A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant inf...A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.展开更多
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t...In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.展开更多
This work deals with the nonlinear control of a marine diesel engine by use of a robust intelligent control strategy based on cerebellar model articulation controller （CMAC）. A mathematical model of diesel engine pr...This work deals with the nonlinear control of a marine diesel engine by use of a robust intelligent control strategy based on cerebellar model articulation controller （CMAC）. A mathematical model of diesel engine propulsion system is presented. In order to increase the accuracy of dynamical speed, the mathematical model of engagement process based on the law of energy conservation is proposed. Then, a robust cerebellar model articulation controller is proposed for uncertain nonlinear systems. The concept of active disturbance rejection control （ADRC） is adopted so that the proposed controller has more robustness against uncertainties. Finally, the proposed controller is applied to engine speed control system. Both the model of the diesel engine propulsion system and of the control law are validated by a virtual detailed simulation environment. The prediction capability of the model and the control efficiency are clearly shown.展开更多
Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research...Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang(FCMACBYY)learning,which is referred to as FCMAC-EBYY,to achieve a synergetic development in the search for optimal fuzzy sets and connection weights.Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training,which involves a large searching space due to complex connections as well as real values.The methodology employed by FCMACEBYY is coevolution,in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled.The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.展开更多
基金The National Natural Science Foundation of China(No.60443007,50390063).
文摘A principal component analysis-cerebellar model articulation controller （PCA-CMAC） model is proposed for machine performance degradation assessment.PCA is used to feature selection,which eliminates the redundant information among the features from the sensor signals and reduces the dimension of the input to CMAC.CMAC is used to assess degradation states quantitatively based on its local generalization ability.The implementation of the model is presented and the model is applied in a drilling machine to assess the states of the cutting tool. The results show that the model can assess the wear states quantitatively based on the normal state of the cutting tool.The influence of the quantization parameter g and the generalization parameter r in the CMAC model on the assessment results is analyzed.If g is larger,the generalization ability is better,but the difference of degradation states is not obvious.If r is smaller,the different states are distinct,but memory requirements for storing the weights are larger.The principle for selecting two parameters is that the memory storing the weights should be small while the degradation states should be easily distinguished.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.
基金the National Natural Science Foundation of China(No.51179102)the China Postdoctoral Science Foundation(No.20110490716)
文摘This work deals with the nonlinear control of a marine diesel engine by use of a robust intelligent control strategy based on cerebellar model articulation controller （CMAC）. A mathematical model of diesel engine propulsion system is presented. In order to increase the accuracy of dynamical speed, the mathematical model of engagement process based on the law of energy conservation is proposed. Then, a robust cerebellar model articulation controller is proposed for uncertain nonlinear systems. The concept of active disturbance rejection control （ADRC） is adopted so that the proposed controller has more robustness against uncertainties. Finally, the proposed controller is applied to engine speed control system. Both the model of the diesel engine propulsion system and of the control law are validated by a virtual detailed simulation environment. The prediction capability of the model and the control efficiency are clearly shown.
基金This research was supported by the Ministry of Knowledge Economy(MKE),Korea,under the Information Technology Research Center(ITRC)supervised by the National IT Industry Promotion Agency(NIPA)(NIPA-2010-(C1090-1021-0002))It was sponsored by Daegu Gyungpook Development Institute 2010.
文摘Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang(FCMACBYY)learning,which is referred to as FCMAC-EBYY,to achieve a synergetic development in the search for optimal fuzzy sets and connection weights.Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training,which involves a large searching space due to complex connections as well as real values.The methodology employed by FCMACEBYY is coevolution,in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled.The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.
