An improved evolutionary extreme learning machine based on particle swarm optimization

Han, Fei; Yao, Hai-Fen; Ling, Qing-Hua

doi:10.1016/j.neucom.2011.12.062

Cited by 212 publications

(99 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computation of the weight vector of the ELM given in Eq. (28) is done using all the data samples at a time.…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

“…Further to get faster convergence speed and improved classification accuracy, we propose a modified TLBO-based LLRBFNN model for the classification of multiple power signal disturbances. Another powerful learning paradigm is the use of ELM [25][26][27][28][29] for both regression-and classification-type problems. ELM is a much wider type of generalized SLFN (single hidden layer feedforward neural network) whose input weights and offsets of the hidden layer are chosen in a random manner and require no tuning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

Nayak¹,

Mishra

Dash

et al. 2015

Neural Comput & Applic

View full text Add to dashboard Cite

This paper presents a modified TLBO (teaching-learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data.

show abstract

“…The computation of the weight vector of the ELM given in Eq. (28) is done using all the data samples at a time.…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

Nayak¹,

Mishra

Dash

et al. 2015

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…PSO is originally attributed to Eberhart and Kennedy [56][57] and was first intended for simulating social behavior, as a stylized representation of the movement of organisms in a bird flock or fish school [58]. At present, PSO is widely applied in lots of optimization fields, like multi-objective optimization, pattern identification, neural network training and function optimization.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…When improved positions are being discovered these will then come to guide the movements of the swarm. The process is repeated and by doing so it is hoped, that a satisfactory solution will eventually be discovered [58][59].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Soft-sensor for Mother Liquor Purity in The Industrial Cane Sugar Crystallization Based on Extreme Learning Machine and Particle Swarm Optimization

Meng¹,

Pang²,

Chen³

et al. 2014

IJCA

View full text Add to dashboard Cite

show abstract

“…A recent one includes a new structure of connectionist network called the summation wavelet extreme learning machine (SW-ELM) to reduce the impact of random initialization procedure by using the well-known Nguyen Widrow [19] procedure to initialize the hidden neuron parameters [20]. Besides that, particle swarm optimization (PSO) is also presented to select and optimize the input weights and biases [21].…”

Section: Introductionmentioning

confidence: 99%

A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

Wong

Yap

et al. 2014

Neural Process Lett

View full text Add to dashboard Cite

This paper presents a Hybrid Fuzzy ARTMAP (FAM) and Online Extreme learning machine (OELM), hereafter denoted as FAM-OELM, which enables online learning to start from the first trained data samples without having to set up an initialization phase which requires a chunk of data samples to be ready prior to training. The idea of developing FAM-OELM is motivated by the ELM concept proposed by Huang et al., for being an efficient learning algorithm that provides better generalization performance at a much faster learning speed. However, different from the batch learning ELM and its variant called the online sequential extreme learning machine which still requires an initial offline training phase before it can turn into online training, the proposed FAM-OELM showcases a framework that enable online learning to commence right from the first data sample. Here, classification can be conducted at any time during the training phase. Such appealing feature of the proposed algorithm has strictly fulfilled the criteria of being truly sequential, while many of the existing algorithms are not. In addition, FAM-OELM automatically grows hidden neuron such that the network can accommodate new information without over fitting and compromising on the knowledge learnt earlier. The simulation results reveal the efficacy and validity of FAM-OELM when it is applied to a real world application and various benchmark problems.

show abstract

An improved evolutionary extreme learning machine based on particle swarm optimization

Cited by 212 publications

References 18 publications

Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

Soft-sensor for Mother Liquor Purity in The Industrial Cane Sugar Crystallization Based on Extreme Learning Machine and Particle Swarm Optimization

A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

Contact Info

Product

Resources

About