التفاصيل البيبلوغرافية
| العنوان: |
A novel learning approach in deep spiking neural networks with multi-objective optimization algorithms for automatic digit speech recognition. |
| المؤلفون: |
Hamian, Melika, Faez, Karim, Nazari, Soheila, Sabeti, Malihe |
| المصدر: |
Journal of Supercomputing; Dec2023, Vol. 79 Issue 18, p20263-20288, 26p |
| مصطلحات موضوعية: |
ARTIFICIAL neural networks, OPTIMIZATION algorithms, DEEP learning, AUTOMATIC speech recognition, PATTERN recognition systems, SPEECH perception, WILD horses |
| مستخلص: |
Here, a new layered spiking neural network (SNN) learning framework is proposed using optimization algorithms for rapid and efficient pattern recognition and classification. In connection with the problem of learning deep SNN layers and with the help of different algorithms of gradient-based optimization and wild horse optimization, the two main parameters of spike neurons (threshold voltage and input weights) for different layers are calculated. SNN has been utilized to model several prominent datasets under machine learning systems, including IRIS and Trip datasets and digital speech recognition systems under MATLAB. Then, their performance is compared and evaluated in different scenarios with other deep learning methods such as artificial neural network and adaptive network-based fuzzy inference system. The results indicated an upsurge in identification and estimation accuracy. Integrating the algorithmic power of deep SNNs with adequate neuromorphic hardware creates an opportunity for speech recognition applications running locally on mobile and other applications. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
