Purpose-Takagi-Sugeno-Kang(TSK)fuzzy systems are widely used in classification and regression problems.However,when the data size is large and the feature dimensions are high,the optimization performance of the TSK fu...Purpose-Takagi-Sugeno-Kang(TSK)fuzzy systems are widely used in classification and regression problems.However,when the data size is large and the feature dimensions are high,the optimization performance of the TSK fuzzy system is poor,and its generalization capability is insufficient.Therefore,this paper proposes an algorithm based on mini-batch gradient descent(MBGD)and batch normalization(BN)to train TSK fuzzy classifiers.Design/methodology/approach-This study uses the AdaDerivative(A)optimizer to optimize the learning rate,weighted firing level(W)applies different weight factors to various features to calculate the firing level of each rule,and adaptive rule firing level loss(A)and knowledge distillation loss(KD)are proposed to encourage each rule to have different average firing levels,as well as to ensure that the output of the rule consequent is close to the mean.Thereby,the final algorithm TSK-BN-2AWKD is obtained.To evaluate the performance of TSK-BN-2AWKD,experiments were conducted on 15 real datasets.Findings-The experimental results show that A,A,W and KD are effective individually,and the algorithm TSK-BN-2AWKD,after integrating them,can effectively improve the classification performance.Originality/value-This study uses the AdaDerivative optimizer to train TSK fuzzy classifiers.Additionally,adaptive rule firing level loss is proposed to assign different expectations to each rule individually.Finally,KD is proposed to force the outputs of the rule consequent to be close to the mean,solving the narrow vision problem that may exist in TSK fuzzy systems.展开更多
This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which...This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which is updated by the pilot symbols. The proposed approach can trace the variation of channel and it is computationally simple. Its performance is tested via simulations. Results show that it is comparable to that of ideal Minimum Mean-Square-Error （MMSE） method, especially at the low Signal to Noise Ratio （SNR）.展开更多
The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-bas...The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.展开更多
For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algo...For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algorithm is proposed.Compared with the type-1(T1)FLS model,the IT2 TSK FLS method can simultaneously model both intra-and inter-individual uncertainty and further optimize the antecedent and consequent parameters using the EKF to improve forecasting performance further.The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region,under the same conditions.It is also compared with the T1 TSK FLS and IT2 TSK FLS methods with back propagation(BP)and particle swarm optimization(PSO)algorithms,as well as IT2 A2-C0 TSK FLS methods with EKF.The experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to the other FLS methods regarding performance,which demonstrates its effectiveness and application potential.展开更多
In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network（SRCPFWNN） for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Net...In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network（SRCPFWNN） for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network（SRWNN） to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang（TSK） model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory（DACT） and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system.展开更多
基金supported in part by the National Natural Science Foundation of China(No:12271211)the Fujian Alliance of Mathematics(No:2023SXLMMS06)the Open Fund of Digital Fujian Big Data Modeling and Intelligent Computing Institute and the Pre-Research Fund of Jimei University.
文摘Purpose-Takagi-Sugeno-Kang(TSK)fuzzy systems are widely used in classification and regression problems.However,when the data size is large and the feature dimensions are high,the optimization performance of the TSK fuzzy system is poor,and its generalization capability is insufficient.Therefore,this paper proposes an algorithm based on mini-batch gradient descent(MBGD)and batch normalization(BN)to train TSK fuzzy classifiers.Design/methodology/approach-This study uses the AdaDerivative(A)optimizer to optimize the learning rate,weighted firing level(W)applies different weight factors to various features to calculate the firing level of each rule,and adaptive rule firing level loss(A)and knowledge distillation loss(KD)are proposed to encourage each rule to have different average firing levels,as well as to ensure that the output of the rule consequent is close to the mean.Thereby,the final algorithm TSK-BN-2AWKD is obtained.To evaluate the performance of TSK-BN-2AWKD,experiments were conducted on 15 real datasets.Findings-The experimental results show that A,A,W and KD are effective individually,and the algorithm TSK-BN-2AWKD,after integrating them,can effectively improve the classification performance.Originality/value-This study uses the AdaDerivative optimizer to train TSK fuzzy classifiers.Additionally,adaptive rule firing level loss is proposed to assign different expectations to each rule individually.Finally,KD is proposed to force the outputs of the rule consequent to be close to the mean,solving the narrow vision problem that may exist in TSK fuzzy systems.
文摘This paper proposes a TSK fuzzy approach to channel estimation for Orthogonal Frequency Division Multiplexing （OFDM） systems. The information of dispersive fading channel is described by using TSK fuzzy model, which is updated by the pilot symbols. The proposed approach can trace the variation of channel and it is computationally simple. Its performance is tested via simulations. Results show that it is comparable to that of ideal Minimum Mean-Square-Error （MMSE） method, especially at the low Signal to Noise Ratio （SNR）.
基金Research Supporting Project Number(RSP2024R421),King Saud University,Riyadh,Saudi Arabia。
文摘The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.
基金Supported by the Key Project of Natural Science Foundation of Gansu Province(25JRRA150)the Gansu Provincial Natural Science Foundation(23JRRA876).
文摘For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algorithm is proposed.Compared with the type-1(T1)FLS model,the IT2 TSK FLS method can simultaneously model both intra-and inter-individual uncertainty and further optimize the antecedent and consequent parameters using the EKF to improve forecasting performance further.The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region,under the same conditions.It is also compared with the T1 TSK FLS and IT2 TSK FLS methods with back propagation(BP)and particle swarm optimization(PSO)algorithms,as well as IT2 A2-C0 TSK FLS methods with EKF.The experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to the other FLS methods regarding performance,which demonstrates its effectiveness and application potential.
文摘In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network（SRCPFWNN） for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network（SRWNN） to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang（TSK） model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory（DACT） and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system.
