Improved spikeprop algorithm for neural network learning
Spiking Neural Network (SNN) utilizes individual spikes in time domain to communicate and to perform computation in a manner like what the real neurons actually do. SNN had remained unexplored for many years because it was considered too complex and too difficult to analyze. Since Sander Bothe intro...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/33796/5/FalahYHAhmedPFSKSM2013.pdf |
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| Summary: | Spiking Neural Network (SNN) utilizes individual spikes in time domain to communicate and to perform computation in a manner like what the real neurons actually do. SNN had remained unexplored for many years because it was considered too complex and too difficult to analyze. Since Sander Bothe introduced SpikeProp as a supervised learning model for SNN in 2002, many problems which were not clearly known regarding the characteristics of SNN have now been understood. Despite the success of Bohte in his pioneering work on SpikeProp, his algorithm is dictated by fixed time convergence in the iterative process to get optimum initial weights and the lengthy procedure in implementing the sequence of complete learning for classification purposes. Therefore, this thesis proposes an improvement to Bohte’s algorithm by introducing acceleration factors of Particle Swarm Optimization (PSO) denoted as Model 1; SpikeProp using ? Angle driven Learning rate dependency as Model 2; SpikeProp using Radius Initial Weight as Model 3a, and SpikeProp using Differential Evolution (DE) Weights Initialization as Model 3b.The hybridization of Model 1 and Model 2 gives Model 4, and finally Model 5 is obtained from the hybridization of Model 1, Model 3a and Model 3b. With these new methods, it was observed that the errors can be reduced accordingly. Training and classification properties of the new proposed methods were investigated using datasets from Machine Learning Benchmark Repository. Performance results of the proposed Models (for which graphs of time errors with iterative timings, table of number of iterations required to reduce time error measurement to saturation level and bar charts of accuracy at saturation time error for all the datasets have been plotted and drawn up) were compared with one another and with the performance results of Standard SpikeProp and Backpropagation (BP). Results indicated that the performances of Model 4, Model5 and Model 1 are better than Model 2, Model 3a and Model 3b. The findings also reveal that all the proposed models perform better than Standard SpikeProp and BP for all datasets used. |
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