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Which begins the derivative calculation process


streams used to provide input–output data need not arise homogeneously, that is, from the same training task. Indeed, we have demonstrated that a single fixed-weight, recurrent neural network, trained by multistream EKF, can carry out multiple tasks in a control context, namely, to act as a stabilizing controller for multiple distinct and unrelated systems, without explicit knowledge of system identity [14]. This work demonstrated that the trained network was capable of exhibiting what could be considered to be adaptive behavior: the network, acting as a controller, observed the behavior of the system (through the system’s output), implicitly identified which system the network was being subjected to, and then took actions to stabilize the system. We view this somewhat unexpected behavior as being the direct result of combining an effective training procedure with enabling representational capabilities that recurrent networks provide.


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we provide insight into the nature of derivative calculations for training of both static and dynamic networks with EKF methods (see [12] for implementation details).

We assume the convention that a network’s weights are organized by node, regardless of the degree of decoupling, which allows us to naturally partition the matrix of derivatives of network outputs with respect to weight parameters, Hk, into a set of G submatrices Hi k, where G is the number of nodes of the network. Then, each matrix Hi kdenotes the matrix of derivatives of network outputs with respect to the weights associated with the ith node of the network. For feedforward networks, these submatrices can be written as the outer product of two vectors [3],

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which begins the derivative calculation process
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