Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8704133
Khairun Nisa Hairol, R. Adnan, A. Samad, Fazlina Ahmat Ruslan
Fish farming industry has become one of the sources of income to people who are involved in business. An entrepreneur in this industry can make more money when the fish pond is equipped with the suitable device. With the existence of such device which capable in controlling the water level, monitor the temperature and so forth; then the entrepreneur has nothing to worry since everything is automated and can be monitored closely by just using a smartphone with Blynk application. This system is designed to control the water level, monitor the water temperature, automatic feeding and automatic water replacement of an aquaculture environment. The user gets to view the live measurement of the water temperature and water level. This study is limited to only a small aquarium where the maximum height is 15cm since it is only a prototype. The Arduino Mega 2560 is used as the microcontroller in this study since its program is easy to change and the language of this microcontroller is more familiar rather than other microcontrollers. This study results in a reliable water level controller which works continuously to regulate the water level to the desired water level. Besides that, this system also capable to feed the fishes automatically every 90 minutes and change the water in the aquarium for every two days. The graph of water level and the water temperature is updated from time to time and the data can be downloaded if the user intended to do so.
养鱼业已经成为从事商业活动的人们的收入来源之一。当鱼塘配备合适的设备时，这个行业的企业家可以赚更多的钱。具有控制水位、监测温度等功能的装置;那么企业家就不用担心了，因为一切都是自动化的，只需使用带有Blynk应用程序的智能手机就可以密切监控。本系统设计用于控制水位、监测水温、自动投料和自动换水的养殖环境。用户可以查看水温和水位的实时测量。本研究仅限于一个最大高度为15厘米的小型水族馆，因为它只是一个原型。本研究使用Arduino Mega 2560作为微控制器，因为它的程序易于更改，并且该微控制器的语言比其他微控制器更熟悉。本研究设计了一种可靠的水位控制器，它可以连续工作，将水位调节到所需的水位。除此之外，该系统还能够每90分钟自动喂鱼，每两天自动换水。水位和水温的图表会不时更新，如果用户想下载数据，可以下载。
{"title":"Aquaculture Monitoring System using Arduino Mega for Automated Fish Pond System Application","authors":"Khairun Nisa Hairol, R. Adnan, A. Samad, Fazlina Ahmat Ruslan","doi":"10.1109/SPC.2018.8704133","DOIUrl":"https://doi.org/10.1109/SPC.2018.8704133","url":null,"abstract":"Fish farming industry has become one of the sources of income to people who are involved in business. An entrepreneur in this industry can make more money when the fish pond is equipped with the suitable device. With the existence of such device which capable in controlling the water level, monitor the temperature and so forth; then the entrepreneur has nothing to worry since everything is automated and can be monitored closely by just using a smartphone with Blynk application. This system is designed to control the water level, monitor the water temperature, automatic feeding and automatic water replacement of an aquaculture environment. The user gets to view the live measurement of the water temperature and water level. This study is limited to only a small aquarium where the maximum height is 15cm since it is only a prototype. The Arduino Mega 2560 is used as the microcontroller in this study since its program is easy to change and the language of this microcontroller is more familiar rather than other microcontrollers. This study results in a reliable water level controller which works continuously to regulate the water level to the desired water level. Besides that, this system also capable to feed the fishes automatically every 90 minutes and change the water in the aquarium for every two days. The graph of water level and the water temperature is updated from time to time and the data can be downloaded if the user intended to do so.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8704158
Mohamed. A. M. Osman, B. Kamaruddin, M. Ramasamy
This work presents a comparison between centralized and decentralized control for cooperative distributed model predictive control scheme for linear systems. The presented scheme compensates the lack of accuracy and computational efficiency of model based technique for the geographically distributed controllers.The advantages of this control strategy is the ability to converges to the centralized optimal solution (Pareto optimum) and generate a control feedback that able to insure the closed-loop stability. A process of two subsystems is presented for simplicity, however, the strategy has the flexibility to be extended for any finite number of subsystems.
{"title":"Optimality and Stability of Cooperative Distributed Model Predictive Control in Large–scale Plant","authors":"Mohamed. A. M. Osman, B. Kamaruddin, M. Ramasamy","doi":"10.1109/SPC.2018.8704158","DOIUrl":"https://doi.org/10.1109/SPC.2018.8704158","url":null,"abstract":"This work presents a comparison between centralized and decentralized control for cooperative distributed model predictive control scheme for linear systems. The presented scheme compensates the lack of accuracy and computational efficiency of model based technique for the geographically distributed controllers.The advantages of this control strategy is the ability to converges to the centralized optimal solution (Pareto optimum) and generate a control feedback that able to insure the closed-loop stability. A process of two subsystems is presented for simplicity, however, the strategy has the flexibility to be extended for any finite number of subsystems.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133690320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8703980
E. Arif, J. Hossen, G. Murthy, Tajrian Mollick, T. Bhuvaneswari, C. Venkataseshaiah
Renewable energy is suitable environmental, energy resources for the new generation. The solar energy system plays a dominant role in electric power generation. The solar panel tracking depends on the sun create the power. The solar panel tracker system among the important element in the PV system, obtaining greatest efficiency several types of the controller has been suggested in the composition to improve the performance. This solar panel tracking process has been used the neuro-fuzzy controller for superior performance. Because in this system neuro-fuzzy controller reduces the sun data error processing and detects the uncertainty weather. Its performance has been studied by using Mat lab simulation. In this system, then compare to "fuzzy logic controller" the proposed "neuro-fuzzy controller" is found to provide better performance.
{"title":"Performance Comparisons of Fuzzy Logic and Neuro-Fuzzy Controller Design in Solar Panel Tracking Systems","authors":"E. Arif, J. Hossen, G. Murthy, Tajrian Mollick, T. Bhuvaneswari, C. Venkataseshaiah","doi":"10.1109/SPC.2018.8703980","DOIUrl":"https://doi.org/10.1109/SPC.2018.8703980","url":null,"abstract":"Renewable energy is suitable environmental, energy resources for the new generation. The solar energy system plays a dominant role in electric power generation. The solar panel tracking depends on the sun create the power. The solar panel tracker system among the important element in the PV system, obtaining greatest efficiency several types of the controller has been suggested in the composition to improve the performance. This solar panel tracking process has been used the neuro-fuzzy controller for superior performance. Because in this system neuro-fuzzy controller reduces the sun data error processing and detects the uncertainty weather. Its performance has been studied by using Mat lab simulation. In this system, then compare to \"fuzzy logic controller\" the proposed \"neuro-fuzzy controller\" is found to provide better performance.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"15 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131804946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8703975
N. Ismail, Hairul Ariffin, M. Rahiman, M. Taib, N. A. Ali, S. N. Tajuddin
This paper presents an attemption to empirically assess statistical learning model Boolean Support Vector Machines (BSVM) to the problem of agarwood oil quality categorization. The modelling starts with data pre-processing of seven significant chemical compounds of agarwood oil, from high and low qualities. During this stage, the data was randomized, normalized and divided into training and testing parts. 80% of the training part was induced as examples and create the maximum margin hyperplane to separates high and low groups in a binary setting and build the model. Another 20% of testing part was used to validate the developed model. MATLAB software version R2016a was used to perform all the analysis. The result obtained a good model utilizing SVM in classifying agarwood oil significant volatile compound quality. The model achieved minimum of 80 % for precision, confusing matrix, accuracy, sensitivity and specificity. The finding in this study will benefit further work and application for agarwood oil research area especially its classification in quality of agarwood oil and many others.
{"title":"Statistical Learning BSVM Model to the Problem of Agarwood Oil Quality Categorization","authors":"N. Ismail, Hairul Ariffin, M. Rahiman, M. Taib, N. A. Ali, S. N. Tajuddin","doi":"10.1109/SPC.2018.8703975","DOIUrl":"https://doi.org/10.1109/SPC.2018.8703975","url":null,"abstract":"This paper presents an attemption to empirically assess statistical learning model Boolean Support Vector Machines (BSVM) to the problem of agarwood oil quality categorization. The modelling starts with data pre-processing of seven significant chemical compounds of agarwood oil, from high and low qualities. During this stage, the data was randomized, normalized and divided into training and testing parts. 80% of the training part was induced as examples and create the maximum margin hyperplane to separates high and low groups in a binary setting and build the model. Another 20% of testing part was used to validate the developed model. MATLAB software version R2016a was used to perform all the analysis. The result obtained a good model utilizing SVM in classifying agarwood oil significant volatile compound quality. The model achieved minimum of 80 % for precision, confusing matrix, accuracy, sensitivity and specificity. The finding in this study will benefit further work and application for agarwood oil research area especially its classification in quality of agarwood oil and many others.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129038255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8703979
P. A. Hosseinabadi, Ali Soltani Sharif Abadi, S. Mekhilef
One of the unavoidable issues with various real-time control systems is existence of the external disturbances and uncertainties which is usually unavailable. To deal with these disturbances and uncertainties, various robust control methods have been introduced in the literature such as Sliding Mode Control (SMC), adaptive control method and so on. This paper proposes a novel Adaptive Terminal SMC (ATSMC) method to control the hyper-chaotic 4-order system within a finite-time by utilizing only one control input. The dynamical model of the hyper-chaotic 4-order system is subjected to the mismatched external disturbances and uncertainties which are bounded, but these bounds are unavailable. The adaptive concept is employed to approximate the upper bounds of these unknown mismatched external disturbances and uncertainties within a finite-time and their estimations are used in the control input. The robust controller is designed by utilizing Lyapunov stability theory. The key features of the designed controller are robustness against all mismatched uncertainties and external disturbances, and providing stability within a finite-time. Finally, a numerical simulation is performed in Simulink/MATLAB to verify the effectiveness of the designed controller to suppress the chaotic oscillations. The numerical simulation results reveal that the finite-time stability is achieved accurately as soon as the controller is introduced.
{"title":"Adaptive Terminal Sliding Mode Control of Hyper-Chaotic Uncertain 4-Order system with One Control Input","authors":"P. A. Hosseinabadi, Ali Soltani Sharif Abadi, S. Mekhilef","doi":"10.1109/SPC.2018.8703979","DOIUrl":"https://doi.org/10.1109/SPC.2018.8703979","url":null,"abstract":"One of the unavoidable issues with various real-time control systems is existence of the external disturbances and uncertainties which is usually unavailable. To deal with these disturbances and uncertainties, various robust control methods have been introduced in the literature such as Sliding Mode Control (SMC), adaptive control method and so on. This paper proposes a novel Adaptive Terminal SMC (ATSMC) method to control the hyper-chaotic 4-order system within a finite-time by utilizing only one control input. The dynamical model of the hyper-chaotic 4-order system is subjected to the mismatched external disturbances and uncertainties which are bounded, but these bounds are unavailable. The adaptive concept is employed to approximate the upper bounds of these unknown mismatched external disturbances and uncertainties within a finite-time and their estimations are used in the control input. The robust controller is designed by utilizing Lyapunov stability theory. The key features of the designed controller are robustness against all mismatched uncertainties and external disturbances, and providing stability within a finite-time. Finally, a numerical simulation is performed in Simulink/MATLAB to verify the effectiveness of the designed controller to suppress the chaotic oscillations. The numerical simulation results reveal that the finite-time stability is achieved accurately as soon as the controller is introduced.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129326401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8704128
Ahmad Sabry Mohamad, Nur Syahirah Abdul Halim, Muhammad Noor Nordin, Roszymah Hamzah, J. Sathar
Image processing technique has been used to produce automated detection of Sickle Cell Anemia. A Laplacian of Gaussian (LoG) edge detection algorithm computed to detect sickle cells diseases at the early stage in diagnosing patient. A MATLAB software able to demonstrate the abnormalities of the human Red Blood Cell (RBC) in the single shapes and quantities of sickle cells present in each dataset. A data samples of sickle cells from government Ampang Hospital has contributed this study to validate the results.
{"title":"Automated Detection of Human RBC in Diagnosing Sickle Cell Anemia with Laplacian of Gaussian Filter","authors":"Ahmad Sabry Mohamad, Nur Syahirah Abdul Halim, Muhammad Noor Nordin, Roszymah Hamzah, J. Sathar","doi":"10.1109/SPC.2018.8704128","DOIUrl":"https://doi.org/10.1109/SPC.2018.8704128","url":null,"abstract":"Image processing technique has been used to produce automated detection of Sickle Cell Anemia. A Laplacian of Gaussian (LoG) edge detection algorithm computed to detect sickle cells diseases at the early stage in diagnosing patient. A MATLAB software able to demonstrate the abnormalities of the human Red Blood Cell (RBC) in the single shapes and quantities of sickle cells present in each dataset. A data samples of sickle cells from government Ampang Hospital has contributed this study to validate the results.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121520533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8704162
Erliana Samsuria, Y. M. Sam, L. Ramli
This paper proposes an optimal control technique of an active suspension system using two degrees of freedom quarter car model. The main purpose of the study is to analyse the effectiveness of state feedback controllers based on Linear Quadratic Regulator (LQR) and Sliding Mode Control (SMC) which are optimised based on Particle Swarm Optimization (PSO) algorithm utilisation of active suspension system. The controllers are designed to improve a ride comfort while maintaining a restriction of suspension travel and wheel deflection subjected to the road disturbances. The performances of SMC based-PSO controller is compared to with the LQR based-PSO controller and the existing conventional suspension system based on the road profile that the car will pass through. To evaluate the effectiveness of the proposed controller, simulations are carried out and tested under the double bump road input profile. The results clearly show that the SMC approach outperforms the LQR and conventional suspension system in achieving a better ride comfort. Simulation by (Simulink Matlab) is carried out to illustrate system control and performances.
{"title":"Active Suspension Control by Using Linear Quadratic Regulator and Sliding Mode Control Techniques with Optimisation","authors":"Erliana Samsuria, Y. M. Sam, L. Ramli","doi":"10.1109/SPC.2018.8704162","DOIUrl":"https://doi.org/10.1109/SPC.2018.8704162","url":null,"abstract":"This paper proposes an optimal control technique of an active suspension system using two degrees of freedom quarter car model. The main purpose of the study is to analyse the effectiveness of state feedback controllers based on Linear Quadratic Regulator (LQR) and Sliding Mode Control (SMC) which are optimised based on Particle Swarm Optimization (PSO) algorithm utilisation of active suspension system. The controllers are designed to improve a ride comfort while maintaining a restriction of suspension travel and wheel deflection subjected to the road disturbances. The performances of SMC based-PSO controller is compared to with the LQR based-PSO controller and the existing conventional suspension system based on the road profile that the car will pass through. To evaluate the effectiveness of the proposed controller, simulations are carried out and tested under the double bump road input profile. The results clearly show that the SMC approach outperforms the LQR and conventional suspension system in achieving a better ride comfort. Simulation by (Simulink Matlab) is carried out to illustrate system control and performances.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121705732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/SPC.2018.8704145
Musab A. M. Ali, Shahad Al-Youif, M. N. Mohammed, Omar Ismael Al-Sanjary, M. I. Abdullah
This paper shows a SumoBot, a set of processorindependent robotics instructions, which empowers instructors to compose robotics code fit for winning on any equipment. Enables educators to share and access educational programs and substance, in the classroom. The outline and ideas of the SumoBot framework will displayed, alongside beginning investigations for information logging and experimentation. A usage of the coding in BASIC Stamp Editor will then be shown by a gathering of usually used instructive robots. The BASIC Stamp Editor empowers understudy to compose widespread mechanical technology programs that enable instructive apply autonomy to break free of current imperatives through crossstage coordinated effort and sharing of educational programs and learning. the instructive market are being focuesd to Robotics systems frameworks as learning devices for an extensive variety of subjects, particularly inside the Science, Technology, Engineering, and Mathematics (STEM) disciplines. The robotics consist variety and inherent leads prompts blended improvement conditions where programs limited to the first robot equipment whereupon they to developed.
{"title":"Design and Implement SumoBot for Classroom Teaching","authors":"Musab A. M. Ali, Shahad Al-Youif, M. N. Mohammed, Omar Ismael Al-Sanjary, M. I. Abdullah","doi":"10.1109/SPC.2018.8704145","DOIUrl":"https://doi.org/10.1109/SPC.2018.8704145","url":null,"abstract":"This paper shows a SumoBot, a set of processorindependent robotics instructions, which empowers instructors to compose robotics code fit for winning on any equipment. Enables educators to share and access educational programs and substance, in the classroom. The outline and ideas of the SumoBot framework will displayed, alongside beginning investigations for information logging and experimentation. A usage of the coding in BASIC Stamp Editor will then be shown by a gathering of usually used instructive robots. The BASIC Stamp Editor empowers understudy to compose widespread mechanical technology programs that enable instructive apply autonomy to break free of current imperatives through crossstage coordinated effort and sharing of educational programs and learning. the instructive market are being focuesd to Robotics systems frameworks as learning devices for an extensive variety of subjects, particularly inside the Science, Technology, Engineering, and Mathematics (STEM) disciplines. The robotics consist variety and inherent leads prompts blended improvement conditions where programs limited to the first robot equipment whereupon they to developed.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-02DOI: 10.1109/SPC.2018.8704148
S. Wong, Keem Siah Yap, Chin Hooi Tan
Regression analysis is one of the most popular methods of estimation or forecasting. For someone who is the non-domain expert to understand how the estimation decision is made, clarity and transparency of the regression model is required to reveal knowledge and information that evaluates the functional relationship between two objects, i.e., the independent and dependent objects the system represents. Hence, this paper presents the hybridization of Genetic Algorithm (GA) and Fuzzy Inference System (FIS)-based computational intelligence systems for tackling data regression problem (hereinafter denoted as GA-FIS-RG). With this regard, GA-FIS-RG first defines the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then GA serves as an optimization tool to construct the best combination of rules in fuzzy inference system. For performance evaluations, we demonstrate the interpretability and applicability of GA-FIS-RG to data regression problems, i.e., the Santa-Fe Series-E and Auto MPG.
{"title":"Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression","authors":"S. Wong, Keem Siah Yap, Chin Hooi Tan","doi":"10.1109/SPC.2018.8704148","DOIUrl":"https://doi.org/10.1109/SPC.2018.8704148","url":null,"abstract":"Regression analysis is one of the most popular methods of estimation or forecasting. For someone who is the non-domain expert to understand how the estimation decision is made, clarity and transparency of the regression model is required to reveal knowledge and information that evaluates the functional relationship between two objects, i.e., the independent and dependent objects the system represents. Hence, this paper presents the hybridization of Genetic Algorithm (GA) and Fuzzy Inference System (FIS)-based computational intelligence systems for tackling data regression problem (hereinafter denoted as GA-FIS-RG). With this regard, GA-FIS-RG first defines the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then GA serves as an optimization tool to construct the best combination of rules in fuzzy inference system. For performance evaluations, we demonstrate the interpretability and applicability of GA-FIS-RG to data regression problems, i.e., the Santa-Fe Series-E and Auto MPG.","PeriodicalId":432464,"journal":{"name":"2018 IEEE Conference on Systems, Process and Control (ICSPC)","volume":"4179 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127569282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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