Generalized neuron: Feedforward and recurrent architectures
Neural Networks
Nonlinear time series online prediction using reservoir Kalman filter
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Echo state networks with sparse output connections
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Recurrent kernel machines: Computing with infinite echo state networks
Neural Computation
Multi-reservoir echo state network with sparse bayesian learning
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Data-driven based model for flow prediction of steam system in steel industry
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Model predictive engine air-ratio control using online sequential relevance vector machine
Journal of Control Science and Engineering - Special issue on Advanced Control in Micro-/Nanosystems
Subspace echo state network for multivariate time series prediction
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Gesture unit segmentation using support vector machines: segmenting gestures from rest positions
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Modular state space of echo state network
Neurocomputing
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A novel chaotic time-series prediction method based on support vector machines (SVMs) and echo-state mechanisms is proposed. The basic idea is replacing "kernel trick" with "reservoir trick" in dealing with nonlinearity, that is, performing linear support vector regression (SVR) in the high-dimension "reservoir" state space, and the solution benefits from the advantages from structural risk minimization principle, and we call it support vector echo-state machines (SVESMs). SVESMs belong to a special kind of recurrent neural networks (RNNs) with convex objective function, and their solution is global, optimal, and unique. SVESMs are especially efficient in dealing with real life nonlinear time series, and its generalization ability and robustness are obtained by regularization operator and robust loss function. The method is tested on the benchmark prediction problem of Mackey-Glass time series and applied to some real life time series such as monthly sunspots time series and runoff time series of the Yellow River, and the prediction results are promising