An Improved BP Neural Network and Its Application

Mou, Rui; Chen, Qinyin; Huang, Minying

doi:10.1109/iccis.2012.68

Cited by 5 publications

(2 citation statements)

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“…ELM [ 26 ] is a new learning algorithm for single hidden layer feedforward neural networks (SLFNs). Its network topology is the same as that of back propagation (BP) [ 27 ] neural networks. It is also composed of an input layer, a hidden layer, and an output layer.…”

Section: Introductionmentioning

confidence: 99%

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Zhao

Liu

Huang

et al. 2018

Sensors

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A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

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Section: Introductionmentioning

confidence: 99%

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Zhao

Liu

Huang

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…At present, the commonly used classification methods include Bayesian [3], Dempster-Shafer (D-S) theory [4], decision tree [5], support vector machine (SVM) [6], and back propagation (BP) neural network [7]. Although these methods can obtain good classification accuracy, their common problem is that the real-time performance is not high.…”

Section: Introductionmentioning

confidence: 99%

Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum‐Behaved Particle Swarm Optimization

Zhao¹,

Liu²,

Huang³

et al. 2017

Mathematical Problems in Engineering

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A novel evolutionary extreme learning machine (ELM) based on improved quantum-behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper. Quantum-behaved particle swarm optimization (QPSO) has been used in ELM to solve the problem that ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. But the method for calculating the characteristic length of Delta potential well of QPSO may reduce the global search ability of the algorithm. To solve this issue, a new method to calculate the characteristic length of Delta potential well is proposed in this paper. Experimental results based on the benchmark functions validate the better performance of IQPSO against QPSO in most cases. The novel algorithm is also evaluated by using real-world datasets and radar data; the experimental results indicate that the proposed algorithm is more effective than BP, SVM, ELM, QPSO-ELM, and so on, in terms of real-time performance and accuracy.

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Research on Intelligent Navigation Algorithm of Long and Short Term Memory Network Based on Firework Algorithm Optimization in Satellite Blocking Environment

Rui,

Wang,

Zhao

et al. 2023

Lecture Notes in Electrical Engineering

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An Improved BP Neural Network and Its Application

Cited by 5 publications

References 1 publication

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum‐Behaved Particle Swarm Optimization

Research on Intelligent Navigation Algorithm of Long and Short Term Memory Network Based on Firework Algorithm Optimization in Satellite Blocking Environment

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