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.

H-infinity control for air-breathing hypersonic vehicle based on online simultaneous policy update algorithm

Purpose–The air-breathing hypersonic vehicle(AHV)includes intricate inherent coupling between the propulsion system and the airframe dynamics,which results in an intractable nonlinear system for the controller design.The purpose of this paper is to propose an H1 control method for AHV based on the online simultaneous policy update algorithm(SPUA).Design/methodology/approach–Initially,the H1 state feedback control problem of the AHV is converted to the problem of solving the Hamilton-Jacobi-Isaacs(HJI)equation,which is notoriously difficult to solve both numerically and analytically.To overcome this difficulty,the online SPUA is introduced to solve the HJI equation without requiring the accurate knowledge of the internal system dynamics.Subsequently,the online SPUA is implemented on the basis of an actor-critic structure,in which neural network(NN)is employed for approximating the cost function and a least-square method is used to calculate the NN weight parameters.Findings–Simulation study on the AHV demonstrates the effectiveness of the proposed H1 control method.Originality/value–The paper presents an interesting method for the H1 state feedback control design problem of the AHV based on online SPUA.

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.

Hardware Acceleration for SLAM in Mobile Systems

The emerging mobile robot industry has spurred a flurry of interest in solving the simultaneous localization and mapping(SLAM)problem.However,existing SLAM platforms have difficulty in meeting the real-time and low-pow-er requirements imposed by mobile systems.Though specialized hardware is promising with regard to achieving high per-formance and lowering the power,designing an efficient accelerator for SLAM is severely hindered by a wide variety of SLAM algorithms.Based on our detailed analysis of representative SLAM algorithms,we observe that SLAM algorithms advance two challenges for designing efficient hardware accelerators:the large number of computational primitives and ir-regular control flows.To address these two challenges,we propose a hardware accelerator that features composable com-putation units classified as the matrix,vector,scalar,and control units.In addition,we design a hierarchical instruction set for coping with a broad range of SLAM algorithms with irregular control flows.Experimental results show that,com-pared against an Intel x86 processor,on average,our accelerator with the area of 7.41 mm^(2) achieves 10.52x and 112.62x better performance and energy savings,respectively,across different datasets.Compared against a more energy-efficient ARM Cortex processor,our accelerator still achieves 33.03x and 62.64x better performance and energy savings,respec-tively.