A resource-allocating network for function interpolation
Neural Computation
Universal approximation using radial-basis-function networks
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fast learning in networks of locally-tuned processing units
Neural Computation
Fire detection model in Tibet based on grey-fuzzy neural network algorithm
Expert Systems with Applications: An International Journal
Applying fuzzy grey modification model on inflow forecasting
Engineering Applications of Artificial Intelligence
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers and Electronics in Agriculture
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Dynamic ensemble extreme learning machine based on sample entropy
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
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For the online prediction of nonlinear systems with characteristics of time-varying dynamics and uncertainty, a sequential grey prediction approach is proposed based on the online sequential extreme learning machine (OS-ELM). The grey processing of time series alleviates the unfavorable effects of uncertainty in measurement data; the extremely fast learning speed and high generalization accuracy of OS-ELM enable online application of the sequential grey prediction approach. Ship's roll motion at sea is a complex nonlinear process with time-varying dynamics. Its dynamics also involves uncertainty caused by wind, random waves and rudder actions. In this paper, the proposed OS-ELM-based grey prediction approach is implemented for online ship roll prediction. The simulation of prediction is based on measurement data obtained from sea trials of the scientific research and training ship Yu Kun. Simulation results of ship roll prediction demonstrate the effectiveness and efficiency of the proposed grey neural prediction approach in dealing with time-varying nonlinear system with uncertainty.