Open AccessJournal Article
Complex Valued Radial Basis Function Network: Network Architecture and Learning Algorithms (part I)
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About: This article is published in EURASIP Journal on Advances in Signal Processing. The article was published on 01 Jan 1994. and is currently open access. The article focuses on the topics: Radial basis function network & Network architecture.
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Citations
Topology Identification and Module–Phase Synchronization of Neural Network With Time Delay
TL;DR: In module–phase synchronization, complex-valued node states are taken into consideration and the topology weights considered here are uncertain and the time delays are bounded.
97
Volterra series truncation and kernel estimation of nonlinear systems in the frequency domain
B. Zhang,Stephen A. Billings +1 more
TL;DR: In this paper, a complex-valued orthogonal least squares algorithm is developed to estimate the Volterra series kernels of a weakly nonlinear system and the physical parameters of the system.
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Adaptive beamforming using complex-valued Radial Basis Function neural networks
R. Savitha,Saravanamuthu Vigneswaran,Sundaram Suresh,Narasimhan Sundararajan +3 more
- 01 Nov 2009
TL;DR: It was observed that the FC-RBF network performed better than the other complex-valued RBF networks in suppressing the nulls and steering beams, as desired, and the learning speed of the network was also faster than the Complex-valued Radial Basis Function network.
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A self-regulated learning in Fully Complex-valued Radial Basis Function Networks
R. Savitha,Sundaram Suresh,Narasimhan Sundararajan +2 more
- 18 Jul 2010
TL;DR: This paper presents a self-regulatory system that selects samples for learning in each epoch of the batch learning scheme, which improves the generalization performance of the FC-RBF network with a lesser computational effort.
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Exact Interpolation and Learning in Quadratic Neural Networks
George M. Georgiou
- 30 Oct 2006
TL;DR: A quadratic matrix mapping scheme is presented where exact interpolation for a set of input vectors is achieved and analogies are drawn with radial-basis function (RBF) neural networks.
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