

actuated by Pneumatic Artificial Muscles.,” in Journal of electrical systems Special, 2009, vol.

, pp. 1–6.

L. D. Khoa, D. Q. Truong, and K. K. Ahn, “Synchronization controller for a 3-R planar

parallel pneumatic artificial muscle (PAM) robot using modified ANFIS algorithm,”

Mechatronics, vol. 23, pp. 462–479, 2013.

S. K. Pradhan, D. R. Parhi, and A. K. Panda, “Neuro-fuzzy technique for navigation of

multiple mobile robots,” Fuzzy Optim. Decis. Mak., vol. 5, pp. 255–288, 2006.

W. Budiharto, A. Jazidie, and D. Purwanto, “Indoor Navigation Using Adaptive Neuro Fuzzy

Controller for Servant Robot,” Comput. Eng. Appl. (ICCEA), 2010 Second Int. Conf., vol. 1,

N. Kannathal, C. M. Lim, U. Rajendra Acharya, and P. K. Sadasivan, “Cardiac state diagnosis

using adaptive neuro-fuzzy technique,” Med. Eng. Phys., vol. 28, pp. 809–815, 2006.

K. Salahshoor, M. Kordestani, and M. S. Khoshro, “Fault detection and diagnosis of an

industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive

neuro-fuzzy inference system) classifiers,” Energy, vol. 35, pp. 5472–5482, 2010.

C. K. Lau, K. Ghosh, M. A. Hussain, and C. R. Che Hassan, “Fault diagnosis of Tennessee

Eastman process with multi-scale PCA and ANFIS,” Chemom. Intell. Lab. Syst., vol. 120, pp.

–14, 2013.

T. Orlowska-Kowalska, M. Dybkowski, and K. Szabat, “Adaptive neuro-fuzzy control of the

sensorless induction motor drive system,” in EPE-PEMC 2006: 12th International Power

Electronics and Motion Control Conference, Proceedings, 2007, pp. 1836–1841.

T. Orlowska-Kowalska, M. Dybkowski, and K. Szabat, “Adaptive sliding-mode neuro-fuzzy

control of the two-mass induction motor drive without mechanical sensors,” in IEEE

Transactions on Industrial Electronics, 2010, vol. 57, pp. 553–564.

X. Huang, D. Ho, J. Ren, and L. F. Capretz, “Improving the COCOMO model using a neurofuzzy

approach,” Appl. Soft Comput. J., vol. 7, pp. 29–40, 2007.

J. Wong, D. Ho, and L. F. Capretz, “An investigation of using neuro-fuzzy with software size

estimation,” in Proceedings - International Conference on Software Engineering, 2009, pp.

–58.

Z. Liu, T. Li, and F. Xiong, “An improved ANFIS method and its application on agricultural

information degree measurement,” in Proceedings - 2008 International Conference on

MultiMedia and Information Technology, MMIT 2008, 2008, pp. 78–81.

Q. Sheng, Z. Qing, X. Z. Gao, and Y. Shuanghe, “ANFIS controller for double inverted

pendulum,” in IEEE International Conference on Industrial Informatics (INDIN), 2008, pp.

–480.

R. C. Tatikonda, V. P. Battula, and V. Kumar, “Control of inverted pendulum using Adaptive

Neuro Fuzzy Inference Structure (ANFIS),” in ISCAS 2010 - 2010 IEEE International

Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, 2010, pp. 1348–

E. Abbasi and A. Abouec, “Stock Price Forecast by Using Neuro-Fuzzy Inference System,”

Int. J. Soc. Hum. Sci., vol. 2, pp. 631–634, 2008.

C. F. Liu, C. Y. Yeh, and S. J. Lee, “Application of type-2 neuro-fuzzy modeling in stock price

prediction,” Appl. Soft Comput. J., vol. 12, pp. 1348–1358, 2012.

S. Soyguder and H. Alli, “An expert system for the humidity and temperature control in

HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach,” Energy

Build., vol. 41, pp. 814–822, 2009.

S. F. Toha and M. O. Tokhi, “Dynamic nonlinear inverse-model based control of a twin rotor

system using adaptive neuro-fuzzy inference system,” in EMS 2009 - UKSim 3rd European

Modelling Symposium on Computer Modelling and Simulation, 2009, pp. 107–111.

S. F. Toha and M. O. Tokhi, “ANFIS modelling of a twin rotor system using particle swarm

optimisation and RLS,” in 2010 IEEE 9th International Conference on Cybernetic Intelligent

Systems, CIS 2010, 2010.

T. Y. Pai, T. J. Wan, S. T. Hsu, T. C. Chang, Y. P. Tsai, C. Y. Lin, H. C. Su, and L. F. Yu,

“Using fuzzy inference system to improve neural network for predicting hospital wastewater

treatment plant effluent,” Comput. Chem. Eng., vol. 33, pp. 1272–1278, 2009.

J. Wan, M. Huang, Y. Ma, W. Guo, Y. Wang, H. Zhang, W. Li, and X. Sun, “Prediction of

effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy

inference system,” Appl. Soft Comput. J., vol. 11, pp. 3238–3246, 2011.

M. Hayati, A. Rezaei, M. Seifi, and A. Naderi, “Modeling and simulation of combinational

CMOS logic circuits by ANFIS,” Microelectronics J., vol. 41, pp. 381–387, 2010.

S. Kurnaz, O. Cetin, and O. Kaynak, “Adaptive neuro-fuzzy inference system based

autonomous flight control of unmanned air vehicles,” Expert Syst. Appl., vol. 37, pp. 1229–

, 2010.

M. Aghajarian and K. Kiani, “Inverse Kinematics solution of PUMA 560 robot arm using

ANFIS,” in URAI 2011 - 2011 8th International Conference on Ubiquitous Robots and

Ambient Intelligence, 2011, pp. 574–578.

H. Chaudhary and R. Prasad, “INTELLIGENT INVERSE KINEMATIC CONTROL OF

SCORBOT-ER V PLUS ROBOT MANIPULATOR,” Int. J. Adv. Eng. Technol. IJAET, vol.

Vol. 1, pp. 158–169, 2011.

C. Grosan and A. Abraham, “Rule-Based Expert Systems,” Intell. Syst. Ref. Libr., vol. 17, pp.

–185, 2011.

K. K. Li, G. J. Chen, T. S. Chung, and G. Q. Tang, “Distribution planning using a rule-based

expert system approach,” 2004 IEEE Int. Conf. Electr. Util. Deregulation, Restruct. Power

Technol. Proc., vol. 2, 2004.

L. K. Soh, C. Tsatsoulis, D. Gineris, and C. Bertoia, “ARKTOS: An intelligent system for

SAR sea ice image classification,” IEEE Trans. Geosci. Remote Sens., vol. 42, pp. 229–248,

I. Hatzilygeroudis and J. Prentzas, “Integrating (rules, neural networks) and cases for

knowledge representation and reasoning in expert systems,” Expert Syst. Appl., vol. 27, pp.

–75, 2004.

L. M. A. Valenzuela, J. M. Bentley, and R. D. Lorenz, “Expert system for integrated control

and supervision of dry-end sections of paper machines,” IEEE Trans. Ind. Appl., vol. 40, pp.

–691, 2004.

I. Hatzilygeroudis and J. Prentzas, “Using a hybrid rule-based approach in developing an

intelligent tutoring system with knowledge acquisition and update capabilities,” Expert Syst.

Appl., vol. 26, pp. 477–492, 2004.

M. H. Zadeh and E. Kubica, “Modelling haptic devices using a rule-based expert system,” in

HAVE 2005: IEEE International Workshop on Haptic Audio Visual Environments and their

Applications, 2005, vol. 2005, pp. 83–88.

H. Wang, S. Kwong, Y. Jin, W. Wei, and K. F. Man, “Agent-based evolutionary approach for

interpretable rule-based knowledge extraction,” IEEE Trans. Syst. Man Cybern. Part C Appl.

Rev., vol. 35, pp. 143–155, 2005.

E. Lamma, P. Mello, A. Nanetti, F. Riguzzi, S. Storari, and G. Valastro, “Artificial intelligence

techniques for monitoring dangerous infections.,” IEEE Trans. Inf. Technol. Biomed., vol. 10,

pp. 143–155, 2006.

E. Seto, K. J. Leonard, J. A. Cafazzo, J. Barnsley, C. Masino, and H. J. Ross, “Developing

healthcare rule-based expert systems: Case study of a heart failure telemonitoring system,” Int.

J. Med. Inform., vol. 81, pp. 556–565, 2012.

T. Kurtoglu and I. Y. Tumer, “A Graph-Based Fault Identification and Propagation

Framework for Functional Design of Complex Systems,” Journal of Mechanical Design, vol.

p. 051401, 2008.

J. Qu and L. Liang, “A production rule based expert system for electronic control automatic

transmission fault diagnosis,” in Proceedings - 2009 International Conference on Information

Engineering and Computer Science, ICIECS 2009, 2009.

A. Jain, R. Balasubramanian, S. C. Tripathy, and Y. Kawazoe, “Topological observability

analysis using heuristic rule based expert system,” 2006 IEEE Power Eng. Soc. Gen. Meet.,

S. Peddabachigari, A. Abraham, C. Grosan, and J. Thomas, “Modeling intrusion detection

system using hybrid intelligent systems,” J. Netw. Comput. Appl., vol. 30, pp. 114–132, 2007.

M. V. Butz, “Combining gradient-based with evolutionary online learning: An introduction to

learning classifier systems,” in Proceedings - 7th International Conference on Hybrid

Intelligent Systems, HIS 2007, 2007, pp. 12–17.

Z. J. Zhou, C. H. Hu, J. B. Yang, D. L. Xu, and D. H. Zhou, “Online updating belief rule based

system for pipeline leak detection under expert intervention,” Expert Syst. Appl., vol. 36, pp.

–7709, 2009.

M. Simard, N. Ueffing, P. Isabelle, and R. Kuhn, “Rule-based Translation With Statistical

Phrase-based Post-editing,” ACL 2007 Second Work. Stat. Mach. Transl., pp. 203–206, 2007.

C. Snae and P. Pongcharoen, “Automatic rule-based expert system for English to Thai

transcription,” in Proceedings of the 3rd IASTED International Conference on Advances in

Computer Science and Technology, ACST 2007, 2007, pp. 342–347.

M. Nilsson, J. Van Laere, T. Ziemke, and J. Edlund, “Extracting rules from expert operators to

support situation awareness in maritime surveillance,” in Proceedings of the 11th International

Conference on Information Fusion, FUSION 2008, 2008.

H. Chtourou and M. Haouari, “A two-stage-priority-rule-based algorithm for robust resourceconstrained

project scheduling,” Comput. & Ind. Eng., vol. 55, pp. 183–194, 2008.

J. H. Chen and P. Baldi, “No electron left behind: A rule-based expert system to predict

chemical reactions and reaction mechanisms,” J. Chem. Inf. Model., vol. 49, pp. 2034–2043,

S. K. Sarma and K. R. Singh, “An Expert System for diagnosis of diseases in Rice Plant,” Int.

J. Artif. Intell., vol. 1, pp. 26–31, 2010.

I. Borlea, G. Vuc, D. Jigoria-Oprea, A. Kilyeni, C. Barbulescu, and T. Slavici, “A rule-based

expert system for steady state diagnosis of electrical distribution networks,” in Proceedings of

the Mediterranean Electrotechnical Conference - MELECON, 2010, pp. 142–147.

N. Lai, W. Dong, J. Wang, X. Xiao, and J. Lai, “Application of rule based expert system to

sand control in oil fields,” in Proceedings - 2012 5th International Conference on Intelligent

Computation Technology and Automation, ICICTA 2012, 2012, pp. 110–113.

P. A. Jaques, H. Seffrin, G. Rubi, F. De Morais, C. Ghilardi, I. I. Bittencourt, and S. Isotani,

“Rule-based expert systems to support step-by-step guidance in algebraic problem solving:

The case of the tutor PAT2Math,” Expert Syst. Appl., vol. 40, pp. 5456–5465, 2013.

M. De la Sen, J. J. Miñambres, A. J. Garrido, A. Almansa, and J. C. Soto, “Basic theoretical

results for expert systems. Application to the supervision of adaptation transients in planar

robots,” Artif. Intell., vol. 152, pp. 173–211, 2004.

A. Bouguerra, L. Karlsson, and A. Saffiotti, “Semantic knowledge-based execution monitoring

for mobile robots,” in Proceedings - IEEE International Conference on Robotics and

Automation, 2007, pp. 3693–3698.

H. C. Huang, “Designing a knowledge-based system for strategic planning: A balanced

scorecard perspective,” Expert Syst. Appl., vol. 36, pp. 209–218, 2009.

A. Berrais, “A knowledge-based expert system for earthquake resistant design of reinforced

concrete buildings,” Expert Systems with Applications, vol. 28. pp. 519–530, 2005.

W. Wen, W. K. Wang, and C. H. Wang, “A knowledge-based intelligent decision support

system for national defense budget planning,” Expert Systems with Applications, vol. 28. pp.

–66, 2005.

S. Piramuthu, “Knowledge-based framework for automated dynamic supply chain

configuration,” Eur. J. Oper. Res., vol. 165, pp. 219–230, 2005.

C. Nan, F. Khan, and M. T. Iqbal, “Real-time fault diagnosis using knowledge-based expert

system,” Process Saf. Environ. Prot., vol. 86, pp. 55–71, 2008.

C. G. Siontorou, F. A. Batzias, and V. Tsakiri, “A knowledge-based approach to online fault

diagnosis of FET biosensors,” IEEE Trans. Instrum. Meas., vol. 59, pp. 2345–2364, 2010.

K. Saleem, A. Derhab, J. Al-muhtadi, and B. Shahzad, “Computers in Human Behavior

Human-oriented design of secure Machine-to-Machine communication system for e-

Healthcare society,” Comput. Human Behav., 2014.

D. K. S. Sanjeev Kumar Jha, “Development of knowledge Base Expert System for Natural

treatment of Diabetes disease,” Int. J. Adv. Comput. Sci. Appl., vol. 3, 2012.

W. Wen, “A knowledge-based intelligent electronic commerce system for selling agricultural

products,” Comput. Electron. Agric., vol. 57, pp. 33–46, 2007.

W. Wen, Y. H. Chen, and I. C. Chen, “A knowledge-based decision support system for

measuring enterprise performance,” Knowledge-Based Syst., vol. 21, pp. 148–163, 2008.

W. Leigh, C. J. Frohlich, S. Hornik, R. L. Purvis, and T. L. Roberts, “Trading with a stock

chart heuristic,” IEEE Trans. Syst. Man, Cybern. Part ASystems Humans, vol. 38, pp. 93–104,

M. R. Aniba, S. Siguenza, A. Friedrich, F. Plewniak, O. Poch, A. Marchler-Bauer, and J. D.

Thompson, “Knowledge-based expert systems and a proof-of-concept case study for multiple

sequence alignment construction and analysis,” Briefings in Bioinformatics, vol. 10. pp. 11–

, 2009.

S. Sulaiman, H. Mohamed, M. R. M. Arshad, N. A. Rashid, and U. K. Yusof, “Hajj-QAES: A

knowledge-based expert system to support hajj pilgrims in decision making,” in ICCTD 2009 -

International Conference on Computer Technology and Development, 2009, vol. 1, pp.

–446.

D. M. Oliver, R. D. Fish, M. Winter, C. J. Hodgson, A. L. Heathwaite, and D. R. Chadwick,

“Valuing local knowledge as a source of expert data: Farmer engagement and the design of

decision support systems,” Environ. Model. Softw., vol. 36, pp. 76–85, 2012.

İ. Güler, A. Demirhan, and R. Karakış, “Interpretation of MR images using self-organizing

maps and knowledge-based expert systems,” Digit. Signal Process., vol. 19, no. 4, pp. 668–

, Jul. 2009.

P. de Almeida, “A knowledge-based approach to the iris segmentation problem,” Image Vis.

Comput., vol. 28, pp. 238–245, 2010.

S. Rahman, F. Khan, B. Veitch, and P. Amyotte, “ExpHAZOP+: Knowledge-based expert

system to conduct automated HAZOP analysis,” J. Loss Prev. Process Ind., vol. 22, pp. 373–

, 2009.

M. R. Aniba, O. Poch, A. Marchler-bauer, and J. D. Thompson, “AlexSys: A knowledge-based

expert system for multiple sequence alignment construction and analysis,” Nucleic Acids Res.,

vol. 38, pp. 6338–6349, 2010.

S. Alhawari, L. Karadsheh, A. Nehari Talet, and E. Mansour, “Knowledge-Based Risk

Management framework for Information Technology project,” Int. J. Inf. Manage., vol. 32, pp.

–65, 2012.

I. A. R. Al-Ani, L. M. Sidek, M. N. M. Desa, and N. E. A. Basri, “Knowledge-based Expert

System for Stormwater Management in Malaysia,” Journal of Environmental Science and

Technology, vol. 5. pp. 381–388, 2012.

M. Wooldridge and N. R. Jennings, “Intelligent agents: theory and practice,” The Knowledge

Engineering Review, vol. 10. p. 115, 1995.

A. J. Thomson and I. Willoughby, “A web-based expert system for advising on herbicide use

in Great Britain,” Comput. Electron. Agric., vol. 42, pp. 43–49, 2004.

H. Z. Alibaba and M. B. Özdeniz, “A building elements selection system for architects,” Build.

Environ., vol. 39, pp. 307–316, 2004.

M. A. Shirazi and J. Soroor, “An intelligent agent-based architecture for strategic information

system applications,” Knowledge-Based Syst., vol. 20, pp. 726–735, 2007.

R. S. T. Lee, “iJADE stock advisor: An intelligent agent based stock prediction system using

hybrid RBF recurrent network,” IEEE Trans. Syst. Man, Cybern. Part ASystems Humans., vol.

, pp. 421–427, 2004.

Y. Wang and L. Fang, “Design of an intelligent agent-based supply chain simulation system,”

in Conference Proceedings - IEEE International Conference on Systems, Man and

Cybernetics, 2007, pp. 1836–1841.

I. Smeureanu, G. Ruxanda, A. Diosteanu, C. Delcea, and L. A. Cotfas, “Intelligent agents and

risk based model for supply chain management,” Technological and Economic Development of

Economy, vol. 18. pp. 452–469, 2012.

C. S. J. Dong and A. Srinivasan, “Agent-enabled service-oriented decision support systems,”

Decis. Support Syst., vol. 55, pp. 364–373, 2013.

R. Govindu and R. B. Chinnam, “MASCF: A generic process-centered methodological

framework for analysis and design of multi-agent supply chain systems,” Comput. Ind. Eng.,

vol. 53, pp. 584–609, 2007.

M. Zhang, J. Tang, and J. Fulcher, “Agent-based grid computing,” Stud. Comput. Intell., vol.

, pp. 439–483, 2008.

S. Gao and D. Xu, “Conceptual modeling and development of an intelligent agent-assisted

decision support system for anti-money laundering,” Expert Syst. Appl., vol. 36, pp. 1493–

, 2009.

L. Li, “An intelligent tutoring system based on agent,” 2009.

S. Ke and X. Lu, “Study on intelligent tutoring system based on multi-agents,” in Proceedings

- 2010 6th International Conference on Natural Computation, ICNC 2010, 2010, vol. 6, pp.

–2952.

I. I. Bittencourt, E. Costa, M. Silva, and E. Soares, “A computational model for developing

semantic web-based educational systems,” Knowledge-Based Syst., vol. 22, pp. 302–315,

R. Gowri, S. Kanmani, and T. T. S. Kumar, “Agent Based Adaptive Learning System,” in

trends in computer science, engineering and information technology, 2011, vol. 204, pp. 423–

M. K. Kouluri and R. K. Pandey, “Intelligent agent based micro grid control,” in IAMA 2011 -

2nd International Conference on Intelligent Agent and Multi-Agent Systems, 2011, pp.

–66.