Simultaneous Perturbation Stochastic Approximation Algorithm Combined with Neural Network and Fuzzy Simulation

In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

Joint state and parameter estimation in particle filtering and stochastic optimization

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

An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks

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.

DESIGN OF SPARSE ARRAY FOR MAD IMAGING BASED ON MAXIMIZING INFORMATION CAPACITY

In past years,growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array.An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible,meanwhile without deteriorating imaging quality.For this end,we propose a novel methodology of arranging sensors in an optimal manner,exploring the concept of information capacity developed originally in the communication society.The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem,which can be resolved efficiently using widely adopted Simultaneous Perturbation and Statistical Algorithm(SPSA).Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.

Optimal Packet Size of Underwater Sensor Networks

Parameter optimization of nodes communication is the foundation of underwater sensor networks.The packet size is an important indicator of the impact of communication performance.As a result,the optimal packet size selection is a critical issue in improving the communication performance.This paper aims to make a model reflecting the communication characteristics as the optimization target,because underwater sensor networks have the characteristics of high time delay,high energy consumption and high bit error rate.Finally,simulation experiments and theory have demonstrated the effectiveness and timeliness of simultaneous perturbation stochastic approximation(SPSA) algorithm.

VISCAL： Heuristic algorithm based application tool to calibrate microscopic simulation parameters

VISCAL （VISSIM calibration） is an automated calibration tool for microscopic simulation parameters in VISSIM environment, based on three heuristic optimization algorithms： （a） genetic algorithm （GA）; Col simultaneous perturbation stochastic approximation （SPSA）; （c） simulated annealing （SA）. It is developed with a goal to automate and ease the tedious process of calibration, offering greater flexibility to the users by providing control on every aspect of the calibration process. It includes multiple features for a generic application tool with the ability to test the significance of the appropriate decision parameter set for a particular network, to determine the most suitable objective function to reflect network characteristics, and to check the suitability of any of the three heuristic optimization al- gorithms for a particular network. VISCAL also offers four objective function choices into the system： （1） speed, （2） flow, （3） delay, and （4） multi-objective criteria. It is able to calibrate all the driving behavior parameters for any type （urban, rural） and extent of network （small or large network）. However, for this study, the operation of the tool is tested by a dataset obtained from a 3.26 km freeway of Dhaka, Bangladesh.

Formulation and solution for calibrating boundedly rational activity-travel assignment:An exploratory study

Parameter calibration of the traffic assignment models is vital to travel demand analysis and management.As an extension of the conventional traffic assignment,boundedly rational activity-travel assignment(BR-ATA)combines activity-based modeling and traffic assignment endogenously and can capture the interdependencies between high dimensional choice facets along the activity-travel patterns.The inclusion of multiple episodes of activity participation and bounded rationality behavior enlarges the choice space and poses a challenge for calibrating the BR-ATA models.In virtue of the multi-state supernetwork,this exploratory study formulates the BRATA calibration as an optimization problem and analyzes the influence of the two additional components on the calibration problem.Considering the temporal dimension,we also propose a dynamic formulation of the BR-ATA calibration problem.The simultaneous perturbation stochastic approximation algorithm is adopted to solve the proposed calibration problems.Numerical examples are presented to calibrate the activity-based travel demand for illustrations.The results demonstrate the feasibility of the solution method and show that the parameter characterizing the bounded rationality behavior has a significant effect on the convergence of the calibration solutions.