An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Approximation by fully complex multilayer perceptrons
Neural Computation
A Complex-Valued RTRL Algorithm for Recurrent Neural Networks
Neural Computation
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
A non-parametric test for detecting the complex-valued nature of time series
International Journal of Knowledge-based and Intelligent Engineering Systems - Advanced Intelligent Techniques in Engineering Applications
Data fusion for modern engineering applications: an overview
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Nonlinear adaptive prediction of complex-valued signals by complex-valued PRNN
IEEE Transactions on Signal Processing
Nonlinear adaptive prediction of nonstationary signals
IEEE Transactions on Signal Processing
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$ . The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted.