Tham khảo tài liệu 'artificial neural networks industrial and control engineering applications part 9', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | A Novel Frequency Tracking Method Based on Complex Adaptive Linear Neural Network State Vector in Power Systems 269 rule. It was developed by ProfessorBernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch-Pitts neuron. It consists of a weight a bias and a summation difference between Adaline and the standard McCulloch-Pitts perceptron is that in the learning phase the weights are adjusted according to the weighted sum of the inputs the net . In the standard perceptron the net is passed to the activation transfer function and the function s output is used for adjusting the weights. The main functional difference with the perceptron training rule is the way the output of the system is used in the learning rule. The perceptron learning rule uses the output of the threshold function either -1 or 1 for learning. The delta-rule uses the net output without further mapping into output values -1 or 1. The ADALINE network shown below has one layer of S neurons connected to R inputs through a matrix of weights W. This network is sometimes called a MADALINE for Many ADALINEs. Note that the figure on the right defines an S-length output vector a. The Widrow-Hoff rule can only train single-layer linear networks. This is not much of a disadvantage however as single-layer linear networks are just as capable as multilayer linear networks. For every multilayer linear network there is an equivalent single-layer linear network. Single ADALINE Consider a single ADALINE with two inputs. The following figure shows the diagram for this network. Simple ADALINE The weight matrix W in this case has only one row. The network output is a purelin n purelin Wp b Wp b Equation a can be written as follows a W1 1p1 W1 2 p2 b 39 40 Like the perceptron the ADALINE has a decision boundary that is determined by the input vectors for which the net input n is zero. For n 0 the equation Wp b 0 specifies such a decision boundary as .
