In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has signifi...In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.展开更多
According to the fault characteristic of the imperial smelting process （ISP）, a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault sy...According to the fault characteristic of the imperial smelting process （ISP）, a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.展开更多
Disk scheduling is one of the main responsibilities of Operating System. OS manages hard disk to provide best access time. All major Disk scheduling algorithms incorporate seek time as the only factor for disk schedul...Disk scheduling is one of the main responsibilities of Operating System. OS manages hard disk to provide best access time. All major Disk scheduling algorithms incorporate seek time as the only factor for disk scheduling. The second factor rotational delay is ignored by the existing algorithms. This research paper considers both factors, Seek Time and Rotational Delay to schedule the disk. Our algorithm Fuzzy Disk Scheduling (FDS) looks at the uncertainty associated with scheduling incorporating the two factors. Keeping in view a Fuzzy inference system using If-Then rules is designed to optimize the overall performance of disk drives. Finally we compared the FDS with the other scheduling algorithms.展开更多
Purpose-As far as the treatment of most complex issues in the design is concerned,approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence,particularl...Purpose-As far as the treatment of most complex issues in the design is concerned,approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence,particularly this involves dealing with vagueness,multi-objectivity and good amount of possible solutions.In practical applications,computational techniques have given best results and the research in this field is continuously growing.The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery.The present study involves the construction of such intelligent computational models using different configurations,including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.Design/methodology/approach-On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools,the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction.The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system(ANFIS)models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data.After evaluating the models over three shuffles of data(training set,test set and full set),the performances were compared in order to find the best design for prediction of patient survival after surgery.The construction and implementation of models have been performed using MATLAB simulator.Findings-On applying the hybrid intelligent neuro-fuzzy models with different configurations,the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer.Experimental results and comparison betw展开更多
To achieve high work performance for compliant mechanisms of motion scope,continuous work condition,and high frequency,we propose a new hybrid algorithm that could be applied to multi-objective optimum design.In this ...To achieve high work performance for compliant mechanisms of motion scope,continuous work condition,and high frequency,we propose a new hybrid algorithm that could be applied to multi-objective optimum design.In this investigation,we use the tools of finite element analysis(FEA)for a magnificationmechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements.A poly-algorithm including the Grey-Taguchi method,fuzzy logic system,and adaptive neuro-fuzzy inference system(ANFIS)algorithm,was utilized mainly in this study.The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade.The results are also predicted by employing the tool of ANFIS in MATLAB.In conclusion,the optimal findings obtained:Its magnification is larger than 40 times in comparison with the initial design,the maximum principal stress is 127.89MPa,and the first modal shape frequency obtained 397.45 Hz.Moreover,we found that the outcomes obtained deviation error compared with predicted results of displacement,stress,and frequency are 8.76%,3.6%,and 6.92%,respectively.展开更多
Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unn...Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.展开更多
This study helps to select the length for fuzzy sets in fuzzy time series prediction.In order to examine the effect of intervals and evaluate the efficiency of the proposed algorithm,numerical data of water recharge a...This study helps to select the length for fuzzy sets in fuzzy time series prediction.In order to examine the effect of intervals and evaluate the efficiency of the proposed algorithm,numerical data of water recharge and discharge are considered to predict water table elevation fluctuation(WTEF).Particle swarm optimization(PSO)is an influential tool to handle optimization of multi-model problems,whereas fuzzy logic can handle uncertainty.In this paper,adaptive inertia weights are adopted rather than static inertia weights for PSO,which further improves efficiency of PSO.This modified PSO is termed as adaptive particle swarm optimization(APSO).APSO optimizes the intervals and these intervals are further used to generate fuzzy sets for prediction.The results indicate that the APSO performs better than PSO and genetic algorithm approaches for the same problem.展开更多
In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, n...In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.展开更多
采用基于模糊逻辑推理的半主动控制技术对风机塔筒进行风致振动控制,通过对塔筒结构实时的动力响应进行模糊逻辑推理,利用半主动控制算法调节调频质量阻尼器(Tuned Mass Damper,TMD)的阻尼系数,输出不同的阻尼力,对塔筒结构进行振动控...采用基于模糊逻辑推理的半主动控制技术对风机塔筒进行风致振动控制,通过对塔筒结构实时的动力响应进行模糊逻辑推理,利用半主动控制算法调节调频质量阻尼器(Tuned Mass Damper,TMD)的阻尼系数,输出不同的阻尼力,对塔筒结构进行振动控制。通过Simulink软件进行半主动模糊控制系统仿真,结果表明,基于模糊逻辑推理的半主动控制比传统被动控制的控制效果更好,可以大幅降低塔筒结构顶端的位移响应。展开更多
文摘In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.
基金This work was supported by National 973 Program (No. 2002CB312200)National Natural Science Foundation of PRC (No. 60634020).
文摘According to the fault characteristic of the imperial smelting process （ISP）, a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.
文摘Disk scheduling is one of the main responsibilities of Operating System. OS manages hard disk to provide best access time. All major Disk scheduling algorithms incorporate seek time as the only factor for disk scheduling. The second factor rotational delay is ignored by the existing algorithms. This research paper considers both factors, Seek Time and Rotational Delay to schedule the disk. Our algorithm Fuzzy Disk Scheduling (FDS) looks at the uncertainty associated with scheduling incorporating the two factors. Keeping in view a Fuzzy inference system using If-Then rules is designed to optimize the overall performance of disk drives. Finally we compared the FDS with the other scheduling algorithms.
文摘Purpose-As far as the treatment of most complex issues in the design is concerned,approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence,particularly this involves dealing with vagueness,multi-objectivity and good amount of possible solutions.In practical applications,computational techniques have given best results and the research in this field is continuously growing.The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery.The present study involves the construction of such intelligent computational models using different configurations,including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.Design/methodology/approach-On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools,the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction.The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system(ANFIS)models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data.After evaluating the models over three shuffles of data(training set,test set and full set),the performances were compared in order to find the best design for prediction of patient survival after surgery.The construction and implementation of models have been performed using MATLAB simulator.Findings-On applying the hybrid intelligent neuro-fuzzy models with different configurations,the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer.Experimental results and comparison betw
基金This work is funded by Hung Yen University of Technology and Education and Industrial University of Ho Chi Minh City.
文摘To achieve high work performance for compliant mechanisms of motion scope,continuous work condition,and high frequency,we propose a new hybrid algorithm that could be applied to multi-objective optimum design.In this investigation,we use the tools of finite element analysis(FEA)for a magnificationmechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements.A poly-algorithm including the Grey-Taguchi method,fuzzy logic system,and adaptive neuro-fuzzy inference system(ANFIS)algorithm,was utilized mainly in this study.The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade.The results are also predicted by employing the tool of ANFIS in MATLAB.In conclusion,the optimal findings obtained:Its magnification is larger than 40 times in comparison with the initial design,the maximum principal stress is 127.89MPa,and the first modal shape frequency obtained 397.45 Hz.Moreover,we found that the outcomes obtained deviation error compared with predicted results of displacement,stress,and frequency are 8.76%,3.6%,and 6.92%,respectively.
文摘Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.
文摘This study helps to select the length for fuzzy sets in fuzzy time series prediction.In order to examine the effect of intervals and evaluate the efficiency of the proposed algorithm,numerical data of water recharge and discharge are considered to predict water table elevation fluctuation(WTEF).Particle swarm optimization(PSO)is an influential tool to handle optimization of multi-model problems,whereas fuzzy logic can handle uncertainty.In this paper,adaptive inertia weights are adopted rather than static inertia weights for PSO,which further improves efficiency of PSO.This modified PSO is termed as adaptive particle swarm optimization(APSO).APSO optimizes the intervals and these intervals are further used to generate fuzzy sets for prediction.The results indicate that the APSO performs better than PSO and genetic algorithm approaches for the same problem.
文摘In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.
文摘采用基于模糊逻辑推理的半主动控制技术对风机塔筒进行风致振动控制,通过对塔筒结构实时的动力响应进行模糊逻辑推理,利用半主动控制算法调节调频质量阻尼器(Tuned Mass Damper,TMD)的阻尼系数,输出不同的阻尼力,对塔筒结构进行振动控制。通过Simulink软件进行半主动模糊控制系统仿真,结果表明,基于模糊逻辑推理的半主动控制比传统被动控制的控制效果更好,可以大幅降低塔筒结构顶端的位移响应。
