Widrow-Hoff

There are modifications to the perceptron learning rule to help effect faster convergence. The Widrow-Hoff delta rule (Widrow Hoff, 1960) multiplies the delta term by a number less than 1, called the learning rate, tv This effectively causes smaller changes to be made at each step. There are heuristic rules to decrease T] as training time increases the idea is that big changes may be taken at first and as the final solution is approached, smaller changes may be desired. 

Potentially, these findings could be due to the simple ASAD strategy implemented here. For example, ASAD agents are not designed to consider the rate of change of prices in the market. Perhaps a more suitable approach would be to implement an adaptive learning rule, such as the Widrow-Hoff delta rule [28], which is the basis of the adaptation mechanism in ZIP [8] and AA [26]. We reserve this extension for future work. 

BSB Widrow-Hoff The weight is changed in proportion to the error in the output, D, - O,-. Each change reduces the error. 

Delta Rule The weights are changed similarly to the Widrow-Hoff rule, Eq. [34]. Mathematically, we have ... 

Neural Network Architecture for EMG Classification BP is based on the generalized form of Widrow-Hoff learning rule to multiple-layer network and nonlinear differentiable transfer function. Here, the input vectors and corresponding target vectors are used to train the neural network until it can approximate a function or associate input vectors with... 

Widrow, B. and Hoff, M.E. (1960) Adaptive switching circuits. In IEEE WESCON Convention Record, IRE, New York, pp. 96-104. 

Other rules are of course possible. One popular choice called the Delta Rule was introduced in 1960 by Widrow and Hoff ([widrowGO], [widrow62]). Their idea was to adjust the weights in proportion to the error (= A) between the desired output (= D(t)) and the actual output (= y(t)) of the perceptron ... 

B. Widrow and M. Hoff, 1960 WESCON Convention Record Part 4, 96-104 (1960), Adaptive Switching Circuits. 

Widrow and Hoff (1960,1962) developed a supervised training algorithm which allows training a neuron for the desired response. This rule was derived so that the square of the difference between the net and... 

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