Authors: Shan Pang; Xinyi Yang
Addresses: College of Information and Electrical Engineering, Ludong University, Yantai, 264025, China ' Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai, 264001, China
Abstract: Extreme learning machine (ELM) is a novel learning algorithm where the training is restricted to the output weights to achieve a fast learning speed. However, ELM tends to require more neurons in the hidden layer and sometimes leads to ill-condition problem due to random selection of input weights and hidden biases. To address these problems, we propose a multi hidden layer extreme learning machine optimised with batch intrinsic plasticity (BIP) scheme. The proposed algorithm has a deep structure and thus learns features more efficiently. The combination of BIP scheme helps to achieve better generalisation ability. Comparisons with some state-of-the-art ELM algorithms on both regression and classification problems have verified the performance and effectiveness of our proposed algorithm.
Keywords: neural network; extreme learning machine; ELM; batch intrinsic plasticity; BIP; multi hidden layers.
International Journal of Computational Science and Engineering, 2019 Vol.18 No.4, pp.375 - 382
Received: 10 May 2016
Accepted: 12 Sep 2016
Published online: 08 Apr 2019 *