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1396 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 TABLE I. INDICATOR SYSTEM FOR ASSESSING LAND ECO-SECURITY AND GRADE CRITERION OF LAND ECO-SECURITY Element Indicators Grade criterion of land eco-security (0.8, 1] a (0.6, 0.8] (0.4, 0.6] (0.2, 0.4] [0, 0.2] Pressure b Cultivated land area per capita(mu/person)x 1 >1.4 0.8-1.4 0.7-0.8 0.6-0.7 0.6-0.5&2500 2000-2500 1500-2000 1000-1500 500-1000&12 10-12 8-10 6-8 5-6&30 25-30 20-25 15-20 10-15&25°proportion (%)x 7 4.0 Population density(person/km 2 ) x 8 650 Area of stable yields despite drought or excessive rain Response proportion (%)x 9 >65 50-65 40-50 30-40 20-30&70 60-70 50-60 40-50 30-40&30 25-30 20-25 15-20 10-15&5 4-5 3-4 2-3 1-2&
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1397 Where w ij (k+1) is the new vector of weight and bias, w ij (k) is the current vector of weight and bias, D(k) is the negative gradient of wij(k) at time k, D(k–1) is the negative gradient of wij(k) at time k–1, η is the learning rate, β is the momentum constant which is a number between 0 and 1.The learning rate usually is trialed with a range of 0.1 to 0.3, if the learning rate is too high, the algorithm may oscillate and becomes unstable. And the momentum constant usually is trialed with a range of 0.9 to 1 since the low momentum may prevent the network from learning. In empirical study, the values of η and β were selected as 0.3 and 0.94, respectively. The performance measure adopted in empirical study was mean-squared error (MSE) function. The MSE function is as follows: 1 MSE = q t −s t n 2 ∑ ( ( ) ( )) (5) k k n t = 1 Where MSE is net error. q k (t) is kth network node desired output of tth training pattern and s k (t) is kth network node actual output of tth training pattern. original grade criterion of land eco-security presented Table Ⅰ were normalized between 0 and 1. The land eco-security grade is determined by the particular combination of indicator values. Therefore the BP neural network should be trained to learn the nonlinear relationship from the learn samples. The endpoints of the interval of land eco-security and indicator values whose correspondence relationship was showed in Table Ⅲ were selected. Although the range of land eco-security values is assumed [0, 1], the values of land eco-security in Hangzhou that will be assessed by BP neural network may greater than 1 or less than 0. So as to prevent the assessment values of land eco-security to break the limitation of an interval and enhance the distinguishability of assessment values of land eco-security, some proper endpoints were selected to reflect the correspondence relationship between the indicator values and land eco-security whose value was 0. There are a variety of methods for normalization. In this empirical study, the equation for normalization is as follows: S ′ = ( S −min )/(max − min ) (6) n n n. value n. value n. value Where max n.value is maximal value in input vector n, and min n.value is minimal value in input vector n. S n is the original input in input vector n. S n′ is the normalized value of the original in input vector n. TABLE II. DESCRIPTIVE STATISTICS OF ORIGINAL DATA IN HANGZHOU Min. Max. Mean Std.Deviation x 1 0.05 0.82 0.61 0.25 x 2 527.04 14001.76 4426.24 4454.91 x 3 10.37 45.29 25.59 11.29 Figure 2. Topological structure of BP neural network for assessment IV. ORIGINAL DATA AND PRETREATMENT The original indicator values of 8 districts (county-level cities, counties) applied to assess the land eco-security of Hangzhou were obtained from the statistical yearbooks and local municipal bureau of land and resources. The descriptive statistics of original indicator values is showed in Table Ⅱ. In order to improve the convergence rates and enhance the estimation accuracies since the feature of logsig transfer function. The original indicator values and x 4 117.55 554.03 338.03 149.02 x 5 3.89 38.31 16.01 11.54 x 6 14.69 76.86 51.39 26.79 x 7 0.04 5.47 2.18 2.01 x 8 103.00 3209.00 730.25 1037.25 x 9 6.79 87.43 49.02 26.13 x 10 30.58 63.27 38.04 10.73 x 11 1.69 190.31 34.87 64.00 x 12 1.29 8.56 3.76 2.20 © 2013 ACADEMY PUBLISHER
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Call for Papers and Special Issues
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(Contents Continued from Back Cover
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