Desulfurization efficiency prediction method based on time delay neural network and support vector machine

The invention belongs to the technical field of desulfurization, and particularly discloses a desulfurization efficiency prediction method based on a time delay neural network and a support vector machine. Comprising the steps of collecting operation data and desulfurization efficiency data of a desulfurization system, constructing a primary desulfurization efficiency prediction model based on a neural network structure, adding a hidden layer of the primary desulfurization efficiency prediction model to construct the desulfurization efficiency prediction model, and calculating an accumulated error of a back propagation error of the desulfurization efficiency prediction model. Transmitting the accumulative error to a multi-classification support vector machine, carrying out accumulative error training, taking the optimal accumulative error as a descending strategy of the desulfurization efficiency prediction model, carrying out convergence calculation, and timely adjusting the weight and the threshold value of the desulfurization efficiency prediction model, so that the prediction precision of the desulfurization efficiency prediction model meets the requirement. And obtaining an optimal desulfurization efficiency prediction model. According to the method, the prediction precision can be greatly improved, and the method has better robustness, memory ability, nonlinear mapping ability and strong self-learning ability.