A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks

Luo, Xiong; Chang, Xiaohui

doi:10.1007/s12555-014-0309-8

Cited by 44 publications

(22 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the LSSVM-based scheme may achieve a good prediction effect, it is impracticable in some monitoring systems in real time because there is an obstacle of employing LSSVM to predict nonlinear time series online. In [11], the author combined ELM and GM for data fusion. Different from GM-LSSVM, the calculating effort of this scheme is improved greatly due to the fact that the training phase in ELM could be completely implemented with a very fast computational speed.…”

Section: Prediction Schemes In Wsnmentioning

confidence: 99%

“…With E-KLMS, the computational complex could be accordingly improved while achieving high-quality solutions. It is worth to note that, different from our previous works [11,23] in which much time are spent in data training because those methods need to construct training set at every sampling point, the samples are only trained once with the proposed method in this article, which will save much time. In consideration of the above reasons, E-KLMS may perform better in data prediction in WSN.…”

Section: Introductionmentioning

confidence: 96%

“…Particularly, in a WSN, most devices of sensor nodes have limited power energy and it's hard to recharge these sensor nodes considering the big amount of those devices in some applications [10]. Furthermore, because the sensed data in a continuous time period are probably closely related to their adjacent data, redundant transmissions between different nodes would accordingly consume much energy [11]. Hence, some data prediction schemes have been proposed to reduce transmissions in a WSN.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some popular machine learning approaches, i.e., least squares support vector machine (LSSVM) and extreme learning machine (ELM), were also used to achieve data prediction in a WSN [11,17]. The detailed analysis for the above methods could be found in Section 2.2.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

Luo

Zhang

et al. 2016

Entropy

Self Cite

View full text Add to dashboard Cite

Abstract:With the recent emergence of wireless sensor networks (WSNs) in the cloud computing environment, it is now possible to monitor and gather physical information via lots of sensor nodes to meet the requirements of cloud services. Generally, those sensor nodes collect data and send data to sink node where end-users can query all the information and achieve cloud applications. Currently, one of the main disadvantages in the sensor nodes is that they are with limited physical performance relating to less memory for storage and less source of power. Therefore, in order to avoid such limitation, it is necessary to develop an efficient data prediction method in WSN. To serve this purpose, by reducing the redundant data transmission between sensor nodes and sink node while maintaining the required acceptable errors, this article proposes an entropy-based learning scheme for data prediction through the use of kernel least mean square (KLMS) algorithm. The proposed scheme called E-KLMS develops a mechanism to maintain the predicted data synchronous at both sides. Specifically, the kernel-based method is able to adjust the coefficients adaptively in accordance with every input, which will achieve a better performance with smaller prediction errors, while employing information entropy to remove these data which may cause relatively large errors. E-KLMS can effectively solve the tradeoff problem between prediction accuracy and computational efforts while greatly simplifying the training structure compared with some other data prediction approaches. What's more, the kernel-based method and entropy technique could ensure the prediction effect by both improving the accuracy and reducing errors. Experiments with some real data sets have been carried out to validate the efficiency and effectiveness of E-KLMS learning scheme, and the experiment results show advantages of the our method in prediction accuracy and computational time.

show abstract

Section: Prediction Schemes In Wsnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

Luo

Zhang

et al. 2016

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…In theory, both BP neural networks and RBF neural networks can approximate any nonlinear function with arbitrary precision. However, their approximation performance is different due to their different incentive functions [7].…”

Section: Introductionmentioning

confidence: 99%

Method of Wireless Sensor Network Data Fusion

Ma¹,

Liu²,

Luo³

2017

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Abstract-In order to better deal with large data information in computer networks, a large data fusion method based on wireless sensor networks is designed. Based on the analysis of the structure and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented. The effectiveness of information fusion processing methods is tested by RBF information fusion algorithm. The proposed algorithm is applied to heterogeneous information fusion of cluster heads or sink nodes in wireless sensor networks. The simulation results show the effectiveness of the proposed algorithm. Based on the above finding, it is concluded that the RBF neural network has good real-time performance and small network delay. In addition, this method can reduce the amount of information transmission and the network conflicts and congestion.

show abstract

Prediction of Pulp Concentration Using Extreme Learning Machine

Jiang

Luo

Yang

et al. 2016

Proceedings in Adaptation, Learning and Optimization

Self Cite

View full text Add to dashboard Cite

Pulp concentration is one of the most important production parameters during ore dressing process. Generally, pulp concentration not only affects concentrate grade and recovery rate, but also has a major influence on the chemical and power consumptions during the flotation process. Recently, there has been a growing interest in the study of prediction for pulp concentration to improve the productivity and reduce consumption of various resources. Since the pulp concentration and other production parameters are nonlinearly related, it imposes very challenging obstacles to the prediction for this parameter. Because extreme learning machine (ELM) has the advantages of extremely fast learning speed, good generalization performance, and the smallest training errors, we employ ELM to predict pulp concentration in this paper. Pulp concentration data is first preprocessed using phase space reconstruction method. Then time series prediction model is adjusted from one dimension to multiple dimensions and thus it is established by several improved ELM algorithms, including traditional ELM, kernel-based ELM (Kernel-ELM), regularized ELM (R-ELM), and L 2 -norm based ELM (ELM-L2). The experiments are conducted with a real-world production data set from a mine. The experimental results show the effectiveness of ELM-based prediction approaches, and we can also find that ELM- L2 has better prediction effects than other algorithms with the increase of sample size. Both training speed and prediction accuracy are improved by employing ELM-L2 to the prediction of pulp concentration.

show abstract

A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks

Cited by 44 publications

References 17 publications

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

Method of Wireless Sensor Network Data Fusion

Prediction of Pulp Concentration Using Extreme Learning Machine

Contact Info

Product

Resources

About