Continual learning systems can adapt to new tasks, changes in data distributions, and new information that becomes incrementally available over time. The key challenge for such systems is how to mitigate catastrophic forgetting, i.e., how to prevent the loss of previously learned knowledge when new tasks need to be solved. In our research, we investigate self-organizing incremental neural networks (SOINN) for continual learning from both stationary and non-stationary data. We have developed a new algorithm, SOINN+, that learns to forget irrelevant nodes and edges and is robust to noise.