Dynamic modelling and time-series prediction by incremental growth of lateral delay neural networks

The difficult problems of predicting chaotic time series and modelling chaotic systems is approached using an innovative neural network design. By combining evolutionary techniques with others, good results can be obtained swiftly via incremental network growing. The network architecture and training algorithm make the creation of dynamic models efficient and hassle-free. The network results accurately reflect the outputs of the chaotic systems being modelled and preserve complex attractor structures of these systems.