Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.

Extreme learning machines: a survey

Turbo equalization is an iterative equalization and decoding technique that can achieve equally impressive performance gains for communication systems that send digital data over channels that require equalization, i.e., those that suffer from intersymbol interference (ISI). In this article, we discuss the turbo equalization approach to coded data transmission over ISI channels, with emphasis on the basic ideas and some of the practical details. The original system introduced by Douillard et al. can be viewed as an extension of the turbo decoding algorithm by considering the effect of the ISI channel as another form of error protection, i.e., as a rate-1 convolutional code.

Turbo equalization

Letters: Fully complex extreme learning machine

https://www3.ntu.edu.sg/home/egbhuang/pdf/I-ELM-Complex.pdf

Incremental extreme learning machine with fully complex hidden nodes

Discusses the application of neural networks to general and radial basis functions and in particular to adaptive equalization and interference rejection problems. Neural-network-based algorithms strike a good balance between performance and complexity in adaptive equalization, and show promise in spread spectrum systems.

Applying radial basis functions

It is generally recognized that digital channel equalization can be interpreted as a problem of nonlinear classification. Networks capable of approximating nonlinear mappings can be quite useful in such applications. The radial basis function network (RBFN) is one such network. We consider an extension of the RBFN for complex-valued signals (the complex RBFN or CRBFN). We also propose a stochastic-gradient (SG) training algorithm that adapts all free parameters of the network. We then consider the problem of equalization of complex nonlinear channels using the CRBFN as part of an equalizer. Results of simulations we have carried out show that the CRBFN with the SG algorithm can be quite effective in channel equalization. >

Channel equalization using adaptive complex radial basis function networks

Interference cancellation using radial basis function networks

We investigate a technique for image restoration using nonlinear networks based on radial basis functions. The technique is also based on the concept of training or learning by examples. When trained properly, these networks are used as spatially invariant feedforward nonlinear filters that can perform restoration of images degraded by nonlinear degradation mechanisms. We examine a number of network structures including the Gaussian radial basis function network (RBFN) and some extensions of it, as well as a number of training algorithms including the stochastic gradient (SG) algorithm that we have proposed earlier. We also propose a modified structure based on the Gaussian-mixture model and a learning algorithm for the modified network. Experimental results indicate that the radial basis function network and its extensions can be very useful in restoring images degraded by nonlinear distortion and noise.

RBFN restoration of nonlinearly degraded images

We consider radial basis function networks (RBFN) for use in various nonlinear signal processing applications. We first present a simple training algorithm for the RBFN based on stochastic gradients of error. We then demonstrate and discuss the usefulness of RBFNs in various applications including channel equalization, interference cancellation, time-series prediction, and nonlinear filtering. >

Radial basis function networks in nonlinear signal processing applications

The radial basis function (RBF) network is investigated for its applicability in adaptive interference cancellation problems. Simulations show that the RBF network can be quite useful in this application. An extended structure that combines a linear canceller with an RBF network is shown to be more robust than the structure using an RBF network only. >

http://ieeexplore.ieee.org/iel2/621/6325/00246809.pdf

Inhyok Cha

Papers

Channel equalization using adaptive complex radial basis function networks

Interference cancellation using radial basis function networks

RBFN restoration of nonlinearly degraded images

Radial basis function networks in nonlinear signal processing applications

Interference cancellation using radial basis function networks