We consider the problem of enabling a network of agents to estimate the state of a discrete-time nonlinear dynamical system. At each time step, each agent in the network receives a measurement characterized by a nonli...
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ISBN:
(纸本)9798350382662;9798350382655
We consider the problem of enabling a network of agents to estimate the state of a discrete-time nonlinear dynamical system. At each time step, each agent in the network receives a measurement characterized by a nonlinear function of the system state and exchanges information with its neighbors in the network. We propose an optimization-based estimator where agents collaboratively solve a distributed optimization problem while satisfying a communication constraint in the form of a fixed number of distributed optimization iterations at each estimation time step. Subject to the assumptions that the system is collectively observable, and the communication network is time-varying and strongly connected, we show that for any given lambda which satisfies 0 < lambda < 1, it is possible to choose q, the number of the distributed optimization iterations, so that the estimation error for each agent converges to zero at least as fast as lambda(t) does.
Sharp-Edged Width Constrictions (SEWC) are hydraulic structures designed to measure flow in open channels. Accurate prediction of the discharge coefficient (Cd) in SEWC is crucial for determining water discharge in th...
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Sharp-Edged Width Constrictions (SEWC) are hydraulic structures designed to measure flow in open channels. Accurate prediction of the discharge coefficient (Cd) in SEWC is crucial for determining water discharge in these channels. This information plays a key role in effective water resource management, supporting decision-making regarding the allocation and conservation of water for agricultural, industrial, and municipal purposes. This study introduces a novel hybrid machine learning model, combining Support Vector Regression (SVR) with the Improved Whale optimization Algorithm (IWOA). Additionally, advanced machine learning models such as NGBoost, AutoInt, and TabNet were employed to predict Cd in SEWC. The SVR-IWOA model offers automatic hyperparameter tuning, significantly enhancing prediction accuracy in complex flow conditions. To develop these models, a dataset consisting of 156 laboratory data points from SEWC experiments was utilized, with 75 % of the data allocated for training and 25 % for testing. The Isolation Forest (IF) algorithm was applied to detect and remove outliers, leading to the exclusion of 5.1 % of the original dataset. Dimensional analysis identified critical factors influencing Cd, including the ratio of upstream depth to opening width (h/b) and the constriction ratio (beta = b/B, where B is the channel width). The validity of these dimensionless parameters was confirmed using ANOVA and SHAP analyses, which highlighted beta as the most influential factor affecting Cd. Model performance was rigorously evaluated using multiple metrics, including the coefficient of determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Weighted Mean Absolute Percentage Error (WMAPE), and symmetric Mean Absolute Percentage Error (sMAPE). Comparative evaluations were conducted using Taylor Diagrams, Residual Error Curves (REC), and the Performance Index (PI). In the training stage, NGBoost demonstrated superior performance with a PI of 4994 an
The Cuckoo Search Algorithm (CSA) is an optimization algorithm inspired by the brood parasitism behavior of cuckoo birds. It mimics the reproductive and breeding tactics of cuckoos to tackle optimization challenges. T...
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The Cuckoo Search Algorithm (CSA) is an optimization algorithm inspired by the brood parasitism behavior of cuckoo birds. It mimics the reproductive and breeding tactics of cuckoos to tackle optimization challenges. To better handle multi-objective optimization problems (MOPs), a variation called the multi-objective CSA (MOCSA) has been developed. MOCSA is designed to uncover a spectrum of solutions, each providing a balance between various objectives, thereby allowing decision-makers to choose the optimal solution according to their specific preferences. The literature has witnessed a notable increase in the number of published MOCSAs, with MOCSA research papers recorded in the SCOPUS database. This paper presents a comprehensive survey of 123 distinct variants of MOCSAs published in scientific journals. Through this survey, researchers will gain insights into the growth of MOCSA, the theoretical foundations of multi-objective optimization and the MOCSA algorithm, the various existing MOCSA variants documented in the literature, the application domains in which MOCSA has been employed, and a critical analysis of its performance. In sum, this paper provides future research directions for MOCSA. Overall, this survey provides a valuable resource for researchers seeking to explore and understand the advancements, applications, and potential future developments in the field of multi-objective CSA.
This study addresses a research gap regarding the impact of dust accumulation on photovoltaic (PV) modules, with a specific focus on parameter extraction using single- and double-diode models (SDMs and DDMs) under dus...
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This study addresses a research gap regarding the impact of dust accumulation on photovoltaic (PV) modules, with a specific focus on parameter extraction using single- and double-diode models (SDMs and DDMs) under dusty conditions. While dust effects on PV performance are well-studied, few have explored how existing models can accurately represent these effects. Experimental data from outdoor testing of small-scale modules subjected to artificially deposited dust were analyzed. The direct current parameters were then extracted using the SDM and DDM, with the application of the improved snake optimization algorithm to enhance the accuracy. Preliminary analysis shows that the fill factor of dusty panels gradually increases, surpassing that of clean panels, due to increased absorption of diffuse light from reflections off the nonuniform dust layer. Efficiency uniformly decreases under dust presence. Computational comparison reveals a significant impact of dust on the algorithm's prediction quality, with maximum root mean square error decreases of 339.1% and 303.5% for DDM and SDM, respectively. The study observes that DDM effectively represents dust effects with fewer parameters than SDM, which includes more parameters conveying dust deposition effects. On average, DDM photocurrent values decrease by 24.2% due to dust, while shunt resistance decreases by 79.7%. For SDM, photocurrent decreases by 24.2%, shunt resistance by 80.1%, diode saturation current by 84.6%, and ideality factor by 10.5%. These findings suggest that current models inadequately represent dust effects, favoring SDM for its simplicity, while partial shading serves as a weak approximation.
Ferroelectric Random Access Memory (FRAM) by Texas Instruments (TI) is a non-volatile memory which allows lower power and faster data throughput compared to other nonvolatile solutions. These features have accelerated...
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ISBN:
(纸本)9781479968435
Ferroelectric Random Access Memory (FRAM) by Texas Instruments (TI) is a non-volatile memory which allows lower power and faster data throughput compared to other nonvolatile solutions. These features have accelerated the interest in this technology as the future of embedded unified memory, in particular in data logging, remote sensing and Wireless Sensor Network (WSN). The application of Model Predictive Control (MPC) in WSN has gained lot of attention in the last years and it requires solving convex optimization problems in real-time. In this paper several convex optimization algorithms have been implemented and compared on a FRAM-based MSP-EXP430FR5739 node by TI, to evaluate its suitability in extending the potentialities of onboard volatile Static Random Access Memory (SRAM) for embedded optimization-based control.
Most of the optimal guidance problems can be formulated as nonconvex optimization problems, which can be solved indirectly by relaxation, convexification, or linearization. Although these methods are guaranteed to con...
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ISBN:
(纸本)9798350382662;9798350382655
Most of the optimal guidance problems can be formulated as nonconvex optimization problems, which can be solved indirectly by relaxation, convexification, or linearization. Although these methods are guaranteed to converge to the global optimum of the modified problems, the obtained solution may not guarantee global optimality or even the feasibility of the original nonconvex problems. In this paper, we propose a computational optimal guidance approach that directly handles the nonconvex constraints encountered in formulating the guidance problems. The proposed computational guidance approach alternately solves the least squares problems and projects the solution onto nonconvex feasible sets, which rapidly converges to feasible suboptimal solutions or sometimes to the globally optimal solutions. The proposed algorithm is verified via a series of numerical simulations on impact angle guidance problems under state dependent maneuver vector constraints, and it is demonstrated that the proposed algorithm provides superior guidance performance than conventional techniques.
Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and on...
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ISBN:
(纸本)9781479938407
Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and one prediction dataset. This work was undertaken to investigate the effectiveness of using auto associative neural networks and optimization algorithms in missing data prediction and classification tasks. If performed appropriately, computational intelligence and optimization algorithm systems could lead to consistent, accurate and trustworthy predictions and classifications resulting in more adequate decisions. The results reveal GA, SA and PSO to be more efficient when compared to RF in terms of predicting the forest area to be affected by fire. GA, SA, and PSO had the same accuracy of 93.3%, while RF showed 92.99% accuracy. For the classification problems, RF showed 93.66% and 92.11% accuracy on the German credit and Heart disease datasets respectively, outperforming GA, SA and PSO.
The biological world is an ideal place for seeking inspiration for developing mathematical optimization algorithms. In this paper we propose two hybrid stochastic optimization algorithms that bear resemblance to the s...
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ISBN:
(纸本)9781479954964
The biological world is an ideal place for seeking inspiration for developing mathematical optimization algorithms. In this paper we propose two hybrid stochastic optimization algorithms that bear resemblance to the sexual reproduction cycle of Jellyfish and asexual reproductive cycle of species of Hydra. The performance of these two algorithms are investigated against other common optimization algorithms on a set of benchmark optimization problems. The results show that the proposed algorithms perform well.
It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measur...
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ISBN:
(纸本)9798350377712;9798350377705
It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated, which leads to degradation in the accuracy of PGO solutions that rely on this assumption. Therefore, in this paper we develop a novel algorithm derived from a realistic, correlated model of relative pose uncertainty, and we quantify the resulting improvement in the accuracy of the solutions we obtain relative to state-of-the-art PGO algorithms. Our approach utilizes Riemannian optimization on the planar unit dual quaternion (PUDQ) manifold, and we prove that it converges to first-order stationary points of a Lie-theoretic maximum likelihood objective. Then we show experimentally that, compared to state-of-the-art PGO algorithms, this algorithm produces estimation errors that are lower by 10% to 25% across several orders of magnitude of correlated noise levels and graph sizes.
Currently, the efficiency improvement of complex technical systems is an urgent scientific task, which in numerous cases is formalized via optimization problem and solved by some well-known optimization methods. Metah...
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