A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast classifier extreme learning machine (ELM), has been introduced for diagnosing erythemato-squamous (ES) diseases. In the proposed method, MRMR is employed as a feature selection tool for dimensionality reduction in order to further improve the diagnostic accuracy of the ELM classifier. The impact of the type of activation functions, the number of hidden neurons and the size of the feature subsets on the performance of ELM have been investigated in detail. The effectiveness of the proposed method has been rigorously evaluated against the ES disease dataset, a benchmark dataset, from UCI machine learning database in terms of classification accuracy. Experimental results have demonstrated that our method has achieved the best classification accuracy of 98.89% and an average accuracy of 98.55% via 10-fold cross-validation technique. The proposed method might serve as a new candidate of powerful methods for diagnosing ES diseases.     

A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines.

This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.     

A simulational comparison of intelligent control algorithms on a direct drive manipulator

This paper investigates the control of nonlinear systems by neural networks and fuzzy logic. As the control methods, Gaussian neuro-fuzzy variable structure (GNFVS), feedback error learning architecture (FELA) and direct inverse modeling architecture (DIMA) are studied, and their performances are comparatively evaluated on a two degrees of freedom direct drive robotic manipulator with respect to trajectory tracking performance, computational complexity, design complexity, RMS errors, necessary training time in learning phase and payload variations.     

A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction

Extreme learning machines (ELM), as a learning tool, have gained popularity due to its unique characteristics and performance. However, the generalisation capability of ELM often depends on the nature of the dataset, particularly on whether uncertainty is present in the dataset or not. In order to reduce the effects of uncertainties in ELM prediction and improve its generalisation ability, this paper proposes a hybrid system through a combination of type-2 fuzzy logic systems (type-2 FLS) and ELM; thereafter the hybrid system was applied to model permeability of carbonate reservoir. Type-2 FLS has been chosen to be a precursor to ELM in order to better handle uncertainties existing in datasets beyond the capability of type-1 fuzzy logic systems. The type-2 FLS is used to first handle uncertainties in reservoir data so that its final output is then passed to the ELM for training and then final prediction is done using the unseen testing dataset. Comparative studies have been carried out to compare the performance of the proposed T2-ELM hybrid system with each of the constituent type-2 FLS and ELM, and also artificial neural network (ANN) and support Vector machines (SVM) using five different industrial reservoir data. Empirical results show that the proposed T2-ELM hybrid system outperformed each of type-2 FLS and ELM, as the two constituent models, in all cases, with the improvement made to the ELM performance far higher against that of type-2 FLS that had a closer performance to the hybrid since it is already noted for being able to model uncertainties. The proposed hybrid also outperformed ANN and SVM models considered.     

Learning to Rank with Extreme Learning Machine

Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on. From machine learning point of view, extreme learning machine actually provides a unified framework where the aforementioned algorithms can be considered as special cases. In this paper, pointwise ELM and pairwise ELM are proposed to learn relevance ranking problems for the first time. In particular, ELM type of linear random node is newly proposed together with kernel version of ELM to be linear as well. The famous publicly available dataset collection LETOR is tested to compare ELM-based ranking algorithms with state-of-art linear ranking algorithms.     

Fast detection of impact location using kernel extreme learning machine

Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN.     

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

Pressure–volume–temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson–Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.     

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.     

Self-adaptive extreme learning machine

In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem.     

极限学习机在岩性识别中的应用

基于传统支持向量机(SVM)训练速度慢、参数选择难等问题,提出了基于极限学习机(ELM)的岩性识别.该算法是一种新的单隐层前馈神经网络(SLFNs)学习算法,不但可以简化参数选择过程,而且可以提高网络的训练速度.在确定了最优参数的基础上,建立了ELM的岩性分类模型,并且将ELM的分类结果与SVM进行对比.实验结果表明,ELM以较少的神经元个数获得与SVM相当的分类正确率,并且ELM参数选择比SVM简便,有效降低了训练速度,表明了ELM应用于岩性识别的可行性和算法的有效性.     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

The equivalence between fuzzy logic systems and feedforward neuralnetworks

Demonstrates that fuzzy logic systems and feedforward neural networks are equivalent in essence. First, we introduce the concept of interpolation representations of fuzzy logic systems and several important conclusions. We then define mathematical models for rectangular wave neural networks and nonlinear neural networks. With this definition, we prove that nonlinear neural networks can be represented by rectangular wave neural networks. Based on this result, we prove the equivalence between fuzzy logic systems and feedforward neural networks. This result provides us a very useful guideline when we perform theoretical research and applications on fuzzy logic systems, neural networks, or neuro-fuzzy systems.     

Extreme learning machine for interval neural networks

Interval data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy, or variability must be taken into account. Considered in this paper is the learning of interval neural networks, of which the input and output are vectors with interval components, and the weights are real numbers. The back-propagation (BP) learning algorithm is very slow for interval neural networks, just as for usual real-valued neural networks. Extreme learning machine (ELM) has faster learning speed than the BP algorithm. In this paper, ELM is applied for learning of interval neural networks, resulting in an interval extreme learning machine (IELM). There are two steps in the ELM for usual feedforward neural networks. The first step is to randomly generate the weights connecting the input and the hidden layers, and the second step is to use the Moore–Penrose generalized inversely to determine the weights connecting the hidden and output layers. The first step can be directly applied for interval neural networks. But the second step cannot, due to the involvement of nonlinear constraint conditions for IELM. Instead, we use the same idea as that of the BP algorithm to form a nonlinear optimization problem to determine the weights connecting the hidden and output layers of IELM. Numerical experiments show that IELM is much faster than the usual BP algorithm. And the generalization performance of IELM is much better than that of BP, while the training error of IELM is a little bit worse than that of BP, implying that there might be an over-fitting for BP.     

The extreme learning machine learning algorithm with tunable activation function

In this paper, we propose an extreme learning machine (ELM) with tunable activation function (TAF-ELM) learning algorithm, which determines its activation functions dynamically by means of the differential evolution algorithm based on the input data. The main objective is to overcome the problem dependence of fixed slop of the activation function in ELM. We mainly considered the issue of processing of benchmark problems on function approximation and pattern classification. Compared with ELM and E-ELM learning algorithms with the same network size or compact network configuration, the proposed algorithm has improved generalization performance with good accuracy. In addition, the proposed algorithm also has very good performance in the TAF neural networks learning algorithms.     

Efficient and effective algorithms for training single-hidden-layer neural networks

Recently there have been renewed interests in single-hidden-layer neural networks (SHLNNs). This is due to its powerful modeling ability as well as the existence of some efficient learning algorithms. A prominent example of such algorithms is extreme learning machine (ELM), which assigns random values to the lower-layer weights. While ELM can be trained efficiently, it requires many more hidden units than is typically needed by the conventional neural networks to achieve matched classification accuracy. The use of a large number of hidden units translates to significantly increased test time, which is more valuable than training time in practice. In this paper, we propose a series of new efficient learning algorithms for SHLNNs. Our algorithms exploit both the structure of SHLNNs and the gradient information over all training epochs, and update the weights in the direction along which the overall square error is reduced the most. Experiments on the MNIST handwritten digit recognition task and the MAGIC gamma telescope dataset show that the algorithms proposed in this paper obtain significantly better classification accuracy than ELM when the same number of hidden units is used. For obtaining the same classification accuracy, our best algorithm requires only 1/16 of the model size and thus approximately 1/16 of test time compared with ELM. This huge advantage is gained at the expense of 5 times or less the training cost incurred by the ELM training.     

Dynamic ensemble extreme learning machine based on sample entropy

Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitting, especially on large datasets. In this paper, a dynamic ensemble extreme learning machine based on sample entropy is proposed, which can alleviate to some extent the problems of instability and over-fitting, and increase the prediction accuracy. The experimental results show that the proposed approach is robust and efficient.     

基于局部保留投影的堆叠隐空间模糊C均值算法*

传统模糊聚类算法在处理复杂非线性数据时学习能力较差。针对此问题,文中基于极限学习机(ELM)理论,结合局部保留投影(LPP)与ELM特征映射,提出压缩隐空间特征映射算法,从而将原始数据从原空间映射至压缩ELM隐空间中。通过连接多个压缩隐空间特征映射,结合模糊聚类技术,提出基于LPP的堆叠隐空间模糊C均值算法。大量实验表明,文中算法对模糊指数的变化不敏感,在处理复杂非线性数据和存在类内差异的图像数据时,能够取得更精确、高效、稳定的学习效果。     

On some idea of a neuro-fuzzy controller

The paper presents a neuro-fuzzy technique for the design of controllers. This technique can effectively deal with two main types of knowledge which usually describe the control strategy for complex systems, that is, a qualitative, linguistic, fuzzy knowledge usually expressed in the form of linguistic rules, and a quantitative, nonfuzzy information in the form of measurements and other numerical data. The proposed technique combines artificial neural networks with fuzzy logic yielding a structure that can be called a neuro-fuzzy controller or, broadly speaking, a fuzzy neural network. The paper presents a general structure of a neuro-fuzzy controller and two essential phases of its design, that is, a learning phase and a functioning phase. In turn, a numerical example which illustrates how the proposed controller works is presented. Finally, the paper describes an application of a neuro-fuzzy control to inverter drive systems for electric vehicles. The results of simulation and experimental investigations carried out on the laboratory model of an inverter drive system are also provided.     

Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry

Due to the strong competition that exists today, most retailers are in a continuous effort for increasing profits and reducing their cost. An accurate sales forecasting system is an efficient way to achieve the aforementioned goals and lead to improve the customers’ satisfaction, reduce destruction of products, increase sales revenue and make production plan efficiently. In this study, the Gray extreme learning machine (GELM) integrates Gray relation analysis and extreme learning machine with Taguchi method to support purchasing decisions. GRA can sieve out the more influential factors from raw data and transforms them as the input data in a novel neural network such as ELM. The proposed system evaluated the real sales data in the retail industry. The experimental results demonstrate that our proposed system outperform several sales forecasting methods which are based on back-propagation neural networks such as BPN and MFLN models.