SPSA

Application of SPSA Arithmetic to Real-time Dispatching Control in Public Transit Hub

SPSA在公交枢纽内车辆实时调度优化中的应用研究

SPSA algorithm and Powell algorithm were combined to optimize the parameter searching process .

配准参数搜索作用SPSA算法和鲍威尔算法相结合的优化策略。

Nonlinear conjugate gradient method based SPSA

基于非线性共轭梯度的同时扰动随机逼近方法

The algorithms integrate simulations , NN , genetic algorithm ( GA ), and SPSA .

该算法集成了模拟技术、神经网络、遗传算法和同步扰动随机逼近算法。

Two key issues in SPSA-based data-driven control are presented and then solved : data utility scheme and control-oriented SPSA method .

论文提出并解决了基于SPSA的数据驱动控制的两个关键问题：数据利用问题以及面向控制的SPSA方法问题。

The simultaneous perturbation stochastic approximation ( SPSA ) algorithm based on the random fuzzy simulation is provided to obtain the optimal production run length .

将随机模糊模拟和随机扰动近似算法结合，设计了获得最优生产时间的算法。

Convergence rate of moments for stochastic perturbation gradient approximation The algorithms can converge faster to the local optimal solutions than the SPSA algorithms based on simulations .

随机扰动梯度近似中的矩收敛率该算法集成了模拟技术、神经网络、遗传算法和同步扰动随机逼近算法。

We analyzed the activation of spsA promoter B1 in these mutant , complemented and overexpression strains in vivo and in vitro .

并在这些菌株中分析了spsABC的启动子B1的体内及体外活性。

First , simulations are used to generate a set of input-output data for the uncertain functions . Then the data are employed to train an NN , which is embedded in the SPSA algorithms .

首先使用模拟为不确定函数产生一组输入输出数据，然后用这些数据训练神经网络，把训练的神经网络嵌入到同步扰动随机逼近算法中。

In the SPSA algorithms based on simulations , much time will be spent on the simulations . Therefore , this dissertation designs the SPSA algorithms combining simulations with neural network ( NN ) .

在基于模拟的同步扰动随机逼近算法中，模拟花费的计算时间较多，为此，设计了集成模拟和神经网络的同步扰动随机逼近算法。

Uncertainties in the mathematical structure of a CLSS are modeled using fuzzy neural networks ( FNN ) and then its unknown parameters are tuned using a simultaneous perturbation stochastic approximation ( SPSA ) algorithm .

采用模糊神经网络对复杂大系统对象中的结构不确定性因素进行建模，并利用同步扰动随机近似算法对模型参数进行在线调整。

The output of neural network is the transit rate that is control measure , and we detailed show the frame of neural network , here the parameter estimation is completed by simultaneous perturbation stochastic approximation ( SPSA ) .

神经网络输出为入口匝道的放行率（即控制变量），并详细地阐述了神经网络的结构和训练算法，其中训练算法采用了SPSA（simultaneousperturbationstochasticapproximation）方法。

This dissertation proposes the simultaneous perturbation stochastic approximation ( SPSA ) algorithms based on simulations ( fuzzy simulation , fuzzy random simulation , and random fuzzy simulation ) to solve the fuzzy programming models , fuzzy random programming models , and random fuzzy programming models .

本文提出了基于模拟（模糊模拟、模糊随机模拟和随机模糊模拟）的同步扰动随机逼近算法求解模糊规划模型、模糊随机规划模型和随机模糊规划模型。