Objective rating of fabric wrinkles via random vector functional link based on the improved salp swarm algorithm

2021 ◽  
pp. 004051752110257
Author(s):  
Zhiyu Zhou ◽  
Zijian Ma ◽  
Zefei Zhu ◽  
Yaming Wang
Keyword(s):  
Extreme Learning Machine ◽  
Random Vector ◽  
Classification Accuracy ◽  
Functional Link ◽  
Salp Swarm Algorithm ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Swarm Algorithm ◽  
Learning Machine

To solve the problem of inefficiency and inaccuracy associated with the classification of fabric wrinkles by human eyes, as well as improve current deficiencies in the application of neural networks for the classification of fabric wrinkles, we propose a model based on the salp swarm algorithm improved by ant lion optimization to optimize the random vector functional link to objectively evaluate the fabric wrinkle level. First, to improve the global searchability of the salp swarm algorithm and avoid the local optima problem, the use of ant lion optimization to improve the salp swarm algorithm is proposed in this study. Afterward, the improved salp swarm algorithm is used to optimize the input weight and hidden layer bias of the random vector functional link to avoid the inaccuracy and instability of random vector functional link classification owing to the randomness of the parameters. Finally, the performance of the proposed algorithm is verified using a fabric wrinkle dataset. Comparative experiments show that the classification accuracy of the proposed ant lion optimization - salp swarm algorithm - random vector functional link algorithm were 8.46%, 2.05%, 10.28%, 3.50%, and 4.42% higher than those of random vector functional link, improved random vector functional link based on salp swarm algorithm, extreme learning machine, improved extreme learning machine based on whale optimization algorithm, and improved backpropagation based on the Levenberg-Marquardt algorithm. Furthermore, the classification accuracy of the wrinkle level was effectively improved. All the fabrics used in this study were monochromatic, and multi-color printed fabrics have a significant impact on the difficulty of image processing and classification results. The next research step is to evaluate the wrinkle level of multi-color printed fabrics.

Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

2020 ◽  
Vol 62 (1) ◽  
pp. 15-21
Author(s):  
Changdong Wu
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Classification Accuracy ◽  
Activation Function ◽  
Machine Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

Mesh Segmentation Based on Optimizing Extreme Learning Machine with Ant Lion Optimization

2020 ◽  
Vol 57 (4) ◽  
pp. 041014
Author(s):  
杨晓文 Yang Xiaowen ◽  
尹洪红 Yin Honghong ◽  
韩燮 Han Xie ◽  
刘佳鸣 Liu Jiaming
Keyword(s):  
Extreme Learning Machine ◽  
Mesh Segmentation ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Learning Machine

scholarly journals Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1651
Author(s):  
Wenming He ◽  
Yanqing Xie ◽  
Haoxuan Lu ◽  
Mingjing Wang ◽  
Huiling Chen
Keyword(s):  
Heart Disease ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Auxiliary Function ◽  
Classification Model ◽  
Diagnostic Model ◽  
Transformation Mechanism ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Learning Machine

To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.

scholarly journals Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Zaher Mundher Yaseen ◽  
Hossam Faris ◽  
Nadhir Al-Ansari
Keyword(s):  
Extreme Learning Machine ◽  
River Flow ◽  
Forecast Accuracy ◽  
Machine Model ◽  
Salp Swarm Algorithm ◽  
Tigris River ◽  
Statistical Measures ◽  
Swarm Algorithm ◽  
Learning Machine ◽  
Intelligent Model

The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

scholarly journals A Tensor Based Stacked Fuzzy Networks for Efficient Data Regression

2021 ◽  
Author(s):  
Jie Li ◽  
Jiale Hu ◽  
Guoliang Zhao ◽  
Sharina Huang ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Fuzzy Sets ◽  
Extreme Learning Machine ◽  
Random Vector ◽  
Fuzzy Neural Network ◽  
Higher Order ◽  
Functional Link ◽  
Fuzzy Neural ◽  
Learning Machine

Abstract Random vector functional link and extreme learning machine have been extended by the type-2 fuzzy sets with vector stacked methods, this extension leads to a new way to use tensor to construct learning structure for the type-2 fuzzy sets-based learning framework. In this paper, type-2 fuzzy sets-based random vector functional link, type-2 fuzzy sets-based extreme learning machine and Tikhonov-regularized extreme learning machine are fused into one network, a tensor way of stacking data is used to incorporate the nonlinear mappings when using type-2 fuzzy sets. In this way, the network could learning the sub-structure by three sub-structures' algorithms, which are merged into one tensor structure via the type-2 fuzzy mapping results. To the stacked single fuzzy neural network, the consequent part parameters learning are implemented by unfolding tensor-based matrix regression. The newly proposed stacked single fuzzy neural network shows a new way to design the hybrid fuzzy neural network with the higher order fuzzy sets and higher order data structure. The effective of the proposed stacked single fuzzy neural network are verified by the classical testing benchmarks and several statistical testing methods.

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine
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