After the reference of straight line stability control strategy of four-wheel drive vehicle, this paper proposes a control algorithm combining the sliding mode variable structure and optimization control method. The c...
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After the reference of straight line stability control strategy of four-wheel drive vehicle, this paper proposes a control algorithm combining the sliding mode variable structure and optimization control method. The control algorithm is mainly divided into the upper generalized moment calculation based on the sliding mode variable structure controller and the lower torque distribution controller based on the optimization algorithm, also including the slip rates controller based on PID algorithm to ensure the straight line stability control. This paper establishes the combined model based on the CarSim and MATLAB, and tests to verify the validation of the control strategy through the four-wheel drive vehicle test-bed based on RT_LAB. The simulation and experimental results show that when the tire-road friction coefficient is low, the control strategy can not only make the vehicle tire slip rates stay near the optimal slip ratio, at the same time through the yawing moment adjustment, ensure the yaw angle of vehicle not beyond 0.5 deg/s, so the method can effectively ensure the straight line stability of four wheel drive vehicle. (C) 2016 Published by Elsevier Ltd.
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determi...
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ISBN:
(纸本)9781628411867
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.
In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during re...
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ISBN:
(纸本)9781538652619
In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i. e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.
In this paper, a method which employs Modified Teaching-Learning Based optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Energy Resources (DERs) units in distributio...
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ISBN:
(纸本)9781479949816
In this paper, a method which employs Modified Teaching-Learning Based optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Energy Resources (DERs) units in distribution systems. DERs are commonly connected near the load in electric power distribution systems and include renewable energy sources such as wind and solar, fossil-fuel-based generation such as micro turbines, and other distributed energy storage elements. Loss minimization and voltage profile improvement as objective function and for every combination of DERs, impact indices, active and reactive losses and voltage profiles is studied on different load models. For all cases current injection distribution load flow method is used and tested on 84-bus Taiwan Power Company distribution system using MTLBO algorithm.
optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local ...
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ISBN:
(纸本)9783642245527;9783642245534
optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local minimums, having low speed of convergence, and also having high order of complexity for implementation. In this paper, we introduce a new optimization algorithm, we called it Stem Cells algorithm (SCA), which is based on behavior of stem cells in reproducing themselves. SCA has high speed of convergence, low level of complexity with easy implementation process. It also avoid the local minimums in an intelligent manner. The comparative results on a series of benchmark functions using the proposed algorithm related to other well-known optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm and artificial bee colony (ABC) algorithm demonstrate the superior performance of the new optimization algorithm.
In this paper we present AOAB, the Automated optimization algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different bench...
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ISBN:
(纸本)9781450300735
In this paper we present AOAB, the Automated optimization algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different benchmarks with different parameter settings. Based on the results, AOAB can automatically perform comparisons between different algorithms and settings. It can aid the researcher to identify trends for good parameter settings and to find which algorithms are suitable for which type of problem. We introduce the system structure of AOAB (the server and the graphical client interface), define the way in which optimizers and benchmark functions can be implemented for the use in AOAB, and conduct an illustrative example experiment with our system: a comparison between Random Search and two Hill Climbers.
In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor re...
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ISBN:
(纸本)9781728101057
In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor reconstruction (FVR-kNN) has been proposed. However, dealing with fault problem caused by multiple variables, the algorithm needs to exhaustive the arrangement of all variables, resulting in high complex computation. And the multivariate estimation in FVR-KNN is not accurate. Thus, this paper proposes a variable recognition optimization algorithm based on FVR-kNN (OFVR-kNN). It optimizes the estimation steps of FVR-kNN in reconstructing multivariate, guaranteeing that the estimations of these potential fault variables have no mutual influence. According to the fault magnitude in corresponding direction, the fault variables are selected in turn. OFVR-kNN does not need to exhaustive all the combinations, greatly reducing the number of reconstructions in fault sample. In this paper, the validity of the optimization method is proved in Tennessee Eastman process.
In order to improve the accuracy of wind speed prediction, a wind speed prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), long short-term memory (LSTM) and gray w...
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ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
In order to improve the accuracy of wind speed prediction, a wind speed prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), long short-term memory (LSTM) and gray wolf optimization (GWO) algorithm was proposed from the perspective of reducing wind speed nonstationarity and optimizing combination weight. First, CEEMDAN was used to decompose the observed wind speed into a series of sub-sequences reflecting the characteristics of the original wind speed. Then the subsequence is predicted by LSTM, and the predicted value of the subsequence is output. Finally, the combined weight of the sub-sequences was optimized by GWO, and the sub-sequences were combined to obtain the wind speed prediction results. The experimental results show that CEEMDAN-LSTM-GWO wind speed prediction model proposed in this study has better performance than the comparison model.
This paper studies the optimization problem of PCB assembly time for multi-head placement machine. Mathematical model is built and analyzed for the problem, which is of a combinatorial nature and computationally intra...
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ISBN:
(纸本)9781538629185
This paper studies the optimization problem of PCB assembly time for multi-head placement machine. Mathematical model is built and analyzed for the problem, which is of a combinatorial nature and computationally intractable. An optimization algorithm based on heuristic strategy and scatter search method is proposed to minimize the PCB assembly time. By relaxing the restrictions on the problem, the algorithm reduces the assembly time by minimizing cycles of pick-and-place, constructing the simultaneous pickups and optimizing sequence of pick-and-place of components. Numerical experiments were conducted to evaluate the proposed algorithm, along with a comparison with a heuristic algorithm(HA) under strong constraints proposed in existed literature. The results show that the proposed algorithm has better performance in optimization results and can shorten PCB assembly time of multi-head placement machine effectively.
As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated a...
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ISBN:
(纸本)9781467374439
As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated as nonlinear optimization problems. A chaos Particle Swarm optimization(PSO) algorithm combined with multi-agent scheme is proposed for the thrust allocation in this paper. The algorithm which uses multi-agent topological structure has three functions that keeps the diversity of the particle swarm population, improving particle swarm global search ability, and enhancing information diversity. Relying on chaotic local search to get rid of local optima, it can also improve the convergence precision. The numerical simulations are conducted to demonstrate the effectiveness of the proposed methods, and the results are compared with PSO algorithm.
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