基于模糊神经网络的混沌优化算法在动力配煤中的应用

针对燃煤结焦过程的模糊性和非线性特点，采用模糊神经网络评估混煤的结焦特性，并用结焦倾向性指标作为动力配煤的约束条件．然后基于混沌优化算法在解空间的遍历性优势，以混煤煤价最低为目标，结合罚函数处理的约束条件方法，对电站动力配煤优化问题进行寻优．结果表明，模糊神经网络结合变尺度混沌算法对电站动力配煤问题具有相当好的实用性和适应性，它能在较短的时间内获得相对最优解，有利于对实时配煤进行在线监测．

Application of Fuzzy Neural Network-Based Chaos Optimization Algorithm to Coal Blending

In view of the fuzzy and nonlinear characteristics of coal slagging process, a fuzzy neural network model is developed to evaluate the slagging characteristic of blended coal, with the slagging tendency index as the restriction condition of coal blending. Then, the coal blending in power plants is optimized by the chaos optimization algorithm with the ergodic advantage of chaotic motion in the solution space. The minimum price of blended coal is used as the optimization object and the restriction conditions are converted by means of the penalty function method. The results reveal that the combination of fuzzy neural network with the mutative-scale chaos algorithm shows good adaptability and practicability in the coal blending in power plants. The proposed model can help acquire relative optimum results in a short time and is conducive to the on-line monitoring of real-time coal blending in power plants.