Error back propagation (EBP) is a widely used training algorithm for feedforward neural networks (FFNNs)，but low learning rate limits its applications in the networks with complex topology architecture and large patterns.In this work

two modifications on Levenberg-Marquardt algorithm for FFNNs were made.One modification was made on the objective function

while the other was the evaluation of the initial weights and biases.The modified algorithm gave a better convergence rate compared to the traditional EBP algorithm and it was less computationally intensive and required less memory.The performance of the algorithm was verified separately with polymer and protein systems.The results showed that the BP network based on modified Levenberg-Marquardt algorithm could be used to predict the binodal curve of H2O/DMAc(N，N-dimethylacetamide)/PSf(polysulfone) system and lysozyme solubility in aqueous salt solution.