Study on GA-based Training Algorithm for Extreme Learning Machine

Song, Shaojian; Wang, Yao; Lin, Xin; Huang, Qingbao

doi:10.1109/ihmsc.2015.156

Cited by 8 publications

(10 citation statements)

References 14 publications

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“…These are expressed mathematically in Eqs. (32) to (34). Afterward, the Bayesian Information Criterion (BIC) [57] (Eq.…”

Section: Model Selection and Performance Indicatorsmentioning

confidence: 99%

“…Using various performance indicators as outlined in Eqs. (32) to (34) on the test datasets, the predictive abilities of various developed models were assessed. The obtained assessment test results are presented in Table 9.…”

Section: Assessment Of Model Performancementioning

confidence: 99%

“…In that regard, Huang et al [33] proposed the kernel-based ELM (KELM) which adds a positive regularisation coefficient in the computation of the output weight to enhance stability and a kernel matrix when the hidden layer feature mapping is unknown. Additionally, other researchers tend to rely on the search abilities of metaheuristic optimisation algorithms notably particle swarm optimisation (PSO) and genetic algorithm (GA) to select the optimal input weights and hidden neurons of the ELM approach [34][35][36][37]. With regard to blasting studies, authors such as Armaghani et al [38] developed a novel hybrid ELM model optimised by autonomous groups particle swarm optimisation (AGPSO) algorithm for blastinduced ground vibration prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

2020

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Blast-induced ground vibration is still an adverse impact of blasting in civil and mining engineering projects that need much consideration and attention. This study proposes the use of Self-Adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) for the prediction of ground vibration due to blasting using 210 blasting data points from an open pit mine in Ghana. To ascertain the predictive performance of the proposed SaDE-ELM approach, several artificial intelligence and empirical approaches were developed for comparative purposes. The performances of various developed models were assessed using model performance indicators of mean squared error (MSE), Nash-Sutcliffe Efficiency Index (NSEI) and correlation coefficient (R). Furthermore, the Bayesian Information Criterion (BIC) was applied to select the best performing approach. The obtained prediction results based on the performance indicators showed that the SaDE-ELM outperformed all the competing models as it had the lowest MSE value of 0.01942, respectively. The SaDE-ELM also achieved the highest R and NSEI values of 0.8711 and 0.7537, respectively. The other artificial intelligent approaches had MSE, R and NSEI in the ranges of (0.02166-0.03006), (0.8012-0.8537) and (0.6188-0.7254), respectively. The empirical approaches performed poorly relative to the artificial intelligence approaches by having had MSE, R and NSEI in the ranges of (0.03419-0.06587), (0.7466-0.7833) and (0.1649-0.5665), respectively. The prediction superiority of SaDE-ELM was confirmed when it is achieved the lowest BIC value of − 293.40. Therefore, the proposed SaDE-ELM has demonstrated great potential to be used for on-site prediction, control and management of blast-induced ground vibration to prevent unwanted effects on the environment.

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“…These are expressed mathematically in Eqs. (32) to (34). Afterward, the Bayesian Information Criterion (BIC) [57] (Eq.…”

Section: Model Selection and Performance Indicatorsmentioning

confidence: 99%

Section: Assessment Of Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

2020

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“…However, random initialization of weights among input and hidden layer does not guarantee that one obtains the best ELM configuration from all possible options [24]. Hence, researchers and practitioners employ biology-inspired algorithms in order to fine-tune ELM models for the specific tasks [24][25][26]. Biology-inspired algorithms are heuristic optimization techniques that are inspired by nature and process a variety of solutions in every iteration.…”

Section: Introductionmentioning

confidence: 99%

Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

Demidova

Gorchakov

2022

Algorithms

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The massive nature of modern university programming courses increases the burden on academic workers. The Digital Teaching Assistant (DTA) system addresses this issue by automating unique programming exercise generation and checking, and provides means for analyzing programs received from students by the end of semester. In this paper, we propose a machine learning-based approach to the classification of student programs represented as Markov chains. The proposed approach enables real-time student submissions analysis in the DTA system. We compare the performance of different multi-class classification algorithms, such as support vector machine (SVM), the k nearest neighbors (KNN) algorithm, random forest (RF), and extreme learning machine (ELM). ELM is a single-hidden layer feedforward network (SLFN) learning scheme that drastically speeds up the SLFN training process. This is achieved by randomly initializing weights of connections among input and hidden neurons, and explicitly computing weights of connections among hidden and output neurons. The experimental results show that ELM is the most computationally efficient algorithm among the considered ones. In addition, we apply biology-inspired algorithms to ELM input weights fine-tuning in order to further improve the generalization capabilities of this algorithm. The obtained results show that ELMs fine-tuned with biology-inspired algorithms achieve the best accuracy on test data in most of the considered problems.

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“…The ELM can be divided into the fixed ELM and the incremental CONTACT Yuzhang Lin yuzhang_lin@uml.edu extreme learning machine (I-ELM) (Huang, Chen, et al, 2006). The training process of the fixed ELM is one-shot computation with the fast learning speed (Song et al, 2015). Nevertheless, how to choose the best number of hidden layer neurons and the optimal weights and bias in the fixed ELM is a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

An improved algorithm for incremental extreme learning machine

Song

Miao

Lin

2020

Systems Science & Control Engineering

Self Cite

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Incremental extreme learning machine (I-ELM) randomly obtains the input weights and the hidden layer neuron bias during the training process. Some hidden nodes in the ELM play a minor role in the network outputs which may eventually increase the network complexity and even reduce the stability of the network. In order to avoid this issue, this paper proposed an enhanced method for the I-ELM which is referred to as the improved incremental extreme learning machine (II-ELM). At each learning step of original I-ELM, an additional offset k will be added to the hidden layer output matrix before computing the output weights for the new hidden node and analysed the existence of the offset k. Compared with several improved algorithms of ELM, the advantages of the II-ELM in the training time, the forecasting accuracy, and the stability are verified on several benchmark datasets in the UCI database.

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Study on GA-based Training Algorithm for Extreme Learning Machine

Cited by 8 publications

References 14 publications

A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

An improved algorithm for incremental extreme learning machine

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