Time-varying Weights Research Articles

A new redefined model of Firefly Algorithm with application to strategic bidding problem in Power Sector

Real-life optimization problems require an effective mechanism that completely utilizes the search space to obtain optimal solutions. A designer has always an opportunity to propose a new effective solution technique to address this issue. In this order, this paper presents a new upgraded redefined model of nature-inspired state of art meta-heuristic firefly algorithm (FA). FA is centered on swarm intelligence, which is motivated by the flashing pattern and behavior of fireflies. However, for a few instances, FA has a tendency to trap in local optima and it exhibits slow convergence. The proposed model is enabled through Time-varying Inertia Weight (TIW), Opposition Based Learning (OBL) and hybridized with sine cosine operators to get updated positions of search agents. A set of twenty-two classical benchmark function problems with numerous range and features are employed to prove the efficacy of the proposed model. Also, the effects of the above-mentioned modifications during the whole optimization routine are analyzed through different statistical and numerical analyses. The result analysis proves that the proposed modifications make FA more compatible. The proposed model is also tested on the strategic bidding problem of the power market of two different test systems with single and multi-trading hours. All the reported results confirm the supremacy of the proposed redefined model of FA.

Propagation of economic shocks from Russia and Western European countries to CEE-Baltic countries

What is the relative importance of Russia and Western European countries on Central and East European and Baltic (CEE-Baltic) countries? This paper tries to address this question by quantifying and comparing the spillover effects of growth and trade shocks coming out of Russia and five major Western European countries on ten CEE-Baltic countries by using a global vector autoregression (GVAR) model. In constructing the foreign variables, a time-varying trade weight is used instead of a fixed weight in order to take account of the financial crisis of 2007–2008 and the recent economic sanctions on Russia. The results show that growth spillover effects are strong in the region. However, shocks to Russia have higher and persistent spillover effects on CEE-Baltic countries compared to shocks to Western European countries. Spillover effects of growth shocks also show that Russia is affected more by Western European countries than the other way round. Trade balance shocks, on the other hand, do not play an important role in this transmission process.

Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome.

An accurate assessment of the safety or effectiveness of drugs in pharmaco-epidemiological studies requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the outcome of interest. To address this issue, we develop, and validate in simulations, a new approach for flexible modeling of the cumulative effects of time-varying exposures on repeated measures of a continuous response variable, such as a quantitative surrogate outcome, or a biomarker. Specifically, we extend the linear mixed effects modeling to estimate how past and recent drug exposure affects the way individual values of the outcome change throughout the follow-up. To account for the dosage, duration and timing of past exposures, we rely on a flexible weighted cumulative exposure methodology to model the cumulative effects of past drug use, as the weighted sum of past doses. Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of doses taken at different times in the past. In simulations, we evaluate the performance of the model under different assumptions concerning (i) the shape of the weight function, (ii) the sample size, (iii) the number of the longitudinal observations and (iv) the intra-individual variance. Results demonstrate the accuracy of our estimates of the weight function and of the between- and within-patients variances, and good correlation between the observed and predicted longitudinal changes in the outcome. We then apply the proposed method to re-assess the association between time-varying glucocorticoid exposure and weight gain in people living with rheumatoid arthritis.

Behavioral agent-based framework for interacting financial markets

PurposeThis paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored.Design/methodology/approachThe agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders.FindingsThe proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure.Practical implicationsArtificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability.Originality/valueThis paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

Single Image Snow Removal Using Sparse Representation and Particle Swarm Optimizer

Images are often corrupted by natural obscuration (e.g., snow, rain, and haze) during acquisition in bad weather conditions. The removal of snowflakes from only a single image is a challenging task due to situational variety and has been investigated only rarely. In this article, we propose a novel snow removal framework for a single image, which can be separated into a sparse image approximation module and an adaptive tolerance optimization module. The first proposed module takes the advantage of sparsity-based regularization to reconstruct a potential snow-free image. An auto-tuning mechanism for this framework is then proposed to seek a better reconstruction of a snow-free image via the time-varying inertia weight particle swarm optimizers in the second proposed module. Through collaboration of these two modules iteratively, the number of snowflakes in the reconstructed image is reduced as generations progress. By the experimental results, the proposed method achieves a better efficacy of snow removal than do other state-of-the-art techniques via both objective and subjective evaluations. As a result, the proposed method is able to remove snowflakes successfully from only a single image while preserving most original object structure information.

Testing Purchasing Power Parity in Cambodia: Time-Varying Trade Weights in Constructing Real Effective Exchange Rate

ABSTRACT The main aim of this study is to figure out whether the PPP hold or not in Cambodia? This research thoroughly takes into account major controversies which many researchers cogitate to be the cause of PPP breakdown. The aforementioned controversies are the using of the bilateral exchange rate which consists of short time-series or the using of multilateral or real effective exchange rate (REER) constructed by the fixed trade weight, the implementing of only the most popular unit root or stationary tests, such as ADF and PP tests to test for PPP. Thereof, this shortcoming is fulfilled through the implementing of another alternative stationary test known as the KPSS test on the monthly constructed REER from January 1995 to July 2019. The total data points or months of 295 is constructed using time-varying trade weights from 1995 to 2019 of Cambodia. After attempting to deal with the controversies thereto, the empirical result indicates that the PPP theory holds in Cambodia based on the result of the ADF test which is modeled with constant and trend. The test result indicates that the REER of Cambodia has a mean reverting process. Keywords: PPP, REER, Time-Varying Trade Weights, Unit Root Test.

Multi-Agent Trajectory-Tracking Flexible Formation via Generalized Flocking and Leader-Average Sliding Mode Control

This paper reports a flexible (or time-varying) multi-agent formation approach with average trajectory tracking for second-order integral multi-agent networks with single virtual leaders. The approach is developed by means of time-varying Olfati-Saber flocking algorithms, and sliding mode control (SMC) in terms of the leader-average dynamics. More precisely, SMC-specifying average trajectory tracking is combined with flexible multi-agent flocking driven by the Olfati-Saber flocking algorithms with time-varying weighting norm. Existence conditions and properties of the suggested multi-agent formation are examined rigorously, together with implementation formulas. It is shown that by designing the sliding surface and the time-varying weighting matrix appropriately, flexible formation with finite-time trajectory tracking can be achieved, free of control action chattering; moreover, the sliding mode control and formation control can be designed separately. Numerical examples are given to illustrate the main results.

Dissipative Consensus Tracking of Fuzzy Multi-Agent Systems Via Adaptive Protocol

This article considers the design problems of adaptive consensus protocol for nonlinear multi-agent systems under directed topology and external disturbance. Considering external disturbance, we propose a new polynomial fuzzy modeling approach. Based on the monotonically increasing functions, novel centralized adaptive protocol and distributed consensus protocol are designed respectively to guarantee that all agents can reach agreement. The distributed protocol is independent of global information of network topology and thereby is fully distributed. By constructing Lyapunov functions, SOS-based relaxed sufficient criteria are presented to achieve consensus. In addition, the time-varying coupling weights approach finite steady-state values. A simulation example is provided to demonstrate the effectiveness of the proposed adaptive consensus techniques.

Dynamic Adaptive Event-Triggered Scheme for General Linear Multi-Agent Systems

Abstract This paper proposes a novel dynamic adaptive event-triggered scheme to deal with consensus problems in a class of general linear multi-agent systems. Firstly, a fully distributed event-triggered consensus protocol is proposed by assigning a time-varying coupling weight for each edge. Then by introducing an additional internal dynamic variable, a dynamic adaptive event condition is skillfully constructed and Zeno behavior is also successfully excluded. Based on this, the asymptotic consensus is eventually achieved and the frequency of communication among agents is significantly reduced. Finally, one simulation example is provided to verify the effectiveness of the proposed scheme.

High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

The current research on multiple information fusion of remote sensing images is mainly aimed at remote sensing images of specific satellite sensors, and cannot be extended to other types of data source images. For high-resolution remote sensing images, when its surface coverage changes significantly, most of the mainstream algorithms are difficult to restore satisfactorily. The algorithm proposed in this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images for the first time to solve the above problems. The algorithm proposed in this paper first simulates the human visual mechanism, and obtains the spatial, spectral, and temporal features of the remote sensing image through the spatial spectral dictionary learning and the time-varying weight learning model. Secondly, local constraints are added to the extraction of temporal features to obtain temporal and geographical change information of heterogeneous remote sensing images. Then, a sparse representation model combining space-spectrum-time features is proposed to extract features of high-resolution remote sensing images. Finally, based on the VGG-16 network, this paper proposes a target recognition network with deep fully convolutional network, and uses the extracted feature map as the input of the target recognition network to realize the target recognition of the remote sensing image. Experimental results show that the method proposed in this paper can improve the accuracy of target recognition and improve the accuracy of recognition.

Mid- and long-term runoff prediction based on time-varying weight combination and Bayesian correction

Mid- and long-term runoff prediction based on time-varying weight combination and Bayesian correction

Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging

A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.

Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

In the process of multi-axis contour tracking control, the traditional time-invariant method could lead to a significant error in contour tracking due to the existence of two different motion conditions, namely single-axis independent motion and multi-axis coupled motion. In order to tackle this issue, a time-varying weighting matrix has been developed considering the trajectory and time-varying random disturbance. In this paper, a time-varying control method for multi-axis motion based on norm optimal cross-coupling iterative learning is proposed. Compared to the time-invariant control method, the simulation and experiment results demonstrate that the proposed method can effectively reduce the contour error improving the multi-axis control precision.

Random Gradient-Free Optimization for Multiagent Systems With Communication Noises Under a Time-Varying Weight Balanced Digraph

In this paper, we focus on a constrained convex optimization problem of multiagent systems under a time-varying topology. In such topology, it is not only B-strongly connected, but the communication noises are also existent. Each agent has access to its local cost function, which is a nonsmooth function. A gradient-free random protocol is come up with minimizing a sum of cost functions of all agents, which are projected to local constraint sets. First, considering the stochastic disturbances in the communication channels among agents, the upper bounds of disagreement estimate of agents’ states are obtained. Second, a sufficient condition on choosing step sizes and smoothing parameters is derived to guarantee that all agents almost surely converge to the stationary optimal point. At last, a numerical example and a comparison are provided to illustrate the feasibility of the random gradient-free algorithm.

Serial Fibroblast Growth Factor 23 Measurements and Risk of Requirement for Kidney Replacement Therapy: The CRIC (Chronic Renal Insufficiency Cohort) Study

Studies using a single measurement of fibroblast growth factor 23 (FGF-23) suggest that elevated FGF-23 levelsare associated with increased risk for requirement for kidney replacement therapy (KRT) in patients with chronic kidney disease. However, the data do not account for changesin FGF-23 levels as kidney disease progresses. Case-cohort study. To evaluate the association between serial FGF-23 levels and risk for requiring KRT, our primary analysis included 1,597 individuals in the Chronic Renal Insufficiency Cohort Study who had up to 5 annual measurements of carboxy-terminal FGF-23. There were 1,135 randomly selected individuals, of whom 266 initiated KRT, and 462 individuals who initiated KRT outside the random subcohort. Serial FGF-23 measurements and FGF-23 trajectory group membership. Incident KRT. To handle time-dependent confounding, our primary analysis of time-updated FGF-23 levels used time-varying inverse probability weighting in a discrete time failure model. To compare our results with prior data, we used baseline and time-updated FGF-23 values in weighted Cox regression models. To examine the association of FGF-23 trajectory subgroups with risk for incident KRT, we used weighted Cox models with FGF-23 trajectory groups derived from group-based trajectory modeling as the exposure. In our primary analysis, the HR for the KRT outcome per 1 SD increase in the mean of natural log-transformed (ln)FGF-23 in the past was 1.94 (95% CI, 1.51-2.49). In weighted Cox models using baseline and time-updated values, elevated FGF-23 level was associated with increased risk for incident KRT (HRs per 1 SD ln[FGF-23] of 1.18 [95% CI, 1.02-1.37] for baseline and 1.66 [95% CI, 1.49-1.86] for time-updated). Membership in the slowly and rapidly increasing FGF-23 trajectory groups was associated with ∼3- and ∼21-fold higher risk for incident KRT compared to membership in the stable FGF-23 trajectory group. Residual confounding and lack of intact FGF-23 values. Increasing FGF-23 levels are independently associated with increased risk for incident KRT.

Construction of China’s financial conditions index in the post-crisis era

Purpose The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations. Design/methodology/approach The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation. Findings Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI. Originality/value FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

A Bayesian Signals Approach for the Detection of Crises

In this paper, we consider the signals approach as an early-warning-system to detect crises. Crisis detection from a signals approach involves Type I and II errors which are handled through a utility function. We provide a Bayesian model and we test the effectiveness of the signals approach in three data sets: (1) Currency and banking crises for 76 currency and 26 banking crises in 15 developing and 5 industrial countries between 1970 and 1995, (2) costly asset price booms using quarterly data ranging from 1970 to 2007, and (3) public debt crises in Europe in 11 countries in the European Monetary Union from the introduction of the Euro until November 2011. The Bayesian model relies on a vector autoregression for indicator variables, and incorporates dynamic factors, time-varying weights in the latent composite indicator and special priors to avoid the proliferation of parameters. The Bayesian vector autoregressions are extended to a semi-parametric context to capture non-linearities. Our evidence reveals that our approach is successful as an early-warning mechanism after allowing for breaks and nonlinearities and, perhaps more importantly, the composite indicator is better represented as a flexible nonlinear function of the underlying indicators.

Adaptive Range-Based Target Localization Using Diffusion Gauss–Newton Method in Industrial Environments

In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results. Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss–Newton (GN) algorithm with cooperation strategy is proposed for solving target localization non linear-least-squares problem in a WSN. The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy. When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN. To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements. Instead of using a static combiner, the new algorithm leverages the evolutionary game theory to assign a time-varying weight for the estimate from each neighboring node based on the individual range error. Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise. We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function. Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment. The effectiveness of energy–accuracy tradeoff is also validated.

Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.

Position control of a tail-sitter UAV using successive linearization based model predictive control

A successive linearization based model predictive control (SLMPC) method is proposed to control a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hovering flight. The dynamic model of the vehicle is derived, including a low-fidelity aerodynamic model and a propulsion system model. The position controller is developed by a state–space prediction model augmented with estimated disturbance and feedback integration terms. The time-varying weight in the objective function is included and the velocity of vehicle is considered as reference to improve the performance. The system is first tested in a software-in-loop environment followed by the real-time indoor flight tests. The results demonstrate the vehicle can precisely follow a trajectory and stably hold position under unsteady wind disturbance