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基于变异CPSO算法的LSSVM出水COD的软测量研究
Keywords: 变异混沌粒子群算法 ,最小二乘支持向量机 ,污水处理 ,化学耗氧量 ,软测量
Abstract:
在分析混沌粒子群优化算法(CPSO)和最小二乘支持向量机(SVM)理论基础上，以某污水处理厂的氧化沟系统为对象，采用带有末位淘汰机制的混沌粒子群优化算法优化支持向量机的参数，建立了基于变异CPSO算法的LS-SVM的氧化沟出水水质COD软测量模型，并与PSO-LSSVM，LSSVM模型比较，研究表明，ICPSO-LSSVM模型预测准确，泛化性能好，且该模型预测结果中相对误差小于10%的样本达到90%，最大相对误差仅为12.5%，均方差MSE为0.0106，模型具有较高的精度，基本可以实现出水COD浓度的在线预估。
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