Neural Approach for Resource Selection with PSO for Grid Scheduling

Srinivasan, T.; Shanmugalakshmi, R.

doi:10.5120/8468-2392

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…DE has been broadly applied to solve several world problems [5,6], in addition to the introduction of the binary version of DE. There have been several advances and enhancements in this area [7][8][9]. DE has been used to derive universal function approximations for any analog function with random updating of weights [42].…”

Section: Related Workmentioning

confidence: 99%

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Using K-Fold Cross Validation Proposed Models for Spikeprop Learning Enhancements

Ahmed¹,

Ali²,

Shamsuddin³

2018

IJET

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Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte’s algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets.

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Section: Related Workmentioning

confidence: 99%

“…On the other hand, the synaptic weights are adjusted accordingly to minimize the network error. Therefore, in SpikeProp the actual weights are assigned to random initialization values followed the radius rule as depicted in (7) and (8). The input values range on the other hand will differ from each of dataset used.…”

Section: Proposed Radius Initial Weight (Riw)mentioning

confidence: 99%

Using K-Fold Cross Validation Proposed Models for Spikeprop Learning Enhancements

Ahmed¹,

Ali²,

Shamsuddin³

2018

IJET

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“…This best value is a global best and called gbest. When a particle takes part of the population as its topological neighbors, the best value is a local best and is called lbest [12].…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Hybrid Discrete Particle Swarm Optimization for Task Scheduling in Grid Computing

Karimi¹

2014

IJGDC

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Neural Approach for Resource Selection with PSO for Grid Scheduling

Cited by 2 publications

References 7 publications

Using K-Fold Cross Validation Proposed Models for Spikeprop Learning Enhancements

Using K-Fold Cross Validation Proposed Models for Spikeprop Learning Enhancements

Hybrid Discrete Particle Swarm Optimization for Task Scheduling in Grid Computing

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