A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution

Saleh, Abdulrazak Yahya; Hamed, Haza Nuzly Bin Abdull; Shamsuddin, Siti Mariyam; Ibrahim, Ashraf Osman

doi:10.1007/978-3-319-59427-9_60

Cited by 3 publications

(2 citation statements)

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“…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]. For the first generations of ANN, the neurons are restricted to binary inputs and outputs.…”

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%

“…D.E. is considered a stochastic powerful search algorithm introduced by [42]. Integrate-and-fire model was the first model to be applied in spikes over time to transfer information [2].…”

Section: Introductionmentioning

confidence: 99%