Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy <svg style="vertical-align:-0.198pt;width:9.2250004px;" id="M1" height="10.4375" version="1.1" viewBox="0 0 9.2250004 10.4375" width="9.2250004" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.022,-0,0,-.022,.062,10.1)"><path id="x1D450" d="M383 397q0 -32 -35 -49q-12 -6 -23 8q-37 45 -84 45t-90 -71q-40 -65 -40 -167q0 -57 22 -86t59 -29q38 0 81.5 24.5t69.5 51.5l16 -21q-44 -53 -104 -84t-109 -31q-56 0 -89.5 41t-33.5 117q0 61 30 124t79 105q33 28 81 50.5t86 22.5q34 0 59 -15.5t25 -35.5z" /></g></svg>-Mean Clustering and Advanced Neural Network

<table>The structure of three-layer feedforward NN with switches.</table>

Journal of Applied Mathematics

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Figure 3

Figure 3: Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy <svg style="vertical-align:-0.198pt;width:9.2250004px;" id="M1" height="10.4375" version="1.1" viewBox="0 0 9.2250004 10.4375" width="9.2250004" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.022,-0,0,-.022,.062,10.1)"><path id="x1D450" d="M383 397q0 -32 -35 -49q-12 -6 -23 8q-37 45 -84 45t-90 -71q-40 -65 -40 -167q0 -57 22 -86t59 -29q38 0 81.5 24.5t69.5 51.5l16 -21q-44 -53 -104 -84t-109 -31q-56 0 -89.5 41t-33.5 117q0 61 30 124t79 105q33 28 81 50.5t86 22.5q34 0 59 -15.5t25 -35.5z" /></g></svg>-Mean Clustering and Advanced Neural Network 

Journal of Applied Mathematics

Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy -Mean Clustering and Advanced Neural Network

Figure 3