Dynamic binary neural networks and evolutionary learning

This paper studies the dynamic binary neural network having N bits input, N bits output and ternary weighting parameters of the hidden layer. Applying feedback from the output to the input, the network can generate dynamic binary sequence. We presents a simple learning algorithm that uses the genetic algorithm and reduces the number of hidden neurons efficiently. Performing a basic numerical experiment, the algorithm efficiency is confirmed. Application to switching power converters is also discussed.