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.
Highlights
Radar target classification technology is of great significance in both military and civil aspects [1, 2]
This paper proposed improved quantum-behaved particle swarm optimization (IQPSO)-extreme learning machine (ELM) algorithm to solve the shortcoming of ELM
We proposed a novel evolutionary extreme learning machine based on improved quantum-behaved particle swarm optimization
Summary
Radar target classification technology is of great significance in both military and civil aspects [1, 2]. In order to solve this problem, an improved ELM method based on particle swarm optimization (PSO) is proposed in [23]. This method can resolve the drawbacks of ELM. QPSO algorithm has better global convergence than PSO, this method calculates the characteristic length of Delta potential well only according to the mean best position, which will reduce the global search ability of the algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have