In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR),irrelevant or correlated features in the samples could spoil the performance of the SVR classifier,leading to decrease of ...
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In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR),irrelevant or correlated features in the samples could spoil the performance of the SVR classifier,leading to decrease of prediction *** order to solve the problems mentioned above,this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of ***,the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit ***,we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR).The experiment indicates that the proposed method is quite effective and ubiquitous.
A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermomet...
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A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermometer is effectively *** dynamic compensator is established and the compensation is described and explicated by the Wiener *** to Wiener model,the novel structure is *** identification of thermometer non-linear dynamic compensator is achieved by particleswarmoptimization *** results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.
This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway *** the stopping times for all passenger trains,minimizing travel distance of empty trains and min...
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This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway *** the stopping times for all passenger trains,minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the *** a given travel demand and specified capacity of stops,the model is solved by heuristic *** improved discrete particleswarmoptimization(PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and *** the algorithm,a stop based representation is designed,and a new method is used to update the position and velocity of *** order to keep the particleswarmalgorithm from premature stagnation,the simulated annealing algorithm,which has local search ability,is combined with the PSO algorithm to make elaborate search near the optimal solution,then the quality of solutions is improved *** empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the *** experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm,and an optimal set of stopping schemes can always be generated for a given *** achieve the best planning outcome,the stopping schemes should be flexibly planned,and not constrained by specific ones as often set by the planner.
Standalone multi-element complementary microgrid plays a very important role in solving the electricity problems of many areas, which are rich in renewable energy but have problems in the power supply of traditional p...
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Standalone multi-element complementary microgrid plays a very important role in solving the electricity problems of many areas, which are rich in renewable energy but have problems in the power supply of traditional power grid. To ensure the reliability of power supply while improving overall economic and environmental operation in micro-grid system, we have to optimize the operation of the system according to actual conditions of the system. This paper considered operating costs of the system and gas pollution emissions, established an optimization model. And the dispatch strategies are designed. Finally, utilized adaptive mutation particle swarm optimization algorithm to solve operational problems. Specific case study results verified the reasonableness and effectiveness of the algorithm.
The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorith...
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The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorithms have been applied in this field and an essential aspect of unmanned vehicles autonomy is the ability for automatic path planning. In this paper, particle swarm optimization algorithm as one of new swarm intelligent optimization methods is introduced into a path planning for autonomous vehicle, which is constructed of a particle representation methods for vehicle routing problem with fast convergence speed. The results show that the particle swarm optimization algorithm can obtain the solution of the vehicle routing problem quickly and effectively. It is a good method for solving the vehicle routing problem.
When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is ...
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ISBN:
(纸本)9781479974566
When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is because, the PSS is low cost, flexible and easy to implement. In this work the problem is designed as a single objective optimization technique to tune the PSS parameters with Eigenvalue analysis. Here the Invasive Weed optimizationalgorithm which is found suitable for these types of problems is selected as a tool to find optimal solutions. Simulations are executed on a 4-machine power system for different operating conditions such as heavy load, light load and capacitive load. The results are obtained under different fault conditions with Invasive Weed optimization technique and compared the same with PSO. At last it is observed that the IWO technique performs better in damping overshoot and settling time.
In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the ...
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ISBN:
(纸本)9781510813465
In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the total cost including the initial investment, fuel costs, net loss costs, environmental damage costs, operation and maintenance costs and power purchase costs. To improve the acceptance ability of the distribution network, and to ensure the economy. Then, using the particle swarm optimization algorithm to solve a typical example, to show that the model and the proposed method is correct and effective.
In the paper, Radial Basis Function(RBF) neural network optimized by particle swarm optimization algorithm is proposed to fault diagnosis for embedded system. The parameters of Radial Basis Function(RBF) neural networ...
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In the paper, Radial Basis Function(RBF) neural network optimized by particle swarm optimization algorithm is proposed to fault diagnosis for embedded system. The parameters of Radial Basis Function(RBF) neural network are selected by particle swarm optimization algorithm to solve the problem of the parameters selection of Radial Basis Function(RBF) neural network. In order to testify the superiority of Radial Basis Function(RBF) neural network optimized by particle swarm optimization algorithm in fault diagnosis for embedded system, traditional Radial Basis Function(RBF) neural network and Back Propagation(BP) neural network are applied to compare with the proposed Radial Basis Function(RBF) neural network optimized by particle swarm optimization algorithm. The comparison results show that the diagnosis accuracy of Radial Basis Function(RBF) neural network optimized by particle swarm optimization algorithm is the best methods among traditional Radial Basis Function(RBF) neural network, Back Propagation(BP) neural network and Radial Basis Function(RBF) neural network optimized by particle swarm optimization algorithm.
With the rapid development of urban rail transit, the problem of energy consumption is drawing more and more attentions. Based on the utilization of regenerative braking energy among trains, this paper studies the ene...
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ISBN:
(纸本)9781467365970
With the rapid development of urban rail transit, the problem of energy consumption is drawing more and more attentions. Based on the utilization of regenerative braking energy among trains, this paper studies the energy-efficient operation method. We propose an utilization strategy of regenerative energy among trains. Based on the proposed strategy, we set up the energy-saving model which optimizes the timetable and speed profile of trains simultaneously. Based on the particle swarm optimization algorithm, taking Beijing Yizhuang subway line and Changping subway line as examples of short section and long section for simulation, we verify the effectiveness of the proposed method. It is showed that the energy consumption of Yizhuang line and Changping line can be reduced by 3.2% and 5.5%, respectively, through improving the utilization of regenerative energy.
An energy storage system (ESS) can provide regulation and reserve capacities for the power system, and hence alleviate the negative impacts of renewable energy such as wind power. Nonetheless, challenges remain as to ...
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ISBN:
(纸本)9781510830059
An energy storage system (ESS) can provide regulation and reserve capacities for the power system, and hence alleviate the negative impacts of renewable energy such as wind power. Nonetheless, challenges remain as to how the operation of wind farms and ESSs can be coordinated efficiently, and how transmission system expansion planning will be impacted. This paper addresses these challenges by: (1) examining the correlation between the cost of ESS and its cycle life, exampled with a Natrium Sulfur (NaS) ESS; (2) investigating the integrated operation of a wind farm and an ESS taking into account both the cost of ESSs and their smoothing effect of wind power fluctuations; (3) developing a transmission system expansion planning model with particular reference to both the investment cost of the transmission system concerned and the operating costs of wind farms and ESSs; and (4) solving the developed planning formulation using an improved particle swarm optimization algorithm. The proposed planning method is illustrated through its applications to two sample systems.
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