By B. Yegnanarayana
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The delta learning law can be generalized to the case of multiple layers of a feedforward network. We will discuss the generalized delta rule or the error backpropagation learning law in Chapter 4. , f(xi)=xi. In this case the change in the weight is made proportional to the negative gradient of the error between the desired output and the continuous activation value, which is also the continuous output signal due to linearity of the output function. Hence, this is also called the Least Mean Squared (LMS) error learning law.
The inputs could be discrete or continuous data values, and likewise the outputs also could be discrete or continuous. The input and output could also be deterministic or stochastic or fuzzy. Interconnections: In an artificial neural network several processing units are interconnected according to some topology to accomplish a pattern recognition task. Therefore the inputs to a processing unit may come from the outputs of other processing units, and/or from external sources. The output of each unit may be given to several units including itself.
However, the structure of an artificial neural network is not useful, unless the rules governing the changes of the activation values and connection weight values are also specified. These rules are implied or specified in the activation and synaptic dynamics equations governing the behaviour of the network structure to accomplish the desired task. In a neural network with N processing units, the set of activation values of the units at any given instant defines the activation state of the network.
Artificial Neural Networks by B. Yegnanarayana