In real active noise control (ANC) applications, the following situations frequently occur, one is that disturbances only present at the error sensor and have low correlation with reference signal, the other is that there is no enough space or ideal position for locating the reference sensor to satisfy causality condition. Thus the residual noise after feedforward control can be seen as uncorre...

The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three central issues that complicate this objective. Fir...

The present experiments examine the neuroregulatory hypothesis that the degree of sample-by-sample regularity of hormone output by an interlinked hypothalamopituitary target-organ system monitors the strength of feedback and/or feedforward signaling. To test this postulate and assess its generality, we implemented a total of nine thematically complementary perturbation experiments. In particula...

The idea of iterative control of batch processes has been discussed in the literature for some time. Many related concepts have been published under the names iterative control, learning control, run-to-run control, and others. This article uses the name iterative learning control (ILC). The main idea of ILC is illustrated in Fig. 1. An ILC controller provides feedforward control input to a pla...

This paper presents a practical comparison for some of the most relevant tuning rules feedforward compensators that have been published in recent years. The work is focused on rejection disturbances affect process output. different are evaluated simulation and several performance indices calculated order to provide quantitative comparison. All compared against standard feedback controller class...

Derivation of backpropagation in convolutional neural network (CNN) is conducted based on an example with two convolutional layers. The step-by-step derivation is helpful for beginners. First, the feedforward procedure is claimed, and then the backpropagation is derived based on the example. 1 Feedforward

The paper presents an approach to construction of hierarchical structures of data based concepts (granules), extending the idea of feedforward neural networks. The operations of processing the concept information and changing the concept specification through the network layers are discussed. Examples of the concepts and their connections are provided with respect to the case study of learning ...