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

Letters: Convex incremental extreme learning machine

The emergent machine learning technique—extreme learning machines (ELMs)—has become a hot area of research over the past years, which is attributed to the growing research activities and significant contributions made by numerous researchers around the world. Recently, it has come to our attention that a number of misplaced notions and misunderstandings are being dissipated on the relationships between ELM and some earlier works. This paper wishes to clarify that (1) ELM theories manage to address the open problem which has puzzled the neural networks, machine learning and neuroscience communities for 60 years: whether hidden nodes/neurons need to be tuned in learning, and proved that in contrast to the common knowledge and conventional neural network learning tenets, hidden nodes/neurons do not need to be iteratively tuned in wide types of neural networks and learning models (Fourier series, biological learning, etc.). Unlike ELM theories, none of those earlier works provides theoretical foundations on feedforward neural networks with random hidden nodes; (2) ELM is proposed for both generalized single-hidden-layer feedforward network and multi-hidden-layer feedforward networks (including biological neural networks); (3) homogeneous architecture-based ELM is proposed for feature learning, clustering, regression and (binary/multi-class) classification. (4) Compared to ELM, SVM and LS-SVM tend to provide suboptimal solutions, and SVM and LS-SVM do not consider feature representations in hidden layers of multi-hidden-layer feedforward networks either.

What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle

Adaptive methods for performing multiuser demodulation in a direct-sequence spread-spectrum multiple-access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This prohibitive complexity has spawned the area of research on suboptimal receivers with moderate complexity. Adaptive algorithms for detection allow for reception when the communication environment is either unknown or changing. Motivated by previous work with radial basis functions (RBF's) for performing equalization, RBF networks that operate with knowledge of only a subset of the system parameters are studied. Although this form of detection has been previously studied (group detection) when the system parameters are known, in this work, neural network techniques are employed to adaptively determine unknown system parameters. This approach is further bolstered by the fact that the optimal detector in the synchronous case can be implemented by a RBF network when all of the system parameters are known. The RBF network's performance (with estimated parameters) is compared with the optimal synchronous detector, the decorrelating detector and the single layer perceptron detector. Clustering techniques and adaptive least mean squares methods are investigated to determine the unknown system parameters. This work shows that the adaptive radial basis function network attains near optimal performance and is robust in realistic communication environments. >

Neural network techniques for adaptive multiuser demodulation

The key point in design of radial basis function networks is to specify the number and the locations of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear least-squares method for the linear weights, have been previously suggested. These hybrid methods can be put into two groups, which will be called as input clustering (IC) and input-output clustering (IOC), depending on whether the output vector is also involved in the clustering process. The idea of concatenating the output vector to the input vector in the clustering process has independently been proposed by several papers in the literature although none of them presented a theoretical analysis on such procedures, but rather demonstrated their effectiveness in several applications. The main contribution of this paper is to present an approach for investigating the relationship between clustering process on input-output training samples and the mean squared output error in the context of a radial basis function network (RBFN). We may summarize our investigations in that matter as follows: (1) A weighted mean squared input-output quantization error, which is to be minimized by IOC, yields an upper bound to the mean squared output error. (2) This upper bound and consequently the output error can be made arbitrarily small (zero in the limit case) by decreasing the quantization error which can be accomplished through increasing the number of hidden units.

Analysis of input-output clustering for determining centers of RBFN

The author and D.S. Broomhead developed (1988) the opinion that most current feedforward layered neural networks perform a curve fitting operation in a high-dimensional space. To create the analogy, it was necessary to generalise earlier papers' assumptions, and so a mechanism for choosing radial basis functions was needed. The method involves optimisation. It is concluded that nonlinear optimisation of the first layer parameters is beneficial only when a minimal network is required to solve a given problem, since the same generalisation performance can be achieved simply by using more centres and adapting only the final layer by linear optimisation. The processing time is many orders of magnitude longer when full adaptation was used. Nonlinear optimisation cannot be used to improve the generalisation performance of the network. Choice of the nonlinearity is not crucial. >

Adaptive radial basis function nonlinearities, and the problem of generalisation

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Adaptive radial basis function nonlinearities, and the problem of generalisation