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

Abstract

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.

An Enriched Prediction Intervals Construction Method with Hybrid Intelligent Optimization

“…Discussions on Different Objective Functions. The effectiveness of ELM has already been demonstrated to have faster learning speed and better generalization ability than traditional NNs in [20][21][22]. In this section, the effectiveness of proposed PIs objective function in 7is demonstrated by comparing it with CWC, constrained CWC (CCWC), and the interval score-based criterion (ISC).…”

“…As a new learning algorithm for training traditional feed forward neural networks, Extreme Learning Machine (ELM) [17] has been applied to construct PIs for its iterative-free learning mechanism [18,19]. The performance of ELM has also been shown to have faster learning speed and better generalization ability than traditional NNs in [20][21][22]. In these ELM-based prediction methods, PIs with associated confidence levels are generated through minimizing the PIs evaluation functions and optimizing the parameters in the ELM to produce high quality PIs.…”

An Improved Multi-Objective Quantum-Behaved Particle Swarm Optimization for Railway Freight Transportation Routing Design

“…Singh and Mahapatra [38] introduced the operator in genetic algorithm in QPSO and used the logistic mapping to generate chaotic numbers to solve the flexible job shop scheduling problem. Zhao et al [39] Presented a novel evolutionary extreme learning machine based on improved quantum-behaved particle swarm optimization for radar target classification. Wang et al [40] applied the QPSO algorithm in the hybrid energy storage system capacity optimization.…”

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

“…To solve this problem, a regularized ELM based on target class information is proposed in this paper. Besides, due to the random selection of input weights and hidden biases, the ELM tends to need more hidden nodes to achieve better generalization performance [ 29 , 39 , 42 ], which makes the network structure complex. In this paper, SAE is used to optimize the input weights and hidden biases of ELM, which then achieves better results with fewer hidden layer nodes.…”