Hoạt động này có thể được thực hiện trong LabVIEW như sau. Trước tiên, chúng ta cần NN (mạng thần kinh) VI nằm trong đường dẫn ICTL ANNs Backpropagation NN Phương Pháp . Sau đó, chúng tôi tạo ra ba-ma trận có giá trị thực như đã thấy trong hình. . Các sơ đồ khối được thể hiện trong hình. . Theo quan điểm của sơ đồ khối, chúng ta cần một số thông số sẽ được giải thích sau. Tại thời điểm này, chúng ta quan tâm trong việc kết nối X-ma trận trong các kết nối đầu vào. | Introduction 51 Fig. Calculations of the output signal Solution. a We need to calculate the inner product of the vector X and W. Then the real-value is evaluated in the sigmoidal activation function. y fsigmoidali X wix C C C i - This operation can be implemented in LabVIEW as follows. First we need the NN neural network VI located in the path ICTL ANNs Backpropagation NN Methods . Then we create three real-valued matrices as seen in Fig. . The block diagram is shown in Fig. . In view of this block diagram we need some parameters that will be explained later. At the moment we are interested in connecting the X-matrix in the inputs connector and W-matrix in the weights connector. The label for the activation function is Sigmoidal in this example but can be any other label treated before. The condition 1 in the L 1 connector comes from the fact that we are mapping a neural network with four inputs to one output. Then the number of layers L is 2 and by the condition L 1 we get the number 1 in the blue square. The 1D array 4 1 specifies the number of neurons per layer the input layer four and the output layer one . At the globalOutputs the y-matrix is connected. From the previous block diagram of Fig. mixed with the block diagram of Fig. the connections in Fig. give the graph of the sigmoidal function evaluated at pictured in Fig. . Note the connection comes from the neuralNet- Fig. Block diagram of Example 52 3 Artificial Neural Networks Fig. Block diagram for plotting the graph in Fig. Fig. The value evaluated at a Sigmoidal function at the sumOut pin. Actually this value is the inner product or the sum of the linear combination between X and W. This real value is then evaluated at the activation function. Therefore this is the x-coordinate of the activation function and the y -coordinate is the globalOutput. Of course these two .
