Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural network design
An Introduction to Neural Networks
An Introduction to Neural Networks
Increased Rates of Convergence Through Learning Rate Adaptation
Increased Rates of Convergence Through Learning Rate Adaptation
Neural Networks for Applied Sciences and Engineering
Neural Networks for Applied Sciences and Engineering
| Hi-index | 0.00 |
We present an improvement to backpropagation (BP) learning for neural networks using a (1+1) evolutionary strategy (ES). The goal is to provide a method than can adaptively change the learning parameters used in BP in an unconstrained manner. The BP/ES algorithm we propose is simple to implement and can be used with various versions of BP. In our experiments there is a substantial increase in performance for time series prediction