A Kind of Extreme Learning Machine Based on Memristor Activation Function

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

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Modeling of Drying Process of Bittim Nuts (pistacia terebinthus) in a Fixed Bed Dryer System by Using Extreme Learning Machine

International Journal of Food Engineering ◽

10.1515/1556-3758.2737 ◽

2012 ◽

Vol 8 (4) ◽

Cited By ~ 7

Author(s):

Asım Balbay ◽

Engin Avci ◽

Ömer Şahin ◽

Resul Coteli

Keyword(s):

Extreme Learning Machine ◽

Fixed Bed ◽

Experimental Studies ◽

Learning Rule ◽

Activation Function ◽

Optimal Topology ◽

Gradient Based ◽

Learning Machine ◽

Pistacia Terebinthus ◽

Hidden Neurons

Abstract Artificial neural networks (ANNs) have been widely used in modeling of various systems. Training of ANNs is commonly performed by backpropagation based on a gradient-based learning rule. However, it is well-known that such learning rule has several shortcomings such as slow convergence and training failures. This paper proposes a modeling technique based on Extreme Learning Machine (ELM) eliminating disadvantages of backpropagation based on a gradient-based learning rule for the drying of bittim (pistacia terebinthus). The samples for ELM based model are obtained by experimental studies. In experimental studies, the sample mass loss rate as a function time was investigated in different air velocities (0.5 and 1 m/s) and air temperatures (40, 60 and 80°C) in a designed dryer system. The obtained samples from experiments are used for training and testing of ELM. Further, some parameters of ELM such as type of activation function and the number of hidden neurons are set to obtain the best possible modelling results. The obtained prediction results show that ELM algorithm with tangent sigmoid activation function and 20 hidden neurons is appeared to be most optimal topology since maximum R2 and minimum rms (0.0500) and cov (0.2256) values are obtained. Thus, it is concluded that ELM can be used as an effective modelling tool in the drying of bittim (pistacia terebinthus) in fixed bed dryer system.

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q-Gaussian activation function Circular Extreme learning machine for classification problems

2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE) ◽

10.1109/iciteed.2016.7863294 ◽

2016 ◽

Cited By ~ 2

Author(s):

Sarutte Atsawaraungsuk

Keyword(s):

Extreme Learning Machine ◽

Activation Function ◽

Classification Problems ◽

Learning Machine

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Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

Energies ◽

10.3390/en12071331 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1331 ◽

Cited By ~ 5

Author(s):

Peng Jiang ◽

Jun Dong ◽

Hui Huang

Keyword(s):

Renewable Energy ◽

Energy Consumption ◽

Power Consumption ◽

Extreme Learning Machine ◽

Activation Function ◽

Forecasting Model ◽

Fossil Energy ◽

Mode Decomposition ◽

Bacterial Foraging Algorithm ◽

Learning Machine

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

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

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2020.62.1.15 ◽

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.

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