The learning algorithm is a process of chaos optimization, which can make the network avoid the local minima problem and false saturation phenomenon. 網路的學習過程是一種混沌優化演算法，可有效避免普通神經網路的局部極值和假飽和現象的發生。
Based on the congruence non-repetition and ergodicity of chaos, the method will avoid the local optimal solution and find satisfactory globe optimal solution. 由於混沌優化演算法的疊代具有不重複性和遍歷性，因此該演算法可以避免陷入局部最優點而獲得全局最優。
