Description: UMDA. a kind of estimation of distribution algorithm , which is the improvement of genetic algorithm-UMDA. A kind of estimation of distribution algorithm, which is the improvement of genetic algorithm Platform: |
Size: 47104 |
Author:sunxiaodian |
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Description: 在实时平台上,高斯混合模型(GMM)具有计算有效性和易于实现的优点。最大似然规则中,模型参数不
断更新,但由于爬山特征,任意的原始模型参数估计通常将导致局部最优 遗传算法(GA)适于求解复杂组合优化问
题及非线性函数优化。提出了基于说话人识别的可以解决GMM局部最优问题的GMM/GA新算法,实验结果表明,
提出的GMM/GA新算法比纯粹的GMM算法能获得更优的效果。
- In real-time platform, the Gaussian mixture model (GMM) with the calculation of the effectiveness and easy to realize benefits. Maximum likelihood rule, the model parameters are not
Broken updates, but due to climbing features, any of the original model parameter estimation will usually result in local optimum genetic algorithm (GA) is suitable for solving complex combinatorial optimization question
Title and non-linear function optimization. Proposed speaker recognition based on GMM can solve the problem of local optimal GMM/GA new algorithm, experimental results show that the
Proposed GMM/GA new algorithm than purely GMM algorithm can get better results. Platform: |
Size: 4448256 |
Author:于高 |
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Description: 模型利用协商历史中隐含的信息自动对数据进行标注以形成训练样本，用最小二乘支持向量回
归机学习此样本得到对手效用函数的估计，然后结合自己和对手的效用函数构成一个约束优化问题，用遗传算法求
解此优化问题，得到的最优解就是己方的反建议．实验结果表明，在信息保密和没有先验知识的条件下，此模型仍然
表现出较高的效率和效用-The proposed model labels the negotiation history data automatically by making full use of the implicit
information in negotiation history．Then，the labeled data become the training samples of least-squares support
vector machine that outputs the estimation of opponent’s utility function．After that，the self s utility function and
the estimation of opponent’s utility function constitute a constraint optimization problem that will be further figured
out by genetic algorithm．The optimal solution is the counter-ofer of onesel~ Experimental results show that the
proposed model is efective and efi cient in environments where information is private and the prior knowledge is
not available． Platform: |
Size: 514048 |
Author:11 |
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Description: Adaptive Blind Equalization Using Bottleneck Networks Implemented by Evolvable Hardware.Using a genetic
algorithm, the network on the hardware is trained to
minimize an energy function based on the maximum likelihood
estimation. Simulation results show that the proposed
equalizers have superior performance to popular CMA blind
equalizers. Platform: |
Size: 149504 |
Author:abd091 |
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Description: The paper presents an in situ parameter estimation method to determine the equivalent circuit parameters of the three-winding
transformer (TWT). The suggested method also estimates geometrically a complex parameter that is mutual leakage between
secondary and tertiary windings, which would be useful in transient studies. Beside the saturation effect of the transformer is taken
into account by estimating a highly nonlinear parameter that is magnetizing circuit reactance. Different search based optimization
tools are applied for parameter estimation among which the results obtained using genetic algorithm is found to be encouraging.
Load test data at one particular operating point namely supply voltage, load currents, input power, and load impedance is suffi cient
to estimate the parameters. Platform: |
Size: 129024 |
Author:fouzirock |
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Description: 针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题，提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点，采用可调整的算法模型对粒子群算法进行改进，将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理，将优化后的卡尔曼滤波器应用于感应电机转速估计。-
Extended Kalman Filter for induction motor speed estimation problem is difficult to obtain a Kalman filter system noise matrix and the measurement noise matrix optimal value proposed speed estimation method based on improved particle swarm optimization of the extended Kalman filter. By virtue of the genetic algorithm and particle swarm optimization algorithm fusion algorithm using the adjustable model PSO improvements that will improve the PSO extended Kalman filter system noise matrix and the measurement noise matrix optimization process, the optimized Kalman filter is applied to the induction motor speed estimate. Platform: |
Size: 1201152 |
Author:周 |
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Description: A novel method based on a combination of the Extended Kalman Filter (EKF) with Particle Swarm Optimization (PSO) to estimate the speed and rotor flux of an induction motor driveis presented. The proposed method will be performed in two steps. As a first step, the covariance matrices of state noise and measurement noise will be optimized in an off-line manner by the PSO algorithm. As a second step, the optimal values of the above covariance matrices are injected in our speed-rotor flux estimation loop (on-line).Computer simulations of the speed and rotor-flux estimation have been performed in order to investigate the effectiveness of the proposed method. Simulations and comparison with genetic algorithms (GAs) show that the results are very encouraging and achieve good performances. Platform: |
Size: 665750 |
Author:pudn0507@yahoo.fr |
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Description: The code helps to estimate the State of Charge (SOC ) by optimizing the battery parameters using Genetic Algorithm, Particle Swarm Optimization, and Battle Royal Optimization Platform: |
Size: 6660524 |
Author:phdabhay |
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Search - Estimation by genetic algorithm