A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights

In the field of marine detection and warning, predicting the heights of ocean wave is a very important project. In order to predict the ocean wave heights accurately and quickly, our methodology utilizes a hybrid Mind Evolutionary Algorithm-BP neural network strategy (MEA-BP). This paper investigates how the BP neural network (BPnn) evolution with MEA improves the generalization ability and predictability of BPnn. The MEA-BP model combines the local searching ability of the BPnn and the global searching ability of the MEA which can avoid premature convergence and poor prediction effect. In order to search individuals which contain optimal weights and thresholds, the MEA searches all the initial weights and thresholds intelligently by similartaxis and dissimilation operation, finally assign them to the initial BPnn. The study is conducted using data collected from 12 observation points across two geographically distinct regions, Bohai Sea, Yellow Sea, for the period from Jan 1, 2016 to Dec 31, 2016. The data is chosen such that the study covers a wide range of geographical locations and different weather. We compare the prediction performance and generalization capabilities of MEA-BP with the Genetic Algorithm-BP neural network model (GA-BP) which also developed with the BPnn. The performance study results demonstrate that MEA-BP performs better than the GA-BP and Standard BP neural network model (St-BP) with faster running time and higher prediction accuracy.