Time-varying Weights Research Articles

Model Predictive Control of Spatially Distributed Systems with Spatio-Temporal Logic Specifications

In this paper, for spatially distributed systems, we propose a new method of model predictive control with spatio-temporal logic specifications. We formulate the finite-time control problem with specifications described by SSTLf (signal spatio-temporal logic over finite traces) formulas. In the problem formulation, the feasibility is guaranteed by representing control specifications as a penalty in the cost function. Time-varying weights in the cost function are introduced to satisfy control specifications as well as possible. The finite-time control problem can be written as a mixed integer programming (MIP) problem. According to the policy of model predictive control (MPC), the control input can be generated by solving the finite-time control problem at each discrete time. The effectiveness of the proposed method is presented through a numerical example.

Model predictive control with real-time variable weight for civil aircraft towing taxi-out control systems

Unlike the traditional method of taxying, where civil aircraft reach the runway by relying on their engines, the new mode of towing taxi-out may become preferred because of its lower energy consumption, lower emissions, and higher efficiency. To improve the tracking accuracy and lateral stability of the towing system, this study used a two-level time-varying weight control method (RMPC) based on model predictive control (MPC) and fuzzy control. The tractor speed and front wheel angle were set as the control variables in the MPC controller, and the real-time lateral displacement error and yaw angle error of the tractor were set as the fuzzy control inputs. The influence of the weight matrix on the accuracy and stability of path tracking was coordinated by online optimization of the MPC objective function weight. The simulations of multiple working conditions were performed using TruckSim software in Simulink, which showed that the proposed control method can limit the lateral displacement error of the tractor and aircraft within 0.4 m, which can be reduced by 70.83% and 77.4% compared with the single MPC, respectively. Additionally, the maximum yaw angle errors of the tractor and aircraft are reduced by 76.11% and 51.31% compared with the single MPC, respectively. Furthermore, the yaw angle error of tractors can be limited to 4° and that of aircraft can be limited to 6.5°, which is an improvement of the lateral stability and driving safety for the civil aircraft towing system. The new controller may provide technical insights and support for the practical development and safe application of the towing taxi-out mode.

ε-Nash Equilibrium of Pursuer–Evader–Defender Missile Navigation Dynamic Games

This research is dedicated to developing a min–max robust control strategy for a dynamic game involving pursuers, evaders, and defenders in a multiple-missile scenario. The approach employs neural dynamic programming, utilizing multiple continuous differential neural networks (DNNs). The competitive controller devised addresses the robust optimization of a joint cost function that relies on the trajectories of the pursuer–evader–defender system, accommodating an uncertain mathematical model while adhering to control restrictions. The dynamic programming min–max formulation facilitates robust control by accounting for bounded modeling uncertainties and external disturbances for each game component. The value function of the Hamilton–Jacobi–Bellman (HJB) equation is approximated by a DNN, enabling the estimation of the closed-loop formulation for the joint dynamic game with state restrictions. The controller’s design is grounded in estimating the state trajectory under the worst possible uncertainties and perturbations, providing a robustness factor through the robust neural controller. The learning law class for the time-varying weights in the DNN is generated by studying the HJB partial differential equation for the missile motion for each player in the dynamic game. The controller incorporates the solution of the obtained learning laws and a time-varying Riccati equation, offering an online solution to the control implementation. A recurrent algorithm, based on the Kiefer–Wolfowitz method, adjusts the initial conditions for the weights to satisfy the final condition of the given cost function for the dynamic game. A numerical example is presented to validate the proposed robust control methodology, confirming the optimization solution based on the DNN approximation for Bellman’s value function.

Population pharmacokinetic modeling of sotatercept in healthy participants and patients with pulmonary arterial hypertension.

Sotatercept is a breakthrough, first-in-class biologic, that is FDA-approved for the treatment of pulmonary arterial hypertension (PAH). A population pharmacokinetic (PopPK) model was developed using data from two phase 1 studies in healthy participants, and two phase 2 studiesand one phase 3 study in participants with PAH. The pooled sotatercept PK data encompassed single intravenous (IV) or subcutaneous (SC) doses ranging from 0.01 to 3.0 mg/kg, as well as multiple SC doses ranging from 0.03 to 1.0 mg/kg, with PK samples collected up to a maximum of ~150 weeks following Q3W and Q4W dosing regimens. The final PopPK analysis included 350 participants, with 30 and 320 participants receiving sotatercept IV and SC, respectively. A two-compartment model with a first-order absorption rate constant and a linear disposition from central compartment well-described sotatercept PK. The estimated bioavailability is ~66%; bioavailability, clearance (CL), and central volume (VC) have low to moderate inter-individual variability. Time-varying body weight and baseline albumin concentration were statistically significant predictors of PK; CL and VC were predicted to increase with increasing body weight, while CL was predicted to decrease with increasing baseline albumin concentration. However, the magnitude of covariate effects is not predicted to meaningfully alter the disposition of sotatercept. Altogether, the PopPK modeling results demonstrate favorable PK characteristics (low to moderate variability and typical bioavailability), supporting sotatercept as a SC biological agent for the treatment of patients with PAH.

Longitudinal associations of e-cigarette use with cigarette, marijuana, and other drug use initiation among US adolescents and young adults: Findings from the population assessment of tobacco and health study (Waves 1–6)

IntroductionResearch examining prospective links of e-cigarette use with cigarette, marijuana, and other substance use has been limited largely to 1–2-year follow-up periods and focused on younger adolescents. This study examined longitudinal associations of e-cigarette use with cigarette, marijuana, and other substance use initiation among U.S. adolescents and young adults (AYAs) across an eight-year period. MethodsAdolescent (ages 12–17) and young adult (ages 18–25) data from waves 1–6 of the nationally representative Population Assessment of Tobacco and Health study (2013–2021) were used. Discrete time survival models with time-varying weights were employed to examine the risk of cigarette, marijuana, and other drug use initiation over an eight-year follow-up period among AYAs with no lifetime use of e-cigarettes/other tobacco, lifetime but no past 30-day use of e-cigarettes/other tobacco, past 30-day e-cigarettes only, other tobacco use only, or past 30-day e-cigarette/other tobacco use. We compare our time-varying weighting approach to a traditional time-invariant/complete case weighting approach. ResultsAcross six follow-up waves, all three past 30-day nicotine/tobacco use groups, including e-cigarettes only, had greater risk for cigarette, marijuana, and other drug use initiation relative to those not using nicotine/tobacco. The three past 30-day nicotine/tobacco use groups did not differ from each other in risk for marijuana use initiation. Associations were smaller in magnitude for young adults compared to adolescents, but significant for both age groups. ConclusionsSubstance use initiation risks persist beyond 1–2 years for U.S. AYAs using e-cigarettes. Prevention strategies to reduce AYA e-cigarette use are needed to reduce cancer-related risk.

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

A multi-strategy spider wasp optimizer based on grouping and dimensional symmetry method with a time-varying weight

A multi-strategy spider wasp optimizer based on grouping and dimensional symmetry method with a time-varying weight

A gradient descent algorithm for SNN with time-varying weights for reliable multiclass interpretation

Interpretation of the prediction is vital for mission critical tasks. Accurate interpretation relies upon the generalization accuracy of the model. In this paper, we propose a modified gradient descent learning algorithm to improve the generalization ability of a Spiking Neural Network with time-varying weights (SNN-t). This algorithm is referred to as GradST, can help towards improving the interpretation of multiclass classification problems. We have transformed the SNN-t to a Generalized Additive Model (GAM) to provide interpretation. The resultant Spiking Additive Model (SAM) has the generalization ability of SNN-t and the interpretable characteristics of GAM for multiclass problems. We also propose a post-processing method to enable better visualization of multiple shape functions of GAMs, towards better relative interpretation for multiclass classification problems. The post-processing method utilizes the properties of multiclass GAMs to visually modify the shape functions to establish the importance of the feature in multiclass setting. We first evaluate the performance of SNN-t, trained with GradST and the SAM generated from it, on large public datasets. The SNN-t trained with GradST has better generalization accuracy than other SNN-t classifier and consequently, the SAM generated from it has better generalization accuracy than other state-of-the-art multiclass GAMs. Improved accuracy in SAM implies more reliable interpretation. Then, we evaluate the proposed post-processing method for multiclass GAMs to provide relative interpretation. It is observed that relative interpretation of multiclass GAM is more meaningful and reliable.

Differential neural network robust constrained controller using approximate dynamic programming

This study focuses on developing a continuous differential neural network (DNN) approximating min–max robust control by applying dynamic neural programming. The suggested controller is applied to a class of nonlinear perturbed systems providing satisfactory dynamics for a given cost function depending on both the trajectories of the perturbed system and the designed constrained control actions. The min–max formulation for dynamic programming offers reliable control for restricted modeling uncertainties and perturbations. The suggested design considers control norm restrictions in the optimization problem. DNN’s approximation of the worst (with respect to the admissible class of perturbations and uncertainties) value function of the Hamilton–Jacobi–Bellman (HJB) equation enables the estimation of the closed-loop formulation of the controller. The robust version of the HJB partial differential equation is studied to create the learning law class for the time-varying weights in the DNN. The controller employs a time-varying Lyapunov-like differential equation and the solution of the corresponding learning laws. A recurrent algorithm based on the Kiefer–Wolfowitz technique can be used by modifying the weights’ initial conditions to fulfill the specified cost function’s end requirements. A numerical example tests the robust control proposed in this study, validating the robust optimal solution based on the DNN approximation for Bellman’s value function.

A robust time-varying weight combined model for crude oil price forecasting

Crude oil plays a vital role in industrial and social development and has become an integral part of the economic development. However, influenced by policies, wars, etc., it is hard to capture the trend of complex and volatile crude oil price if only one model is used, which often leads to poor forecasts. To enhance the prediction accuracy and robustness of forecasting models, a novel combined forecasting method with time-varying weights, i.e., Jaynes weight hybrid (JWH) model incorporating the Shannon information entropy and several forecasting methods is proposed in this paper. In the selection of the baseline models, the autoregressive integrated moving average in classical statistical forecasting strategies, back propagation neural network, extreme learning machine in neural network and long short-term memory neural network in deep learning models are chosen to fit the crude oil price. Four datasets and three experiments are constructed to verify the prediction ability of the novel combined forecasting model. Empirical results are calculated by five measurement criteria, suggesting that the prediction accuracy of the novel combined method is significantly higher than several comparison models and the mean absolute percentage error of the model has arrived 2.81 % in detail. Particularly, the proposed model has achieved satisfactory performances in Covid-19 and the War in Ukraine, further verifying the robustness of the combined methodology.

Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method

Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a nonstationary model through the Bayesian weighted averaging of two lumped conceptual rainfall–runoff (RR) models (the Xinanjiang and GR4J model) with time-varying weights; and (ii) detecting the temporal variation in the optimized Bayesian weights under climate change conditions. By combining the BMA method with period partition and time sliding windows, the efficacy of adopting time-varying model structures is investigated over three basins located in the U.S. and Australia. The results show that (i) the nonstationary ensemble-averaged model with time-varying weights surpasses both individual models and the ensemble-averaged model with time-invariant weights, improving NSE[Q] from 0.04 to 0.15; (ii) the optimized weights of Xinanjiang model increase and that of GR4J declines with larger precipitation, and vice versa; (iii) the change in the optimized weights is proportional to that of precipitation under monotonic climate change, as otherwise the mechanism changes significantly. Overall, it is recommended to adopt nonstationary structures in hydrological modeling.

Reliability evaluation of flexible manufacturing systems based on three-layer index system with time-varying weights

With the development of industrial manufacturing, flexible manufacturing system provides an effective way to improve the machining quality and efficiency, as well as reduce the manufacturing cost. Fflexible manufacturing system reliability has been one focus of recent studies. But a systematic, effective and executable reliability evaluation method has not yet proposed in existing researches. This work firstly presented a three-layer index system for flexible manufacturing system reliability evaluation, which is composed of the whole layer indexes, subsystem layer indexes and equipment layer indexes determined by system composition and running features analysis. Time-varying weights of each layer are calculated through the modelling of the reliability and reliability importance based on the classical two-state failure hypothesis. Then, considering multi-state failure modes of elements, a fuzzy evaluation model of flexible manufacturing system reliability is established based on fault grading and statistics, considering grade and number distributions of faults within a certain statistical period. Moreover, through a simulation case of a box-parts finishing flexible manufacturing system, the influence of failure rates and service time on the index weights distribution is firstly revealed, which can provide theoretical basis for the improvement of flexible manufacturing system reliability; then the effectiveness of the proposed method is verified through the comparison of results obtained by the previous model based on two-state failure and the proposed model based on multi-state failure.

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a novel multi-frequency ground-penetrating radar (GPR) data-fusing method through a joint sliding window and wavelet transform weighting method to accurately detect the top-coal structure. It possesses the advantages of both high resolution and great detection depth, and it can also integrate multi-frequency GPR data into one composite profile to interpret the internal structure information of top coal in detail. The detection procedure is implemented following several steps: First of all, the multi-frequency GPR data are preprocessed and aligned through a band-pass filter and a zero offset elimination method to establish their spatial correspondences. Secondly, the proposed method is used to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window; also, the edge detection algorithm is introduced to improve the fusion efficiency of the wavelet transform so as to realize the effective fusion of the multi-frequency GPR data. Thirdly, a reflection intensity model of multi-frequency GPR signals traveling in the top-coal is established by using the stratified identification method, and then, the detailed top-coal structure can be inversely interpreted. Finally, the quantitative evaluation criteria, information entropy (IE), space–frequency (SF) and Laplacian gradient (LG), are used to evaluate the multi-frequency GPR data fusion’s effectiveness in laboratory and field environments. The experimental results show that, compared with the genetic, time-varying and wavelet transform fusion method, the fusion performance of the presented method possesses higher values in the IE, SF and LG evaluation criteria, and it also has both the merits of high resolution and great detection depth.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

New evidence on US monetary policy activism and the Taylor rule

Abstract We provide new evidence about US monetary policy using a model that: (i) estimates time-varying monetary policy weights without relying on stylized theoretical assumptions; (ii) allows for endogenous breakdowns in the relationship between interest rates, inflation, and output; and (iii) generates a unique measure of monetary policy activism that accounts for economic instability. The joint incorporation of endogenous time-varying uncertainty about the monetary policy parameters and the stability of the relationship between interest rates, inflation, and output materially reduces the probability of determinate monetary policy. The average probability of determinacy over the period post-1982 to 1997 is below 60% (hence well below seminal estimates of determinacy probabilities that are close to unity). Post-1990, the average probability of determinacy is 75%, falling to approximately 60% when we allow for typical levels of trend inflation.

Robust Space Launcher Control with Time-Varying Objectives

This paper presents a novel approach to robust linear time-varying control design for space launchers. It allows the controller to explicitly account for the launcher’s time-varying dynamics and changing control objectives along the ascent trajectory. The latter are readily incorporated via time-varying weighting functions into a mixed sensitivity design. For a traceable and transparent control design, a physically motivated weighting scheme is applied, which requires little tuning effort. The controller is obtained via a novel observer-based finite horizon linear time-varying synthesis approach. It provides a transparent structure which is easy to implement. The approach is used to design a pitch controller for a flexible expendable launch vehicle in atmospheric ascent tracking the open-loop guidance reference signal.

Applying Particle Swarm Optimization for Investigating the Stability of DC-DC Buck-Boost Converter: A Software Based Approach

ABSTRACT Stability of power converters has always been a major concern when dealing with nonlinear behavior of the system and swarm-based optimization techniques are proven to be handy as they are widely employed. Hence, this study presents an investigative approach to explore the compatibility of applying different variants of Particle Swarm Optimization (PSO) to design an optimized PID controller for DC-DC Buck-Boost converter. This work assesses converter stability through optimization of the PID controller using classical PSO (cPSO), Time-Varying Inertia Weight PSO (TVIW-PSO), Chaotic Descending Inertia Weight PSO (CDIW-PSO), Self-organizing Hierarchical PSO (HPSO) and Random inertia weight PSO (RIW-PSO). The State Space Average (SSA) method is utilized to model and attain the transfer function; eigenvalue analysis and time domain simulation analysis are considered to investigate the stability. Moreover, four objective functions and various performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to assess the system’s efficiency and the outcomes of different approaches are compared extensively. Amongst all, CDIW-PSO showcased better performances e.g. overshoot of 1.08%, settling time 0.000228s and rise time 0.000143s. The findings illustrate algorithm’s capacity to reduce transient and steady-state faults and makes it compatible with more nonlinear applications.

Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework.

Objective.Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize potential dynamic spatial and temporal fluctuations. Since multi-subject dFNC data from sliding-window analysis are also naturally a higher-order tensor, we propose an innovative sparse and low-rank CPD (SLRCPD) for the three-way dFNC tensor to excavate significant dynamic spatiotemporal aberrant changes in schizophrenia.Approach.The proposed SLRCPD approach imposes two constraints. First, the L1regularization on spatial modules is applied to extract sparse but significant dynamic connectivity and avoid overfitting the model. Second, low-rank constraint is added on time-varying weights to enhance the temporal state clustering quality. Shared dynamic spatial modules, group-specific dynamic spatial modules and time-varying weights can be extracted by SLRCPD. The strength of connections within- and between-IC networks and connection contribution are proposed to inspect the spatial modules. K-means clustering and classification are further conducted to explore temporal group difference.Main results.82 subject resting-state functional magnetic resonance imaging (fMRI) dataset and opening Center for Biomedical Research Excellence (COBRE) schizophrenia dataset both containing schizophrenia patients (SZs) and healthy controls (HCs) were utilized in our work. Three typical dFNC patterns between different brain functional regions were obtained. Compared to the spatial modules of HCs, the aberrant connections among auditory network, somatomotor, visual, cognitive control and cerebellar networks in 82 subject dataset and COBRE dataset were detected. Four temporal states reveal significant differences between SZs and HCs for these two datasets. Additionally, the accuracy values for SZs and HCs classification based on time-varying weights are larger than 0.96.Significance.This study significantly excavates spatio-temporal patterns for schizophrenia disease.

LSTM-Based Coherent Mortality Forecasting for Developing Countries

This paper studies a long short-term memory (LSTM)-based coherent mortality forecasting method for developing countries or regions. Many of such developing countries have experienced a rapid mortality decline over the past few decades. However, their recent mortality development trend is not necessarily driven by the same factors as their long-term behavior. Hence, we propose a time-varying mortality forecasting model based on the life expectancy and lifespan disparity gap between these developing countries and a selected benchmark group. Here, the mortality improvement trend for developing countries is expected to converge gradually to that of the benchmark group during the projection phase. More specifically, we use a unified deep neural network model with LSTM architecture to project the life expectancy and lifespan disparity difference, which further controls the rotation of the time-varying weight parameters in the model. This approach is applied to three developing countries and three developing regions. The empirical results show that this LSTM-based coherent forecasting method outperforms classical methods, especially for the long-term projections of mortality rates in developing countries.

Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA

Pavlovian associative learning, a form of classical conditioning, has significantly impacted the development of psychology and neuroscience. However, the realization of a prototypical photonic neural network (PNN) for full-function Pavlov associative learning, encompassing both photonic synapses and photonic neurons, has not been achieved to date. In this study, we propose and experimentally demonstrate the first InP-based full-function Pavlov associative learning PNN. The PNN utilizes semiconductor optical amplifiers (SOAs) as photonic synapses and the distributed feedback laser with a saturable absorber (DFB-SA) as the photonic spiking neuron. The connection weights between neurons in the PNN can be dynamically changed based on the fast, time-varying weighting properties of the SOA. The optical output of the SOA can be directly coupled into the DFB-SA laser for nonlinear computation without additional photoelectric conversion. The results indicate that the PNN can successfully perform brain-like computing functions such as associative learning, forgetting, and pattern recall. Furthermore, we analyze the performance of PNN in terms of speed, energy consumption, bandwidth, and cascadability. A computational model of the PNN is derived based on the distributed time-domain coupled traveling wave equations. The numerical results agree well with the experimental findings. The proposed full-function Pavlovian associative learning PNN is expected to play an important role in the development of the field of photonic brain-like neuromorphic computing.