中南大学学报（自然科学版）2012，Vol.43Issue(4)：1538-1544,7.
鱼群算法与神经网络结合的节能减排效果评价
Comprehensive effect evaluation of energy saving and emission reduction based on fish-swarm algorithm optimizing neural network
摘要
Abstract
Based on five factors, i.e. pollutant emission reduction rate, emission reduction of unit industrial added value, total investment in industrial pollution control, related indicators of GDP and energy consumption reduction rate, an index system to comprehensively evaluate energy saving and emission reduction effect was established. Then the feasibility of optimizing BP neural network by fish-swarm algorithm was analyzed, and the procedures for the optimization of BP neural network by fish swarm algorithm were researched.According to the energy-saving data in seven regions from 2006 to 2009 and based on the assessment of expert scoring, energy-saving effects were evaluated by neural network and fish swarm algorithm-optimized neural network respectively. The results show that during the convergence, the error local optima is about 0.7 for a long time using neural network algorithm-optimized neural network while it reaches the target error figure quickly using fish swarm algorithm-optimized neural network.When error is 0.001, the former reaches the target after 202 times of training, and the latter only 75 times. The results indicate that the method offish swarm algorithm-optimized neural network is accurate, fast, simple and easy, and this method is effective for the evaluation on effect of energy saving and emission reduction.关键词
鱼群算法/BP神经网络/节能减排/综合评价Key words
fish-swarm algorithm/ BP neural network/ energy saving and emission reduction/ comprehensive evaluation分类
资源环境引用本文复制引用
杨淑霞,韩奇,徐琳茜,刘达,路石俊..鱼群算法与神经网络结合的节能减排效果评价[J].中南大学学报（自然科学版）,2012,43(4):1538-1544,7.基金项目
中央高校基本科研业务费专项资金资助项目(09MR39) （09MR39）
教育部人文社会科学研究规划项目(11YJA790181) （11YJA790181）
