Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Roles of neural regeneration in memory pharmacology

Neural regeneration, or neuroregeneration, is a brain mechanism essential for rescuing cognitive functions pharmacologically against memory disorders and aging-related memory abnormality. In this short Perspective article, we intend to briefly present the essential roles of neuroregeneration and neural plasticity in learning and memory, memory disorders, and critical involvement in an effective treatment of memory disorders.

Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.

Almost periodic solutions of memristive multidirectional associative memory neural networks with mixed time delays

Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectional associative memory neural networks(MAMNNs)with mixed time-varying delays are investigated in the sense of Filippov solution.First,three steps are given to prove the existence of the almost periodic solution.Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function.Second,the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function.The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions,Halanay inequality and Lyapunov function.The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks.Finally,numerical examples with simulations are presented to show the validity of the main results.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

Existence and exponential stability of almost-periodic solutions for MAM neural network with distributed delays on time scales

This paper is concerned with multidirectional associative memory neural network with distributed delays on almost-periodic time scales.Some sufficient conditions on the existence,uniqueness and the global exponential stability of almost-periodic solutions are established.An example is presented to illustrate the feasibility and effectiveness of the obtained results.

Influence of acupuncture with exercise training on learning and memory functions, as well as microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region, in a rat model of cerebral infarction

The present study was designed to determine microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region in a rat model of middle cerebral artery occlusion. The rats were treated with acupuncture at Baihui （GV 20）, Qubin （GB 7）, and Qianding （GV 21） points, in addition to exercise training. Results were compared with rats undergoing exercise training only. The Y-maze method and immunohistochemistry revealed decreased error frequency of passing through Y-maze, as well as significantly increased microtubule-associated protein-2 and synaptophysin expression, in the acupuncture with exercise training group compared with the model and exercise training groups after 5 weeks. Microtubule-associated protein-2 and synaptophysin expressions negatively correlated with error frequency of passing through the Y-maze. These results suggested that acupuncture combined with exercise training improved learning and memory functions in a rat model of cerebral infarction. The mechanisms of action were hypothesized to be associated with dendritic or synaptic plasticity in the ipsilateral hippocampal CA3 region.

Subhypnotic doses of propofol impair spatial memory retrieval in rats

Abundant evidence indicates that propofol profoundly affects memory processes, although its specific effects on memory retrieval have not been clarified. A recent study has indicated that hippocampal glycogen synthase kinase-3β（GSK-3β） activity affects memory. Constitutively active GSK-3β is required for memory retrieval, and propofol has been shown to inhibit GSK-3β. Thus, the present study examined whether propofol affects memory retrieval, and, if so, whether that effect is mediated through altered GSK-3β activity. Adult Sprague-Dawley rats were trained on a Morris water maze task（eight acquisition trials in one session） and subjected under the influence of a subhypnotic dose of propofol to a 24-hour probe trial memory retrieval test. The results showed that rats receiving pretest propofol（25 mg/kg） spent significantly less time in the target quadrant but showed no change in locomotor activity compared with those in the control group. Memory retrieval was accompanied by reduced phosphorylation of the serine-9 residue of GSK-3β in the hippocampus, whereas phosphorylation of the tyrosine-216 residue was unaffected. However, propofol blocked this retrieval-associated serine-9 phosphorylation. These findings suggest that subhypnotic propofol administration impairs memory retrieval and that the amnestic effects of propofol may be mediated by attenuated GSK-3β signaling in the hippocampus.

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.

Delay-Dependent Exponential Stability Criterion for BAM Neural Networks with Time-Varying Delays

By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.

Prediction Method of Equipment Remaining Life Based on Self-Attention Long Short-Term Memory Neural Network

Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-attention mechanism with the long short-term memory neural network(LSTM-NN)is proposed,called Self-Attention-LSTM.First,the auto-encoder is used to obtain the component-level state information;second,the state information of each component is input into the self-attention mechanism to learn the correlation between components;then,the multi-component correlation matrix is added to the LSTM input gate,and the LSTM-NN is used for life prediction.Finally,combined with the commercial modular aero-propulsion system simulation data set(C-MAPSS),the experiment was carried out and compared with the existing methods.Research results show that the proposed method can achieve better prediction accuracy and verify the feasibility of the method.

Deinterleaving of radar pulse based on implicit feature

In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Global stability of bidirectional associative memory neural networks with continuously distributed delays

Global asymptotic stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with continuously distributed delays is studied. Under two mild assumptions on the activation functions, two sufficient conditions ensuring global stability of such networks are derived by utilizing Lyapunov functional and some inequality analysis technique. The results here extend some previous results. A numerical example is given showing the validity of our method.

A Visual-Based Gesture Prediction Framework Applied in Social Robots

In daily life,people use their hands in various ways for most daily activities.There are many applications based on the position,direction,and joints of the hand,including gesture recognition,gesture prediction,robotics and so on.This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures.The model is applied to the NAO robot to verify the effectiveness of the proposed method.First of all,in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion,the Kalman filter is applied to the original data.Then some new feature descriptors are introduced.The length feature,angle feature and angular velocity feature are extracted from the filtered data.These features are fed into the long-short time memory recurrent neural network(LSTM-RNN)with different combinations.Experimental results show that the combination of coordinate,length and angle features achieves the highest accuracy of 99.31%,and it can also run in real time.Finally,the trained model is applied to the NAO robot to play the finger-guessing game.Based on the predicted gesture,the NAO robot can respond in advance.

Exercise preconditioning exhibits neuroprotective effects on hippocampal CA1 neuronal damage after cerebral ischemia

Recent evidence has suggested the neuroprotective effects of physical exercise on cerebral ischemic injury. However, the role of physical exercise in cerebral ischemia-induced hippocampal damage remains controversial. The aim of the present study was to evaluate the effects of pre-ischemia treadmill training on hippocampal CA1 neuronal damage after cerebral ischemia. Male adult rats were randomly divided into control, ischemia and exercise ＋ ischemia groups. In the exercise ＋ ischemia group, rats were subjected to running on a treadmill in a designated time schedule（5 days per week for 4 weeks）. Then rats underwent cerebral ischemia induction th rough occlusion of common carotids followed by reperfusion. At 4 days after cerebral ischemia, rat learning and memory abilities were evaluated using passive avoidance memory test and rat hippocampal neuronal damage was detected using Nissl and TUNEL staining. Pre-ischemic exercise significantly reduced the number of TUNEL-positive cells and necrotic cell death in the hippocampal CA1 region as compared to the ischemia group. Moreover, pre-ischemic exercise significantly prevented ischemia-induced memory dysfunction. Pre-ischemic exercise mighct prevent memory deficits after cerebral ischemia through rescuing hippocampal CA1 neurons from ischemia-induced degeneration.

Knowledge Tracking Model Based on Learning Process

Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.