代数神经网络算法能够克服BP神经网络易于陷入局部极小和收敛慢的问题,通过优选激励函数和采用代数算法计算权值,将复杂的非线性优化问题转化为简单的代数方程组求解问题,提高了神经网络的精度与收敛速度.在使用代数神经网络算法进行煤自燃预测的实例中,采用均值规格化数据预处理,解决了煤自燃指标气体异动对分类结果的过度扰动.实验结果表明了算法的有效性和实用性. Algebraic neural network algorithm can overcome the problem that BP neural network is easy to fall into local minima and slow convergence. By optimizing the excitation function and using algebraic algorithm to calculate the weights, the complex nonlinear optimization problems are transformed into simple algebraic equations to solve the problem and improve The accuracy and convergence speed of neural network are studied.In the example of predicting coal spontaneous combustion using algebraic neural network algorithm, mean normalized data preprocessing is used to solve the excessive disturbance of coal spontaneous combustion index gas on the classification results.The experimental results show that the algorithm Effectiveness and practicality.