This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with othe...This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with other algorithms in use, this method is theoretically more strict, while its structure is simple and the results obtained are accurate.展开更多
Corrective control theory lays a novel foundation for the fault-tolerant control of asynchronous sequential machines. In this paper, we present a corrective control scheme for tolerating permanent state transition fau...Corrective control theory lays a novel foundation for the fault-tolerant control of asynchronous sequential machines. In this paper, we present a corrective control scheme for tolerating permanent state transition faults in the dynamics of asynchronous sequential machines. By a fault occurrence, the asynchronous machine may be stuck at a faulty state, not responding to the external input. We analyze the detectability of the considered faults and present the necessary and sufficient condition for the existence of a controller that overcomes any permanent transition faults. Fault tolerance is realized by using potential reachability and asynchronous mechanisms in the machine. A case study on an asynchronous counter is provided to illustrate the proposed fault detection and tolerance scheme.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause ...Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause fails,the bindings made by the clause must be undone and the stored choice point is reactivated,and then another clause of the candidate ones is chosen to run on it. Storing and reactivating choice points and undoing account for the great overhead are required to control PROLOG execution,which is quite different from conventional programs. This paper focuses on the techniques used in Sequential PROLOG Engine(SPE)to reduce the overhead of control operations.The control instructions of SPE store no more choice points than the necessary.Its architecture takes the approaches of analysing the potential parallelism in the con- trol operations and developing a fraction of it due to the cost-effect consideration.The results of executing two sample programs on SPE in the form of hand timings are presented,which favor the approach.展开更多
Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timel...Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.展开更多
文摘This paper presents a method for state simplification in incompletely specified sequential machines. The new method adopts Inclusive-OR operation of column vectors for multi-level output matrix E_k! Compared with other algorithms in use, this method is theoretically more strict, while its structure is simple and the results obtained are accurate.
文摘Corrective control theory lays a novel foundation for the fault-tolerant control of asynchronous sequential machines. In this paper, we present a corrective control scheme for tolerating permanent state transition faults in the dynamics of asynchronous sequential machines. By a fault occurrence, the asynchronous machine may be stuck at a faulty state, not responding to the external input. We analyze the detectability of the considered faults and present the necessary and sufficient condition for the existence of a controller that overcomes any permanent transition faults. Fault tolerance is realized by using potential reachability and asynchronous mechanisms in the machine. A case study on an asynchronous counter is provided to illustrate the proposed fault detection and tolerance scheme.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
基金SPE is partly supported by National Natural Science Foundation of China.
文摘Nondeterminism of PROLOG execution requires that a block of control information or a choice point for each procedure call be stored when there are other candidate clauses to be used.When the currently selected clause fails,the bindings made by the clause must be undone and the stored choice point is reactivated,and then another clause of the candidate ones is chosen to run on it. Storing and reactivating choice points and undoing account for the great overhead are required to control PROLOG execution,which is quite different from conventional programs. This paper focuses on the techniques used in Sequential PROLOG Engine(SPE)to reduce the overhead of control operations.The control instructions of SPE store no more choice points than the necessary.Its architecture takes the approaches of analysing the potential parallelism in the con- trol operations and developing a fraction of it due to the cost-effect consideration.The results of executing two sample programs on SPE in the form of hand timings are presented,which favor the approach.
文摘Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.
