Abstract: Based on the inverse characteristics of the continuous Hopfield neural network(CHNN) model,combining with the observed data and numerical calculation results of groundwater level,an artificial neural network inverse analysis model for percolation coefficients of rock and soil body is established.Through employing the properties of selfastringency of netneural unit to finally trend towards a balance status,an inverse optimal result can be found.It is verified from an illustration that the computed results are in good agreement with the observed data.

Key words: continuous Hopfield neural network(CHNN) model, percolation coefficient, inversion