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Reinforcement and backpropagation training for an optical neural network using self-lensing effects
A A Cruz-Cabrera1, M Yang, G Cui
1Department of Electrical and Computer Engineering, Wichita State University, Wichita, KS 67260-0044, USA.
IEEE Transactions on Neural Networks
|February 6, 2008
Summary
This study presents an optical feedforward neural network trained in real time using optical nonlinear materials. Researchers demonstrated optical error backpropagation for training, paving the way for complex optical computing tasks.
Area of Science:
- Optics
- Artificial Intelligence
- Optical Computing
Background:
- Optical neural networks offer potential for high-speed computation.
- Current limitations include training complexity and real-time adaptability.
Purpose of the Study:
- To present the optical bench training of a novel optical feedforward neural network.
- To demonstrate real-time training capabilities using optical nonlinear materials.
- To showcase optical error backpropagation for network weight updates.
Main Methods:
- Utilized an optical nonlinear material for neuron processing.
- Employed a trainable optical pattern for network weights.
- Implemented a reinforcement training paradigm for logic gate training.
- Demonstrated optical error backpropagation using a CCD camera for detection and computer for weight updates.
Main Results:
- Successfully trained six logic gates in real time.
- Demonstrated optical error backpropagation for training three logic gates.
- Verified the dynamic modulation of a light beam's phase front by the nonlinear material and weight pattern.
Conclusions:
- The developed optical neural network can be trained in real time.
- Optical error backpropagation is a viable method for training optical neural networks.
- This work provides a foundation for implementing multilayer optical neural networks for complex problem-solving.
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