Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

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.

Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.

“Here Comes the New”:Individual and Collective Forgetting in Toni Morrison’s Jazz

This article examines the problem of individual and collective attempts at forgetting the traumatic past in Toni Morrison’s sixth novel Jazz(1992).More specifically,it emphasizes by selected examples psychological and social aspects of willful amnesia which can lend itself useful in helping traumatized(country)individuals to repress painful remembrances,heal mental wounds and build a new identity in a memory-free modern city.Analyzing Jazz’s narrative featuring Joe and Violet Trace,with a particular focus put on the expectations and experiences connected with their migration to and life in the City,the article explores via Paul Connerton’s ruminations on cultural forgetting in modern times-delineated in his book How Modernity Forgets(2009)-the mechanisms of intentional amnesia used in the process of recovering from personal and social traumas resulting from more recent(migration and urban life)and more time-distant(slavery and racism)ordeals.

A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour

It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.

RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are a lot of intelligent tutoring systems.In these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available data.From these data,personalized education that suits each student can be mined.To improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep learning.However,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the knowledge.Although some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning sequences.The main contribution of this paper is in two aspects.One is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political combinations.The other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting behavior.This paper proposes an adaptive algorithm for the original DKT model.By using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳learning.The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

Forgetting In Creative Problem Solving for College Students

As more and more benefits of forgetting have been found in recent studies,whether forgetting could promote individuals ability of creative problem solving remains a controversial debate.This article discusses the effect of two types of forgetting,the retrieval-induced forgetting(RIF)and the forgetting during incubation,in benefiting creative problem solving by introducing and analysing the relevant experiments.The results reveal that retrieval-induced forgetting only works when previous mental fixations occurred and the promotion varies when solving different types of problems.The level of RIF is irrelevant to the performance in solving closed-ended creative problems and high level of RIF even impairs the creativity when solving open-ended problems.And forgetting during incubation cannot explain the incubation effect.The spreading activation of relevant information or the unconscious work is more likely to be the possible reasons.In conclusion,the current article brings about the discussions about the work conditions and effects of forgetting in creative problem solving.

On the effects of contextualized explanation and Ebbinghaus Forgetting Curve on the teaching and learning of English vocabulary

This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.

Uncovering the Mist of Forced Forgetting: On Forgiveness in The Buried Giant

The Buried Giant by Kazuo Ishiguro begins with an elderly couple who start a quest for the past memory which disap pears under the spell of the she-dragon Querig.During,individual confrontations and collective revenges work together to disclose the dark secrets that have been withheld.At the same time,it probes into the problem of forgiveness:can forced forgetting enable individuals or collectives forget their dark history for either love or peace?Based on the analysis of the individual and collective memories embodied in the novel,the present paper by virtue of Paul Ricoeur’s theory of abuses of memory,especially forced forget ting exhumes Ishiguro’s critical attitude towards forced forgetting,which ignores the threatening elements of love and peace like vi olent revenge and betrayal.

The Enlightenment of Language Attrition and Forgetting to English Vocabulary Memory Strategies for College English Majors

This paper reviews the theory of language attrition,which refers to the loss or degradation of second language skills due to lack of using the second language for a certain period of time.Although our country attaches great importance to English language teaching,most of college English majors use English far less frequently than that of Chinese in real life,which makes them easily influenced by language attrition.Therefore,it is of great significance for college English majors to improve the efficiency of English vocabulary memory from the perspective of language attrition combined with Forgetting.This thesis consists of three parts.Chapter one is an analysis the concept of language attrition and Forgetting.Chapter two describes and analyzes the existing problems in current vocabulary memory among the college English majors via a questionnaire survey.The final chapter puts forward some corresponding countermeasures to help college English majors get rid of the influence of language attrition on vocabulary learning.

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

Deep knowledge tracking algorithm based on forgetting law

Knowledge tracking(KT)algorithm,which can model the cognitive level of learners,is a fundamental artificial intelligence approach to solve the personalized learning problem in the field of education.The recently presented separated self-attentive neural knowledge tracing(SAINT)algorithm has got a great improvement on predictingthe accuracy of students’answers in comparison with the present other methods.However there is still potential to enhance its performance for it fails to effectively utilize temporal features.In this paper,an optimization algorithm for SAINT based on Ebbinghaus’law of forgetting was proposed which took temporal features into account.The proposed algorithm used forgetting law-based data binning to discretize the time information sequences,so as to obtain the temporal featuresin accordance with people’s forgetting pattern.Then the temporal features were used as input in the decoder of SAINT model to improve its accuracy.Ablation experiments and comparison experiments were performed on the EdNet dataset in order to verify the effectiveness of the proposed model.Seen in the experimental results,it achieved higher area under curve(AUC)values than the other present representative knowledge tracing algorithms.It demonstrates that temporal featuresare necessary for KT algorithms if it can be properly dealt with.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Deep learning algorithm featuring continuous learning for modulation classifications in wireless networks

Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Inhibition of Rac1-dependent forgetting alleviates memory deficits in animal models of Alzheimer's disease

Accelerated forgetting has been identified as a feature of Alzheimer's disease(AD),but the therapeutic efficacy of the manipulation of biological mechanisms of forgetting has not been assessed in AD animal models.Ras-re-lated C3 botulinum toxin substrate 1(Rac1),a small GTPase,has been shown to regulate active forgetting in Drosophila and mice?Here,we showed that Rac1 activity is aberrantly elevated in the hippocampal tissues of AD patients and AD animal models.Moreover,amyloid-beta 42 could induce Rac1 activation in cultured cells.The elevation of Rac1 activity not only accelerated 6-hour spatial memory decay in 3-month-old APP/PS1 mice,but also significantly contributed to severe memory loss in aged APP/PS1 mice.A similar age-dependent Rac1 activity-based memory loss was also observed in an AD fly model.Moreover,inhibition of Rac1 activity could ameliorate cognitive defects and synaptic plasticity in AD animal models.Finally,two novel compounds,identified through behavioral screening of a randomly selected pool of brain permeable small molecules for their positive effect in rescuing memory loss in both fly and mouse models,were found to be capable of inhibiting Rac1 activity.Thus,multiple lines of evidence corroborate in supporting the idea that inhibition of Rac1 activity is effective for treating AD-related memory loss.

Online Identification of Power Battery Parameters for Electric Vehicles Using a Decoupling Multiple Forgetting Factors Recursive Least Squares Method

Li-ion batteries are widely used in electric vehicles(EVs).However,the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries.This paper proposes a decoupling multiple forgetting factors recursive least squares method(DMFFRLS)for EV battery parameter identification.The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters.The Thevenin equivalent circuit model(ECM)is employed considering the complexity of battery management system(BMS)on the basis of comparative analysis of several common battery ECMs.In addition,decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process.Numerous experiments are employed to verify the proposed DMFFRLS method.The parameters for commonly used LiFePO4(LFP),Li(NiCoMn)O2(NCM)battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods.The experimental results show that the error of the DMFFRLS method is less than 15 mV,which is significantly lower than the conventional methods.The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries,and provides a basis for state of charge(SOC)estimation and BMS design of EVs.

Adaptive Subspace Predictive Control with Time-varying Forgetting Factor

Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.

Convergence Analysis of Forgetting Gradient Algorithm by Using Martingale Hyperconvergence Theorem

The stochastic gradient (SG) algorithm has less of a computational burden than the least squares algorithms, but it can not track time varying parameters and has a poor convergence rate. In order to improve the tracking properties of the SG algorithm, the forgetting gradient (FG) algorithm is presented, and its convergence is analyzed by using the martingale hyperconvergence theorem. The results show that: (1) for time invariant deterministic systems, the parameter estimates given by the FG algorithm converge consistently to their true values; (2) for stochastic time varying systems, the parameter tracking error is bounded, that is, the parameter tracking error is small when both the parameter change rate and the observation noise are small.