In: Proceedings of the 2001 congress on evolutionary computation, pp 81–86 Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: 2018 13th IEEE conference on industrial electronics and applications (ICIEA), pp 1599–1603 Zhu Z, Zhang Z, Man W, Tong X, Qiu J, Li F (2018) A new beetle antennae search algorithm for multi-objective energy management in microgrid. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems.
Huang GB, Chen L (2007) Convex incremental extreme learning machine. Wang G, Zhao Y, Wang D (2008) A protein secondary structure prediction framework based on the extreme learning machine. Kim J, Shin HS, Shin K, Lee M (2009) Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Song Y, Crowcroft J, Zhang J (2012) Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Multimodal User Interfaces 10(2):139–149
Kaya H, Salah AA (2016) Combining modality-specific extreme learning machines for emotion recognition in the wild. Lan Y, Hu Z, Soh YC, Huang GB (2013) An extreme learning machine approach for speaker recognition. Muthusamy H, Polat K (2015) Yaacob S (2015) Improved emotion recognition using gaussian mixture model and extreme learning machine in speech and glottal signals. Marques I, Graña M (2012) Face recognition with lattice independent component analysis and extreme learning machines. Neural Netw 81:91–102Ĭao F, Liu B, Park DS (2013) Image classification based on effective extreme learning machine. Neural Comput Appl 22(3–4):501–508Ĭao J, Zhang K, Luo M, Yin C, Lai X (2016) Extreme learning machine and adaptive sparse representation for image classification. Xu Y, Dai Y, Dong ZY, Zhang R, Meng K (2013) Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems. In: Chinese automation congress (CAC), pp 5740–5744 Zhang L, Hu X, Li P, Shi F, Yu Z (2017) ELM model for power system transient stability assessment.
#Scan master elm too big to fit into memory generator#
Wong PK, Yang Z, Vong CM, Zhong J (2014) Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Experiment results show that the proposed method is capable of simultaneously reducing the condition number and regression error, and achieving good generalization performance. Then, the proposed MBAS is applied for optimizing the input weights and biases of ELM to solve its ill-conditioned problems. Aiming to optimize the conditioning of ELM, we propose an effective particle swarm heuristic algorithm called Multitask Beetle Antennae Swarm Algorithm (MBAS), which is inspired by the structures of artificial bee colony (ABC) algorithm and Beetle Antennae Search (BAS) algorithm. However, the strategy of selecting input weights and biases at random may result in ill-conditioned problems. Different from the general single hidden layer feedforward neural network (SLFN), the input weights and biases in hidden layer of ELM are generated randomly, so that it only takes a little computational overhead to train the model. Extreme learning machine (ELM) as a simple and rapid neural network has been shown its good performance in various areas.