Tham khảo tài liệu 'manufacturing the future 2012 part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Assembly Sequence Planning Using Neural Network Approach 441 A structure of a typical biological neuron is shown in Fig. 2 a . It has many inputs in and one output out . The connections between neurons are realized in the synapses. An artificial neuron is defined by Fig. 2 b Inputs x1 x2 . xn Weights bound to the inputs w1 w2 . wn An input function f which calculates the aggregated net input Signal U to the neuron this is usually a summation function An activation signal function which calculates the activation Level of the neuron O g u Figure 2 a . Schematic view of a real neuron 442 Manufacturing the Future Concepts Technologies Visions Figure 2 b Schematic representation of the artificial neural network Fig. 2 c shows the currently loaded network. The connections can represent the current weight values for each weight. Squares represent input nodes circles depict the neurons the rightmost being the output layer. Triangles represent the bias for each neuron. The neural network consists of three layer which are input output and hidden layers. The input and outputs data are used as learning and testing data. Figure 2 c Currently loaded network Assembly Sequence Planning Using Neural Network Approach 443 The most important and time-consuming part in neural network modeling is the training process. In some cases the choice of training method can have a substantial effect on the speed and accuracy of training. The best choice is dependent on the problem and usually trial-and-error is needed to determine the best method. In this study logistic function and back-propagation learning algorithm are employed to train the proposed NN. Back propagation algorithm is used training algorithm for proposed neural networks. Back propagation is a minimization process that starts from the output and backwardly spreads the errors Canbulut Sinanoglu 2004 . The weights are updated as follows Aw . . t -n dE oOWi t -1 1 y dwI t yA 7 where n is the learning rate and a is the momentum .
