The electrical-gas coupled power flow problem is one of the most basic problems under Energy Internet. In order to achieve a more complete analysis of the electric-gas coupled power flow, this paper establishes a nove...
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For battery management systems, it is significant to reliably estimate state-of-charge (SOC) from limited measurements in real time. Based on a nonlinear SOC-dependent equivalent circuit model, we propose a real-time ...
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The number of arithmetic units used in one-dimensional (1-D) discrete wavelet transform (DWT) is the main consideration for reducing the area of VLSI implementation of 1-D DWT, while the size of intermediate memory us...
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This paper presents an improved deep reinforcement learning (DRL) algorithm, namely Advanced Actor Critic (AAC), which is based on Actor Critic (AC) algorithm, to the video game Artificial intelligence (AI) training. ...
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Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a u...
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In this paper, distributed optimization problem is investigated under a second-order multi-agent network, in which each agent is described as the double integrator. The multi-agent network is introduced for solving a ...
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ISBN:
(纸本)9781509009107
In this paper, distributed optimization problem is investigated under a second-order multi-agent network, in which each agent is described as the double integrator. The multi-agent network is introduced for solving a large scale optimization problem by the cooperation of coupled agents. Based on the interaction over the network, the optimal solution of the problem can be obtained. Since the existing distributed algorithms for second-order multi-agent network enforce each agent to transmit complete information(both state information and derivation information of the independent variable, i.e., corresponded position and velocity information of the agent), this paper is motivated to design the distributed algorithm with only using the position information of neighbors, which reduces the requirement on communication bandwidth. With the help of Lyapunov analysis and La Sallel's Invariance Principle, the optimal solution is derived and the optimization problem is solved via the second-order multi-agent network. Finally, a numerical example is presented to illustrate the theoretical result.
In this paper, a novel terminal guidance law is proposed to solve the problem of exo-atmospheric interception. It is designed based on the proportional navigation (PN) and the classical optimal sliding-mode guidance (...
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In this paper, a novel terminal guidance law is proposed to solve the problem of exo-atmospheric interception. It is designed based on the proportional navigation (PN) and the classical optimal sliding-mode guidance (OSMG). It overcomes the shortcoming of these two traditional guidance laws and inherits their merits. Particularly, in the scenario of exoatmospheric interception, when the maneuvering information of the targets are unavailable , the proposed guidance law has superior performance than the traditional guidance law. Briefly, to enhance the interceptive performance, we introduce the optimal control and sliding-mode control methods to the guidance law for maneuvering target's interception. On the other hand, the traditional proportional navigation law is introduced to intercept the target with a constant speed. After that, a fuzzy switching function is provided to harmonize both situations above, according to the real-time estimation of maneuver. To guarantee the stability of the law, a Lyapunov based condition is obtained. Finally, different from the two-dimensional simulation in most of the literatures of terminal guidance law researches, we illustrate our method in the nonlinear discrete three-dimensional simulation environment by the Runge-Kutta method. Compared with the pure OSMG and PN, the proposed law performs better.
Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature select...
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Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for *** overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural ***,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.
Speckle is a granular noise that inherently exists in all types of coherent imaging systems. This paper presents a quantitative study on five despeckling methods such as frost filter, kuan filter, speckle reducing an ...
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