Analog VLSI circuits for learning rate adaptation in self-organizing neural networks

We present analog VLSI circuits for the learning rate adaptation in self-organizing neural networks using the Mulier-Cherkassky learning rate adapted to the continuous-time case. The circuit design uses the solution of the Riccati equation as a basis for implementing the learning rate schedule.