Time-varying Weights Research Articles

Data driven modeling and nonlinear model predictive control design for a rotary cement kiln

Cement production is an energy intensive process, on the other hand the product quality directly influences the economic benefit. In this work a nonlinear model predictive control is designed to achieve an optimal compromise between energy consumption, production volume, and product quality. Based on measurements from a 100 t/h rotary cement kiln a non-linear autoregressive NARMAX-model is identified, and cross validation of this model shows good accuracy for control design. A prediction of the most influential disturbance (quality of the feed material) is utilized, product quality can be defined as a set-point, and the optimization criterion is defined using time-varying performance weights. This design achieves good transient response while still guaranteeing that the desired production volume is met. Validation results of the model with measured data and simulation results for the closed-loop operation demonstrate the functionality of the proposed methodology.

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

Because of its excellent small sample learning abilities and simple network structure, support vector machine (SVM) is widely applied in various pattern recognition fields, e.g., face recognition, scene classification, fault diagnosis, etc. Due to the complexity and diversity of analog circuit faults, the diagnosis accuracy and stability of SVM classifier optimized by traditional particle swarm optimization (PSO) are unsatisfactory. Therefore, this paper proposes an improved hybrid particle swarm optimization (IH-PSO) algorithm to optimize SVM, which is applied in the fault diagnosis of analog circuits. Compared with the traditional PSO algorithm, the proposed IH-PSO mainly has three improvements, namely, the opposition-based learning population initialization, the nonlinear time-varying inertia weight, and the new position updating strategy with a spiral convergence mechanism. The performance of the proposed IH-PSO algorithm is verified by 12 commonly used benchmark functions and experimental results show that the proposed IH-PSO algorithm overcomes the deficiencies of the traditional PSO algorithm, such as slow convergence speed and trapping into local optimums. In addition, to further verify the performance of IH-PSO algorithm, the IH-PSO optimized SVM is applied to solve analog circuits fault diagnosis problems. Extensive experiments are carried out and results indicate that the proposed method has better performances both in diagnosis accuracy and stability compared with that of the traditional method.

Plug-and-Play Fault Detection and Isolation for Large-Scale Nonlinear Systems With Stochastic Uncertainties

This paper proposes a novel scalable model-based fault detection and isolation approach for the monitoring of nonlinear large-scale systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically manage the possible plug-in of novel subsystems and unplugging of existing ones. The reconfiguration procedure involves only local operations and communication with neighboring subsystems, thus, yielding a distributed and scalable architecture. In particular, the proposed fault diagnosis methodology allows the unplugging of faulty subsystems in order to possibly avoid the propagation of faults in the interconnected large-scale system. Measurement and process uncertainties are characterized in a probabilistic way leading to the computation, at each time-step, of stochastic time-varying detection thresholds with guaranteed false-alarms probability levels. To achieve this goal, we develop a distributed state estimation scheme, using a consensus-like approach for the estimation of variables shared among more than one subsystem; the time-varying consensus weights are designed to allow plug-in and unplugging operations and to minimize the variance of the uncertainty of the fault diagnosis thresholds. Convergence results of the distributed estimation scheme are provided. A novel fault isolation method is then proposed, based on a generalized observer scheme and providing guaranteed error probabilities of the fault exclusion task. Detectability and isolability conditions are provided. Simulation results on a power network model comprising 15 generation areas show the effectiveness of the proposed methodology.

Ascent trajectory tracking method using time-varying quadratic adaptive dynamic programming

Ascent trajectory tracking in the longitudinal plane is a class of nonaffine noncascade nonlinear system control problems with single input and multiple output, which is difficult to control by nonlinear method directly. Adaptive dynamic programming algorithm has the advantages of precise control and adaptability for general nonlinear control problem, and the time-varying quadratic adaptive dynamic programming algorithm proposed in this paper promotes the convergence and calculation speed of the traditional adaptive dynamic programming algorithm. To implement the algorithm effectively, the independent variables of ascent model are substituted in the launching coordinate, and then the model is treated as a discrete nominal trajectory tracking problem. Besides, the heuristic dynamic programming structure is used to train the processed model, and thus only the time-varying weight in the designed evaluation network needs to be updated. Simulation shows that the proposed algorithm can update the control variable online after predicting the cost function offline with the dynamic equations, which is faster than the general adaptive dynamic programming algorithm. In addition, the proposed algorithm can effectively and accurately track the nominal trajectory under the uncertainty of parameters compared with the linear quadratic regulators algorithm.

Growth cycle synchronization of the Visegrad Four and the European Union

In this paper, we map the growth cycle synchronization across the European Union, specifically focusing on the position of the Visegrad Four countries. We study the synchronization using frequency and time–frequency domain. To accommodate for dynamic relationships among the countries, we propose a wavelet cohesion measure with time-varying weights. Analyzing quarterly data from 1995 to 2017, we show an increasing co-movement of the Visegrad Four countries with the European Union after the countries have accessed the European Union. We show that participation in a currency union increases the co-movement of the country adopting the Euro. Furthermore, we find a high degree of synchronization at business cycle frequencies of the Visegrad Four and countries of the European monetary union.

Learning From Sleeping Experts

We consider a generalized model of learning from expert advice in which experts could abstain from participating at some rounds. Our proposed online algorithm falls into the class of weighted average predictors and uses a time-varying multiplicative weight update rule. This update rule changes the weight of an expert based on his or her relative performance compared to the average performance of available experts at the current round. This makes the algorithm suitable for recommendation systems in the presence of an adversary with many potential applications in the new emerging area of the Internet of Things. We prove the convergence of our algorithm to the best expert, defined in terms of both availability and accuracy, in the stochastic setting. In particular, we show the applicability of our definition of best expert through convergence analysis of another well-known algorithm in this setting. Finally, through simulation results on synthetic and real datasets, we justify the out-performance of our proposed algorithms compared to the existing ones in the literature.

Designing Fully Distributed Adaptive Event-Triggered Controllers for Networked Linear Systems With Matched Uncertainties.

This paper considers the distributed event-triggered consensus control problem for a network of linear systems subject to bounded uncertainties satisfying the matching condition. Due to the existence of nonidentical uncertainties, the multiagent system studied in this paper is essentially heterogeneous, and the event-triggered consensus problem of which is much more challenging than that of homogeneous linear networks in the existing works. We propose a static nonsmooth event-triggered protocol that includes a nonlinear term to ensure that consensus is achieved and the Zeno behavior is excluded. To avoid the undesirable chattering effect caused by the nonsmooth protocol, we design a static continuous event-based protocol, which can guarantee that the consensus error is ultimately bounded and the upper bound of the consensus error can be made satisfactorily small by choosing properly the design parameters. We also design a continuous adaptive event-triggered protocol that includes time-varying weights into both the control law and the triggering function. Contrary to the event-triggered protocols in the previous related works, the adaptive event-based protocol is fully distributed and scalable, whose design does not require any global information of the network graph. Besides, all the event-triggered protocols in this paper do not need continuous communications among neighboring agents in either control laws' updating or triggering conditions' monitoring.

Adaptive sliding mode fault-tolerant control for type-2 fuzzy systems with distributed delays

In this paper, the problem of sliding-mode fault-tolerant control is addressed for a class of uncertain nonlinear systems with distributed delays and parameter perturbations. By using interval type-2 Takagi–Sugeno (T–S) fuzzy models, the nonlinear systems are formulated , of which uncertain parameters and distributed state delays are represented in a unified type-2 fuzzy framework. In order to tackle with the uncertain parameters in pre-designed membership functions, an adaptive mechanism is utilized to manage the time-varying weightings corresponding to the upper membership functions. A simple linear sliding surface subject to several solvable matrix inequalities is designed by using a reduced-order system. To guarantee the stability of the overall dynamic system, an adaptive sliding mode controller is designed, which can compensate for both uncertainties and distributed delays. Finally, a truck-trailer model system is used in simulations to verify the applicability and effectiveness of the control and estimation schemes.

Interval-Boundary-Dependent Control for Interval Type-2 T–S Fuzzy Systems

This paper introduces a state-feedback controller for interval type-2 Takagi–Sugeno fuzzy systems that are described as a convex combination of linear subsystems via normalized time-varying fuzzy weighting parameters, which are unknown but belong to intervals. The introduced structure employs lower and upper interval boundaries directly, rather than alternative parameters generated by the interval boundaries in the studies, which does lead to a stabilization condition of parameterized linear matrix inequalities (LMIs), associated only with the parameters and their interval boundaries. The exact relationship among the parameters and their interval boundaries can, therefore, be fully used to convert parameterized LMIs into LMIs, which provides a great feature distinguished from those in the studies. Two illustrative examples demonstrate the effectiveness and robustness of the derived controller.

Strategic and Dynamic Temporal Weighting for Perceptual Decisions in Humans and Macaques.

Perceptual decision-making is often modeled as the accumulation of sensory evidence over time. Recent studies using psychophysical reverse correlation have shown that even though the sensory evidence is stationary over time, subjects may exhibit a time-varying weighting strategy, weighting some stimulus epochs more heavily than others. While previous work has explained time-varying weighting as a consequence of static decision mechanisms (e.g., decision bound or leak), here we show that time-varying weighting can reflect strategic adaptation to stimulus statistics, and thus can readily take a number of forms. We characterized the temporal weighting strategies of humans and macaques performing a motion discrimination task in which the amount of information carried by the motion stimulus was manipulated over time. Both species could adapt their temporal weighting strategy to match the time-varying statistics of the sensory stimulus. When early stimulus epochs had higher mean motion strength than late, subjects adopted a pronounced early weighting strategy, where early information was weighted more heavily in guiding perceptual decisions. When the mean motion strength was greater in later stimulus epochs, in contrast, subjects shifted to a marked late weighting strategy. These results demonstrate that perceptual decisions involve a temporally flexible weighting process in both humans and monkeys, and introduce a paradigm with which to manipulate sensory weighting in decision-making tasks.

Estimation and Explanation of Time-Varying Weights in Chinese CPI

ABSTRACTThe time-varying weights in the Chinese consumer price index (CPI) are estimated using a time-varying coefficient model, which is shown to be useful for testing the CPI’s “weighting biases” and adjustment problem. The empirical results show that China’s CPI has “weighting biases,” in which the time-varying intercept reveals negative adjustment of the CPI. This means that people pay less for food and more for medical care, education, and housing. By comparing the weights between urban CPI and rural CPI, the extended examination suggests a widening income gap between urban and rural households.

RETRACTED: TOPSIS time variant decision fusion model evaluation for internet of public service things

Abstract To improve the effectiveness of the performance analysis on the development and supply of public cultural service policies in urban communities, the collaborative data fusion recommendation algorithm of user satisfaction and feature approximation is adopted in this paper to establish the development and supply performance research algorithm of the public cultural service policies in urban communities from the horizon of public satisfaction. First, research is made according to the development model of public cultural service policies in urban communities from the horizon of public satisfaction to establish the performance valuation model. Two, the time-varying weight method is adopted to improve the standard TOPSIS fusion, enhance the time-varying attribute of TOPSIS decision fusion and realize the valid attribute fusion of the user similarity data. Third, the effectiveness of the proposed algorithm has been verified by making the experimental comparison according to the test case.

Temporally weighting a time varying noise field to improve Green function retrieval.

The authors consider the retrieval of Green functions G from the correlations of non-stationary non-fully diffuse noise incident on an array of sensors. Multiple schemes are proposed for optimizing the time-varying weights with which correlations may be stacked. Using noise records created by direct numerical simulation of waves in a two-dimensional multiply scattering medium, cases are shown in which conventional stacking does a poor job and for which the proposed schemes substantially improve the recovered G, rendering it more causal and/or more symmetric, and more similar to the actual G. It is found that the schemes choose weights such that the effective incident intensity distribution is closer to isotropic.

A dynamic algorithm for stochastic trust propagation in online social networks: Learning automata approach

The dynamic nature of trust has been universally accepted in the literature. As two users interact with each other, trust between them evolves based on their interaction's experience, in such a way that the level of trust increases if the experience is positive and otherwise, it decreases. Since social interactions in online networks, especially in activity and interaction networks, occur continuously in time, trust networks can be considered as stochastic graphs with continuous time-varying edge weights. This is while previous work on the trust propagation has assumed trust network as a static graph and developed deterministic algorithms for inferring trust in the graph. The problem becomes more challenging since trust propagation based algorithms are too time-consuming and therefore it is highly probable that trust weights change during their running time. In order to tackle this problem, this paper proposes a dynamic algorithm called DyTrust to infer trust between two indirectly connected users. The proposed algorithm utilizes distributed learning automata (DLA) to capture the dynamicity of trust during the trust propagation process and dynamically update the found reliable trust paths upon the trust variations. To the best of our knowledge, DyTrust is the first dynamic trust propagation algorithm presented so far. We conduct several experiments on the real trust network dataset, Kaitiaki, and evaluate the performance of the proposed algorithm DyTrust in comparison with the well-known trust propagation algorithms. The results demonstrate that by considering the dynamicity of trust, DyTrust can infer trust with a higher accuracy.

Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network

In the present paper, a new neural network is developed to improve the prediction accuracy of crude oil price fluctuations. The proposed model combines wavelet neural network (WNN) with random time effective function. WNN is a predictive system with the ability to implement strong nonlinear approximation. The random time effective function is applied to formulate the varied impact of historical data on current market, which endows historical data with time-variant weights to make them affect differently on the training process of WNN. Besides, the multiscale composite complexity synchronization (MCCS) is used as the new method to evaluate the predictive performance. The empirical experiments are implemented in predicting crude oil prices and moving average absolute return series of WTI and BRE. Through comparing with the traditional back propagation neural network (BPNN), support vector machine (SVM) and WNN models, the empirical results demonstrate that the proposed model has a higher accuracy in crude oil price fluctuations predicting and is advantageous in improving the precision of prediction.

The dynamic gammawarp auditory filterbank.

Auditory filterbanks are an integral part of many metrics designed to predict speech intelligibility and speech quality. Considerations in these applications include accurate reproduction of auditory filter shapes, the ability to reproduce the impact of hearing loss as well as normal hearing, and computational efficiency. This paper presents an alternative method for implementing a dynamic compressive gammachirp (dcGC) auditory filterbank [Irino and Patterson (2006). IEEE Trans. Audio Speech Lang. Proc. 14, 2222-2232]. Instead of using a cascade of second-order sections, this approach uses digital frequency warping to give the gammawarp filterbank. The set of warped finite impulse response filter coefficients is constrained to be symmetrical, which results in the same phase response for all filters in the filterbank. The identical phase responses allow the dynamic variation in the gammachirp filter magnitude response to be realized as a sum, using time-varying weights, of three filters that provide the responses for high-, mid-, and low-intensity input signals, respectively. The gammawarp filterbank offers a substantial improvement in execution speed compared to previous dcGC implementations; for a dcGC filterbank, the gammawarp implementation is 24 to 38 times faster than the dcGC Matlab code of Irino.

Life-cycle reliability assessment of reinforced concrete bridges under multiple hazards

This paper proposes a novel probabilistic methodology for estimating the life-cycle reliability of existing reinforced concrete (RC) bridges under multiple hazards. The life-cycle reliability of an RC bridge pier under seismic and airborne chloride hazards is compared to that of a bridge girder under traffic and airborne chloride hazards. When conducting a life-cycle reliability assessment of existing RC bridges, observational data from inspections can provide the corrosion level in reinforcement steel. Random variables related with the prediction of time-variant steel weight loss can be updated based on the inspection results using Sequential Monte Carlo Simulation (SMCS). This paper presents a novel procedure for identifying the hazards that most threaten the structural safety of existing RC bridges, as well as the structural components with the lowest reliability when these bridges are exposed to multiple hazards. The proposed approach, using inspection results associated with steel weight loss, provides a rational reliability assessment framework that allows comparison between the life-cycle reliabilities of bridge components under multiple hazards, helping the prioritisation of maintenance actions. The effect of the number of inspection locations on the updated reliability is considered by incorporating the spatial steel corrosion distribution. An illustrative example is provided of applying the proposed life-cyle reliability assessment to a hypothetical RC bridge under multiple hazards.

Improving the Forecasting Accuracy of Crude Oil Prices

Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk.

Optimal income taxation and migration

We tackle the issue of optimal dynamic taxation of capital income in an economy with disconnection as in Weil (J Public Econ 38:183–198, 1989), generated by migration and intra-family altruism. We show that, when the government aims at correcting such a disconnection using time-varying weights in the social welfare function, then there is room for nonzero capital income taxation, both in the short and in the long run.

Local Identification of LPV Dual-Rate System With Random Measurement Delays

This paper considers the problem of linear parameter-varying (LPV) dual-rate system identification with slow-rate output data corrupted by random measurement delays. The local approach is used, and the LPV model is built through convex combination of several local linear models with time-varying weights. The dual-rate sampled data often exist in industry, and the measured output data are inevitably corrupted by random measurement delays. In order to handle random measurement delays and dual-rate sampled data in LPV system identification, the statistical description of the identification problem is established based on the presented output-interpolated LPV dual-rate model, and then, the proposed algorithm to estimate all the unknown model parameters, real output data, and measurement delays are derived in the generalized expectation–maximization algorithm scheme. One simulation example and a practical chemical process are used to verify the efficiency of the proposed method.