A model to predict photosynthetic carbon assimilation rate(A)with high accuracy is important for forecasting crop yield and productivity.Long short-term memory(LSTM),a neural network suitable for time-series data,enab...A model to predict photosynthetic carbon assimilation rate(A)with high accuracy is important for forecasting crop yield and productivity.Long short-term memory(LSTM),a neural network suitable for time-series data,enables prediction with high accuracy but requires mesophyll variables.In addition,for practical use,it is desirable to have a technique that can predict A from easily available information.In this study,we propose a BLSTM augmented LSTM(BALSTM)model,which utilizes bi-directional LSTM(BLSTM)to indirectly reproduce the mesophyll variables required for LSTM.The most significant feature of the proposed model is that its hybrid architecture uses only three relatively easy-to-collect external environmental variables—photosynthetic photon flux density(Q_(in)),ambient CO_(2) concentration(C_(a)),and temperature(T_(air))—to generate mesophyll CO_(2) concentration(C_(i))and stomatal conductance to water vapor(g_(sw))as intermediate outputs.Then,A is predicted by applying the obtained intermediate outputs to the learning model.Accordingly,in this study,1)BALSTM(Q_(in),C_(a),T_(air))had a significantly higher A prediction accuracy than LSTM(Q_(in),C_(a),T_(air))in case of using only Q_(in),C_(a),and T_(air);2)BALSTMC_(i),g_(sw),which had C_(i) and g_(sw) as intermediate products,had the highest A prediction accuracy compared with other candidates;and 3)for samples where LSTM(Q_(in),C_(a),T_(air))had poor prediction accuracy,BALSTMC_(i),g_(sw)(Q_(in),C_(a),T_(air))clearly improved the results.However,it was found that incorrect predictions may be formed when certain factors are not reflected in the data(e.g.,timing,cultivar,and growth stage)or when the training data distribution that accounts for these factors differs from the predicted data distribution.Therefore,a robust model should be constructed in the future to improve the prediction accuracy of A by conducting gasexchange measurements(including a wide range of external environmental values)and by increasing the number of training data samples.展开更多
This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. The most important aspects of design building blocks of adaptive internal mod...This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. The most important aspects of design building blocks of adaptive internal model control are the choice of architectures, learning algorithms, and examples of learning. The choice of parametric adaptation algorithm for updating elements of the conventional adaptive internal model control shows limitations. To overcome these limitations, we chose the architectures of neural networks deduced from the conventional models and the Levenberg-marquardt during the adjustment of system parameters of the adaptive neural internal model control. The results of this latest control showed compensation for disturbance, good trajectory tracking performance and system stability.展开更多
Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory inputs.Here,we analytically investigate the trade-off betw...Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory inputs.Here,we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy quantity.We observed several phase transitions in the synaptic weight matrix,particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory interactions.The optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy use.These results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems.展开更多
Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vect...Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.展开更多
基金the support of JST PRESTO(Grant No.JPMJPR16O3)JSPS KAKENHI(Grant Nos.16KK0169 and 19K15944).
文摘A model to predict photosynthetic carbon assimilation rate(A)with high accuracy is important for forecasting crop yield and productivity.Long short-term memory(LSTM),a neural network suitable for time-series data,enables prediction with high accuracy but requires mesophyll variables.In addition,for practical use,it is desirable to have a technique that can predict A from easily available information.In this study,we propose a BLSTM augmented LSTM(BALSTM)model,which utilizes bi-directional LSTM(BLSTM)to indirectly reproduce the mesophyll variables required for LSTM.The most significant feature of the proposed model is that its hybrid architecture uses only three relatively easy-to-collect external environmental variables—photosynthetic photon flux density(Q_(in)),ambient CO_(2) concentration(C_(a)),and temperature(T_(air))—to generate mesophyll CO_(2) concentration(C_(i))and stomatal conductance to water vapor(g_(sw))as intermediate outputs.Then,A is predicted by applying the obtained intermediate outputs to the learning model.Accordingly,in this study,1)BALSTM(Q_(in),C_(a),T_(air))had a significantly higher A prediction accuracy than LSTM(Q_(in),C_(a),T_(air))in case of using only Q_(in),C_(a),and T_(air);2)BALSTMC_(i),g_(sw),which had C_(i) and g_(sw) as intermediate products,had the highest A prediction accuracy compared with other candidates;and 3)for samples where LSTM(Q_(in),C_(a),T_(air))had poor prediction accuracy,BALSTMC_(i),g_(sw)(Q_(in),C_(a),T_(air))clearly improved the results.However,it was found that incorrect predictions may be formed when certain factors are not reflected in the data(e.g.,timing,cultivar,and growth stage)or when the training data distribution that accounts for these factors differs from the predicted data distribution.Therefore,a robust model should be constructed in the future to improve the prediction accuracy of A by conducting gasexchange measurements(including a wide range of external environmental values)and by increasing the number of training data samples.
文摘This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. The most important aspects of design building blocks of adaptive internal model control are the choice of architectures, learning algorithms, and examples of learning. The choice of parametric adaptation algorithm for updating elements of the conventional adaptive internal model control shows limitations. To overcome these limitations, we chose the architectures of neural networks deduced from the conventional models and the Levenberg-marquardt during the adjustment of system parameters of the adaptive neural internal model control. The results of this latest control showed compensation for disturbance, good trajectory tracking performance and system stability.
基金supported by the National Natural Science Foundation of China(Grant Nos.12047503,11747601 and 12247104)the National Innovation Institute of Defense Technology(Grant No.22TQ0904ZT01025)。
文摘Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory inputs.Here,we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy quantity.We observed several phase transitions in the synaptic weight matrix,particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory interactions.The optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy use.These results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems.
基金supported by the funds of Ningde Normal University Youth Teacher Research Program(2015Q15)The Education Science Project of the Junior Teacher in the Education Department of Fujian province(JAT160532).
文摘Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.
