基于代数神经网络的煤自燃预测

Forecasting of Coal Spontaneous Combustion Based on Algebra Neural Networks

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 could overcome the problem that BP neural network may easily fall into local minimum point and converge slowly. Weights are calculated by preferred exciter functions and algebraic algorithm. The algorithm transforms the complicated nonlinear optimization problem into linear algebraic equations and the adapting speed and accuracy of the neural network are improved. In an predicting coal spontaneous Instance using the algebraic neural network algorithm for predicting coal spontaneous combustion instance, because of mean normalized data preprocessing ,the problem that coal spontaneous combustion index gas transaction excessive disturbance classification results is resovled. Experimental results show the effectiveness and practicality of the algorithm.