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Daidai Chen

Bio: Daidai Chen is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Stability (learning theory) & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 12 citations.

Papers
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Journal ArticleDOI
TL;DR: VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability, and the simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS- RBF.
Abstract: The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: An OBFs detector based on fast convergence wavelet artificial neural network (FC-W-ANN), which can detect abnormal magnetic signals under low SNR and has higher training accuracy and better stability is proposed.

10 citations

Journal ArticleDOI
TL;DR: A novel control system to solve the deviation of system feedback by using dynamic lift and the mechanical decoupling method of fin hydrodynamic force and the lift measurement method based on double bearing load are proposed.
Abstract: For improving the control system of traditional ship fin stabilizers, this paper presents a novel control system to solve the deviation of system feedback by using dynamic lift. However, the difficulty of lift feedback control is lift measurement technology. Thus, the lift sensor is designed using Spoke-type structure and Wheatstone bridge-type strain conversion circuit. The design of compensation resistors is adopted in the circuit, which effectively reduces the effect of temperature on zero drift and sensitivity for the lift sensor. In addition, the mechanical decoupling method of fin hydrodynamic force and the lift measurement method based on double bearing load are proposed. In addition, then, the control system of ship fin stabilizer is improved in order to solve the main technical problems. Finally, the experimental results demonstrate that the designed sensors meet the requirements of lift measurement. In addition, the simulation results of the ship at different situations show that the effect of roll stabilization (86.803%–93.858%) is improved effectively.

6 citations

Journal ArticleDOI
TL;DR: A novel Takagi-Sugeno fuzzy robust control (NTSFRC) is designed, which keeps cost and inventory low and robust stability is guaranteed and part is supplied in time under a low cost in comparation with robust H∞ control strategy with particle swarm optimization.

4 citations

Journal Article
TL;DR: The results show that promoting the capability of flight deck is the key factor to increase the number of sorties and can provide reference and quantization basis for improving the capacity of sortie generation and operation efficiency for carrier-borne aircraft.
Abstract: The operational capability of flight deck is the critical factor to affect the sortie generation of carrier-based aircraft, including launch operation, recovery and respot operations, serving and so on The definition of optimized flight deck operation plan was given A method to calculate the number of sorties generation in optimized flight deck operation plan was proposed Some factors including aircraft number, launch time, respotted time, recovery time how to affect the sortie generation were analyzed Finally, a type example was given The results show that promoting the capability of flight deck is the key factor to increase the number of sorties The results can provide reference and quantization basis for improving the capacity of sortie generation and operation efficiency for carrier-borne aircraft

3 citations

Journal ArticleDOI
TL;DR: In this article, a customized FP-Growth implementation tailored to the requirements of SPADE was proposed, which significantly accelerates pattern mining and result filtering, and the energy consumption was reduced by up to two orders of magnitude.
Abstract: The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85 to 90 % of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

2 citations