To improve the convergence speed and generalization ability of the fuzzy neural network(FNN), a fuzzy neural network, based on the hybrid gradient(HG) descent algorithm, is proposed in this paper. This HG-FNN can obtain the adaptive learning rate of the parameter adjustment process. Then, the chain rule is used to calculate the gradient descent of the learning process to adjust the parameters of FNN. Meanwhile, the convergence proof of HG-FNN is given in details to ensure the convergence speed and the precision of FNN. Finally, the proposed HG-FNN is used to model the nonlinear systems and predict the effluent qualities of wastewater treatment process. The results show that the proposed HG-FNN owns faster convergence speed, as well as with suitable generalization ability than other FNNs.