In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- ma...In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm展开更多
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri...Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.展开更多
无线监测网络中多电台监测节点通过捕捉和分析无线用户的通信数据,可以达到监测网络行为、诊断网络故障和管理网络资源的目的,而为多电台监测节点优化选择工作信道、最大化捕获数据量、获得最佳网络监测质量(quality of monitoring,QoM...无线监测网络中多电台监测节点通过捕捉和分析无线用户的通信数据,可以达到监测网络行为、诊断网络故障和管理网络资源的目的,而为多电台监测节点优化选择工作信道、最大化捕获数据量、获得最佳网络监测质量(quality of monitoring,QoM)是一个关键问题。文章研究了一种基于同步微扰随机近似(SPSA)的信道选择算法。该算法在迭代过程中以随机扰动策略得到目标函数的近似梯度,引导搜索过程逐步逼近最优解;适合于复杂的多维优化问题求解,收敛速度快、复杂度低。实验结果表明,该算法可以实现无线监测网络中多电台监测节点的信道优化选择,并且性能优良。展开更多
基金the National Natural Science Foundation of China (No. 60404011)
文摘In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm
文摘Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
文摘无线监测网络中多电台监测节点通过捕捉和分析无线用户的通信数据,可以达到监测网络行为、诊断网络故障和管理网络资源的目的,而为多电台监测节点优化选择工作信道、最大化捕获数据量、获得最佳网络监测质量(quality of monitoring,QoM)是一个关键问题。文章研究了一种基于同步微扰随机近似(SPSA)的信道选择算法。该算法在迭代过程中以随机扰动策略得到目标函数的近似梯度,引导搜索过程逐步逼近最优解;适合于复杂的多维优化问题求解,收敛速度快、复杂度低。实验结果表明,该算法可以实现无线监测网络中多电台监测节点的信道优化选择,并且性能优良。
