py. Algorithm (20 points) 1. Given the training data in the following table, which is corresponding with the function f(x,y)=x +3y+1, where" : " in the last row means there are more data. x2 2 1.0- 1.02 4.02 1.0- 2.02 7.02 2.0- 1.04 7.00 2.02 2.04 11.02 . + : We only have these data and we don't know the original function. You are required to design an algorithm to learn a function from the data above. + 47 Questions are: 1) Please draw the structure of ANN fulfilling the above task of function estimation (don't consider the weights). [5 points] 2) Please develop a learning algorithm to obtain the weights of your designed ANN: 2-1) Described the learning objective used in your designed learning algorithm. [3 points) 2-2) Use the Particle Swarm Optimization algorithm to optimize the ANN based on the above learning objective. Describe the pseudo-code of this optimization algorithm. [8 points] 2-3) Explain how you solve the tradeoff between the exploration ability and the exploitation ability of the above optimization algorithm of yours. [4 points] py. Algorithm (20 points) 1. Given the training data in the following table, which is corresponding with the function f(x,y)=x +3y+1, where" : " in the last row means there are more data. x2 2 1.0- 1.02 4.02 1.0- 2.02 7.02 2.0- 1.04 7.00 2.02 2.04 11.02 . + : We only have these data and we don't know the original function. You are required to design an algorithm to learn a function from the data above. + 47 Questions are: 1) Please draw the structure of ANN fulfilling the above task of function estimation (don't consider the weights). [5 points] 2) Please develop a learning algorithm to obtain the weights of your designed ANN: 2-1) Described the learning objective used in your designed learning algorithm. [3 points) 2-2) Use the Particle Swarm Optimization algorithm to optimize the ANN based on the above learning objective. Describe the pseudo-code of this optimization algorithm. [8 points] 2-3) Explain how you solve the tradeoff between the exploration ability and the exploitation ability of the above optimization algorithm of yours. [4 points]