With the development of control technology,the scales of the control systems become larger and larger,so does the computational load of the identification algorithms.For nonlinear systems with complex structures,especially for the nonlinear systems that contain the products of the unknown parameters of the nonlinear part and linear part,the sizes of the involved matrices in the over-parameterization model based least squares methods greatly increase,this makes the computational amount of the identification algorithms increase dramatically.Therefore,it is necessary to explore new parameter estimation methods with less computation.For output nonlinear equation-error type systems,this paper discusses the over-parameterization model based recursive least squares type identification algorithms; in order to reduce computational loads and improve the identification accuracy,this paper uses the decomposition technique and the filtering technique and presents the model decomposition based recursive least squares identification methods and the filtering based recursive least squares identification methods.Finally,the computational efficiency,the computational steps and the flowcharts of several typical identification algorithms are discussed.