To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the s...
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To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.
In this paper a design condition for magnetically balanced operation in a single phase induction motor is presented in analytic forms. In addition a condition for minimal stator copper is deduced under condition that ...
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In this paper a design condition for magnetically balanced operation in a single phase induction motor is presented in analytic forms. In addition a condition for minimal stator copper is deduced under condition that the balanced operation is satisfied. Using these conditions, an optimal induction motor is designed by a proper optimization algorithm, and its loss characteristics are investigated. From the results, the validity of the proposed method is verified.
The medical image registration algorithm uses the mutual information measure function that has many local extremes. Therefore, we propose our medical image registration algorithm that combines generalized mutual infor...
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ISBN:
(数字)9783642134951
ISBN:
(纸本)9783642134944
The medical image registration algorithm uses the mutual information measure function that has many local extremes. Therefore, we propose our medical image registration algorithm that combines generalized mutual information with PSO-Powell hybrid algorithm and uses the objective measure function based on Renyi entropy. The Renyi entropy can remove the local extremes. We use the particle swarm optimization (PSO) algorithm to locate the measure function near the local extremes. Then we take the local extremes as initial point and use the Powell optimization algorithm to search for the global optimal solution. Section 2.2 of the paper presents the six-step procedure of our registration algorithm. We simulate medical image data with the registration algorithm;the simulation results, given in Table. 2 and 3, show preliminarily that the registration algorithm can eliminate the local extremes of objective measure function and accelerate the convergence rate, thus obtaining accurate and better registration results.
A hybrid evolutionary algorithm is applied to the design of interplanetary trajectories with multiple impulses and gravity assists. The optimization procedure runs three different optimizers based on genetic algorithm...
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A hybrid evolutionary algorithm is applied to the design of interplanetary trajectories with multiple impulses and gravity assists. The optimization procedure runs three different optimizers based on genetic algorithms, differential evolution, and particle swarm optimization "in parallel";the algorithms, which can also he employed separately, are used synergistically here by letting the best individuals, found by each-algorithm, migrate to the others at prescribed intervals. A comparison with the results presented In recent literature, which state that differential evolution is well suited to deal with this kind of problem, is carried out. The performance of the hybrid optimizer is comparable to that of differential evolution in terms of computational time and function evaluations when problems with a reduced number of variables are considered. The hybrid optimizers may instead exhibit better performance when more complex problems are dealt with. The results also show that the algorithm performance is remarkably improved by introducing a "mass mutation' operator to avoid premature converge nee to suboptimal solutions and by means of a particular choice of the variables to describe the trajectory.
In This paper, Fractal calculating dimension is firstly put forward. Combined Fractal theory with Neural network, A Fractal Neural network identification methods is built and applied to the state control and fault dia...
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ISBN:
(纸本)9780819478115
In This paper, Fractal calculating dimension is firstly put forward. Combined Fractal theory with Neural network, A Fractal Neural network identification methods is built and applied to the state control and fault diagnosis of Mechanical equipment. This network is made of three layers construct: Input layer, hide layer and output layer. Input and output of standard samples are respectively Fractal calculating dimension of different period sampling and the unit matrix equal to sample numbers. Weight and threshold of network is rapidly and correctly computed by conjugate terraced optimization. Rolling bearing fault is perfectly identified by this diagnosis way.
A large-scale powder-painting scheduling problem is explored. The purpose is to find out the optimal sequence of a number of batches that dynamically arrive from upstream processes within a given scheduling horizon. T...
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ISBN:
(纸本)9780878492800
A large-scale powder-painting scheduling problem is explored. The purpose is to find out the optimal sequence of a number of batches that dynamically arrive from upstream processes within a given scheduling horizon. The objective is to enhance the production efficiency and decrease the production cost as well. To solve this problem, a mixed integer nonlinear programming (MINLP) model is constructed and an algorithm called greedy randomized adaptive search procedure (GRASP) is designed. Case studies demonstrate that the proposed approach can improve the production performance significantly.
There is distortion phenomenon in image emerge, transmit and record. Image restoration is a process which recover bad image into original image. When we use genetic algorithm for image restoration, there will be prema...
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ISBN:
(纸本)9780769541105
There is distortion phenomenon in image emerge, transmit and record. Image restoration is a process which recover bad image into original image. When we use genetic algorithm for image restoration, there will be premature problem. The paper discusses a new algorithm for image restoration based on combination of parallel genetic algorithm with Hopfield neural network, take the advantage of parallel GA parameter selection and then use Hopfield NN to train sample efficiently. Experiments demonstrate that this optimization method in this paper will overcome premature problem and run more rapidly, as a result obtain a better recovery image.
This paper considers a two user Gaussian multiple-input single-output (MISO) broadcast channel with a per-antenna peak power constraint (or simply peak power constraint). It is more realistic to consider the peak powe...
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ISBN:
(纸本)9781424456383
This paper considers a two user Gaussian multiple-input single-output (MISO) broadcast channel with a per-antenna peak power constraint (or simply peak power constraint). It is more realistic to consider the peak power constraint on each transmit antenna because each antenna is equipped with its own power amplifier in many practical implementations. Assuming the perfect channel state information (CSI) at the transmitter, we propose an achievable scheme using a dirty-tape coding (DTC). The uniform input in a fixed range of the DTC scheme helps to control the peak power of the transmit signal easily. We also present an optimization algorithm that finds the capacity achieving beamforming vectors and power allocations under a per-antenna average power constraint used in our achievable scheme. Simulation results show that as the transmit power increases, the achievable rate region under the peak power constraint is getting close to the capacity region under the reduced per-antenna average power constraint by 1/3. Compared to a non-DTC scheme based on minimum mean square error (MMSE) beamforming, the proposed scheme performs better.
In this paper, we show Scatterometry simulation software which has the spectroscopy calculation and optimization algorithm systems. We analyze the spectral Scatterometry using the wavelength range of 400nm to around 8...
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ISBN:
(纸本)9780819480521
In this paper, we show Scatterometry simulation software which has the spectroscopy calculation and optimization algorithm systems. We analyze the spectral Scatterometry using the wavelength range of 400nm to around 800nm. The calculation is sped up by parallel computing using a multicore CPU. Threading Building Blocks (TBB) techniques are used in the parallel computing. We calculate the spectroscopy using the rigorous coupled wave analysis (RCWA) which provides a method for calculating the diffraction of electromagnetic waves by periodic grating. A conjugate gradient (CG) method is used to automatically search the data which resembles the given spectrum. In this simulation, we can check the sensitivity for profile measurements. And we provide the results using this simulator.
In view or show:timings of BP neural network, which is slow to converge and tends to nap in local optimum when applied in fault diagnosis. an approach for fault diagnosis based on BP neural network optimized by chaos ...
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ISBN:
(数字)9783642134951
ISBN:
(纸本)9783642134944
In view or show:timings of BP neural network, which is slow to converge and tends to nap in local optimum when applied in fault diagnosis. an approach for fault diagnosis based on BP neural network optimized by chaos ant colony algorithm is proposed Mathematical model of chaos ant colony algorithm is created Real-coded method is adopted and the weights and thresholds of BP neural network are taken as ant space position searched by chaos ant colony algorithm to train BP neural network naming result of chaos ant colony algorithm is compared with that of conventional BP algorithm and from both results it is can be seen that chaos ant colony algorithm can overcome the shortcomings of BP algorithm It is proved that mathematical model of chaos ant colony algorithm is correct and optimization method is valid through experimental simulation for machinery fault diagnosis of mine ventilator
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