【正文】 that gene is maximally impacted, and then gene, phytohormone, climate, dease is following. Optimize leaves shape for maximize exposure Explain and answer requirment From the model 2?s conclusion we get above, we acquaint that sunlight is a critical factor for plants. Plants are photoautotrophs, obtain their own energy through photosynthesis and produce oxygen in the meantime. From evolutionary considerations, it seems that the leaves always in a favorable direction, so that they can maximize their exposure to the sun. As is considered above, sunlight changes the blade shape through influencing the distribution of growth hormones. Thus we discuss this issue from the perspective of growth hormones. First of all, we need to know more specifically how growth hormones affect leaves. Growth hormones is a directional transport, but sometimes it transports to the backlight. By inhibiting the growth of the shaded side to effects the phototropism movement of plants. Due to this phenomenon leaves “do their best efforts” make themselves exposed. In other words , It is to minimize mutual shading impact. We try to build cells mechanic model to solve this problem, meanwhile, explain reasons for this phenomenon. Conclusion: plants always “optimize” their leaves shape for maximize exposure. Put another way, they are “minimize” overlapping individual shadows that are cast. The result can primely explain the reasons. Set up a Elastic mechanics model We choose Elastic mechanics model to simplify and imitate Physical force of mesophyll cells. We assume that each cell of leaf is subject to two forces, one is the expansive force inf generated by cytoplasm of cells inside。 ? Difference gene. we believe that there exits 4 base factors that lead to the variety of leaves shape. They are climate, disease, phytohormone and gene. And we endeavor find out reasons to them. climate: the change of sun shine， water， temperature， humidity which alters leaves shape. disease: through effecting the activity of an enzyme, so that influence leaves shape. Phytohormone auxin: have influence on gene expression gene: through DNA determine the general leaf shape Set up a AHP model to value these base factors We solve this problem based on the reasons listed above. After analyzing all of them, we hold an opinion that human attempt is usually fairly haphazard. Since we view all the leaves? living environment is stable, we don?t take artificial factor into Team 15263 Page 7 of 23 consideration. We definite total impact as target layer”, and climate, disease, phytohormone, gene as the criterion layer. As shown in the following figure : Figure ： reasons for the various shapes Paired parison matrix structure To analyze the effects of electric vehicles? widespread use on the environment, social, economic and health, we each take two factors: , ( , 1, 2 , 4)ijC C i j = (11) , ( , 1, 2 , 4)ijC C i j = (12) They are used to represent environmental, economic, social and health by turns. All results are available the following pairwise parison matrix: 1( ) , 0 ,i j n n i j j i ijA a a a a180。 Internal factors: ? Deformation of cells, moisture loss of Mesophyll cells may cause volume decrease。 ? Plant diseases and insect pests。 (6) Therefore, the superimposed signal i received is: 3 3 21 1 1()s s sjki i j j i j kj j kh w H w w Ij= = ===邋 ? (7) The final output of the work is: 3 3 21 1 1( ) ( ) ( ( ) )s s z sjki i i j j i j kj j kO h w H w w Ij j j j= = == = =邋 ? (8) We hope the final output is idealization. For example. For example,after learning maple leaf ?s features, if the output is like the form of ( )1,0,1,0,1,1,0 , we called the output like this the ideal output, the ideal output is noted for {}siT . Figure ： Different types of shapes ()a Linear. ()b Lanceolate. ()c Oblanceolate. ()d Spatulate. ()e Ovate. ()f Obovate. ()g Elliptic. ()h Oblong. ()i Deltoid. ()j Reniform. ()k Orbicular. ()l Peltate. ()m Perfoliate()n Connate. Backpropogation In order to minimizing the differences between actual output and desired output，we choose BP algorithm,which is one part of NN . As set forth, the error obtained when training a pair (pattern) consisting of both input and output given to the input layer of the work is given by: 255,1( ) ( )2 iiisE w T O=229。 (1) Team 15263 Page 4 of 23 Where w is the weight, x is the input node value: k k kvuq= (2) k? is Threshold value: ()kkyvj= (3) ()j is activation function, ky is the output of a neuron in the successive layer. The activation function ()vj is a nonlinear function and is given by: ( ) ( )11 expv vj = + (4) Multilayer perceptron work This is the main structure of NN . Figure ： Multilayer perceptron work The structure of the Artificial Neural Network ANN in this work contains three layers: input, hidden and output layers as shown in figure . We use input layer to input the characteristics of the leaves. Each layer contains ,ij and k nodes. The node is also called neuron or unit. This study summarized eight factors for ANN input, that is to say 8i= . The eight input units are sawtooth number, petiole length, blade length, blade width, blade thickness, leaf area and circular degree. For the hidden layer we make 3j= . The function of the output layer is to output classified information corresponding to the input data. The value of k ranges from the types of leaves we need to identify. The jkw is denoted as numerical weights between input and hidden layers, ijw between hidden and output layers as also Team 15263 Page 5 of 23 shown in figure . In fact, as for a sample of “s ”, the input of the hidden layer is: 21s sj jk kkh w I==229。Team 15263 Page 0 of 23