Adaptive particle swarm optimization learning in a time delayed recurrent neural network for multi-step prediction

In this study we propose the development of an adaptive particle swarm optimization (APSO) learning algorithm to train a non-linear autoregressive (NAR) neural network, which we call PSONAR, for short term time series prediction of ocean wave elevations. We also introduce a new stochastic inertial weight to the APSO learning algorithm. Our work is motivated by the expected need for such predictions by wave energy farms. In particular, it has been shown that the phase resolved predictions provided in this paper could be used as inputs to novel control methods that hold promise to at least double the current efficiency of wave energy converter (WEC) devices. As such, we simulated noisy ocean wave heights for testing. We utilized our PSONAR to get results for 5, 10, 30, and 60 second multistep predictions. Results are compared to a standard backpropagation model. Results show APSO can outperform backpropagation in training a NAR neural network.