Tham khảo tài liệu 'adaptive control 2011 part 9', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Adaptive Control Based On Neural Network 193 2 _ Oi aij n2 _ov . i112 u2 dTv O2 p u2 bij 35 where i 1 2 L n j 1 2 L m aij and b ij are the mean and the standard deviation of the Gaussian membership function the subscript ij indicates the jth term of the ith input variable. Fig. 6. Structure of four-layer RFNN Layer 3 Rule Layer This layer forms the fuzzy rule base and realizes the fuzzy inference. Each node is corresponding to a fuzzy rule. Links before each node represent the preconditions of the corresponding rule and the node output represents the firing strength of corresponding rule. If the qth fuzzy rule can be described as 194 Adaptive Control qth rule if X1 is A- X2 is A 1 . xn is An then y1 is B y2 is B21 . yp is Bp1 where A5 is the term of the ith input in the qth rule Bj1 is the term of the jth output in the qth rule. Then the qth node of layer 3 performs the AND operation in qth rule. It multiplies the input signals and output the product. Using o2qi to denote the membership of Xi to A5 where qi e 1 2 L m then the input and output of qth node can be described as uq nojq. oq uq i 1 2 . n q 1 2 . l 36 i Layer 4 Output Layer Nodes in this layer performs the defuzzification operation. the input and output of sth node can be calculated by u4 L w4qoq q o4 u4 L oq q 37 where s 1 2 L p q 1 2 L l w4q is the center of Bjq which represents the output action strength of the sth output associated with the qth rule. From the above description it is clear that the proposed RFNN is a fuzzy logic system with memory elements in first layer. The RFNN features dynamic mapping with feedback and more tuning parameters than the FNN. In the above formulas if the weights in the feedback unit w1 are all equal to zero then the RFNN reduces to an FNN. Since a fuzzy system has clear physical meaning it is very easy to choose the number of nodes in each layer of RFNN and determine the initial value of weights. Note that the parameters w1 of the feedback units are not set from human
