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Orthogonal least squares learning algorithm for radial basis function networks
The authors propose an alternative learning procedure based on the orthogonal least-squares method, which provides a simple and efficient means for fitting radial basis function networks.
An LMS style variable tap-length algorithm for structure adaptation
This paper proposes an improved variable tap-length algorithm using the concept of the pseudo fractionaltap-length (FT), which not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms.
Practical identification of NARMAX models using radial basis functions
A wide class of discrete-time non-linear systems can be represented by the nonlinear autoregressive moving average (NARMAX) model with exogenous inputs. This paper develops a practical algorithm for…
A low-complexity soft-MIMO detector based on the fixed-complexity sphere decoder
- L. G. Barbero, T. Ratnarajah, C. Cowan
- Computer Science, BusinessIEEE International Conference on Acoustics…
- 12 May 2008
Simulation results show that the soft-FSD can be used to approximate the performance of the LSD while having a considerably lower and fixed complexity, making the algorithm suitable for hardware implementation.
High Speed FPGA-Based Implementations of Delayed-LMS Filters
The method has been used to derive a series of retimed delayed LMS (RDLMS) architectures, which allow a 66.7% reduction in delays and 5 times faster convergence time thereby giving superior performance in terms of throughput rate when compared to previous work.
Virtex FPGA implementation of a pipelined adaptive LMS predictor for electronic support measures receivers
- L. Ting, Roger Francis Woods, C. Cowan
- Computer ScienceIEEE Transactions on Very Large Scale Integration…
- 1 January 2005
High-speed field-programmable gate array (FPGA) implementations of an adaptive least mean square (LMS) filter with application in an electronic support measures (ESM) digital receiver, are presented.…
Parallel recursive prediction error algorithm for training layered neural networks
A new recursive prediction error algorithm is derived for the training of feedforward layered neural networks. The algorithm enables the weights in each neuron of the network to be updated in an…
Adaptive Filters and Equalisers
1 Introduction.- 1.1 Adaptive Signal Processing.- 1.2 The Adaptive Filter.- 1.3 Modes of Operation.- 1.4 Application of Adaptive Filters.- 1.5 Summary.- 2 Adaptive Fir Filter Algorithms.- 2.1…
Adaptive equalization of finite nonlinear channels using multilayer perceptron
Multilayer perceptron structures applied to adaptive equalisers for data communications
Its ability to form decision regions with nonlinear boundaries enables it to equalize both minimum-phase and non-minimum-phase channels without the introduction of any timing delay, a capability that may be of value in nonstationary environments.