基于模糊识别理论的上海市十一五节能减排评价(3)

[4]国务院关于进一步加大工作力度 确保实现“十一五”节能减排目标的通知 [5]张雪 高岚 徐善类 港航节能减排模糊评价方法研究

[6]熊德琪 环境系统模糊集分析理论与应用 大连：大连海事大学出版社，2001 [7]吕刚，蔡乐等 模糊识别方法在港口大气环境质量评价中的应用[J] 交通环保，2004.5-7

[8]上海市电力公司http://www.sh.sgcc.com.cn/load.loadPage.d?siteCode

=sdww&page=index.xml

[9]上海市统计年鉴2010 http://www.stats-sh.gov.cn/data/toTjnj.xhtml?y=2010

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附录：

%分级隶属度的确定

Y=[4 3 2 1;4 3 2 1;3.1 3.05 3 2.95;4 3 2 1;8 6 4 2];

X=[0.79 2.5 9.1 7.65;0.94 1.99 10.08 13.1;2.94 2.93 3.00 3.06;2 5.99 -0.26 3.91;6.06 7.85 4.95 4.26];

S=[(Y(1,1:4)-Y(1,4))/3;(Y(2,1:4)-Y(2,4))/3;(Y(3,1:4)-Y(3,4))/0.15;(Y(4,1:4)-Y(4,4))/3;(Y(5,1:4)-Y(5,4))/6];

R=[(X(1,1:4)-Y(1,4))/3;(X(2,1:4)-Y(2,4))/3;(X(3,1:4)-Y(3,4))/0.15;(X(4,1:4)-Y(4,4))/3;(X(5,1:4)-Y(5,4))/6;];

for i=1:4

for j=1:4

d(i,j)=sqrt(0.2*sum(R(1:5,j)-S(1:5,i))^2); end end for i=1:4

for j=1:4

u(i,j)=1/d(i,j);

end

end%2层BP神经网络系统 val =

Neural Network object: architecture:

numInputs: 1 numLayers: 2 biasConnect: [1; 1] inputConnect: [1; 0] layerConnect: [0 0; 1 0] outputConnect: [0 1]

numOutputs: 1 (read-only) numInputDelays: 0 (read-only) numLayerDelays: 0 (read-only) subobject structures:

inputs: {1x1 cell} of inputs layers: {2x1 cell} of layers

outputs: {1x2 cell} containing 1 output biases: {2x1 cell} containing 2 biases

inputWeights: {2x1 cell} containing 1 input weight layerWeights: {2x2 cell} containing 1 layer weight functions:

adaptFcn: 'trains' divideFcn: 'dividerand' gradientFcn: 'gdefaults' initFcn: 'initlay' performFcn: 'mse'

plotFcns: {'plotperform','plottrainstate','plotregression'} trainFcn: 'trainlm' parameters:

adaptParam: .passes

divideParam: .trainRatio, .valRatio, .testRatio gradientParam: (none) initParam: (none)

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performParam: (none)

trainParam: .show, .showWindow, .showCommandLine, .epochs, .time, .goal, .max_fail, .mem_reduc, .min_grad, .mu, .mu_dec, .mu_inc, .mu_max weight and bias values:

IW: {2x1 cell} containing 1 input weight matrix LW: {2x2 cell} containing 1 layer weight matrix b: {2x1 cell} containing 2 bias vectors other:

name: ''

userdata: (user information) %生成神经网络训练数据

x1=unifrnd(3,10,1,20);x2=unifrnd(2,3,1,20); x3=unifrnd(1,2,1,20);x4=unifrnd(0,1,1,20); x5=unifrnd(3,11,1,20);x6=unifrnd(2,3,1,20); x7=unifrnd(1,2,1,20);x8=unifrnd(0,1,1,20);

x9=unifrnd(3.05,5,1,20);x10=unifrnd(3,3.05,1,20); x11=unifrnd(2.95,3,1,20);x12=unifrnd(2.90,2.95,1,20); x13=unifrnd(3,6,1,20);x14=unifrnd(2,3,1,20); x15=unifrnd(1,2,1,20);x16=unifrnd(0,1,1,20); x17=unifrnd(6,8,1,20);x18=unifrnd(4,6,1,20); x19=unifrnd(2,4,1,20);x20=unifrnd(0,2,1,20); y1=ones(1,20);

Y=[y1 2*y1 3*y1 4*y1];

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