Abstract: Active vibration control (AVC) systems without online path modeling and controller adjustment are provided that are able to adapt to an uncertain operating environment. The controller (250, 280, 315, 252, 282, 317, 254, 319) of such an AVC system is an adaptive recursive neural network whose weights are determined in an offline training and are held fixed online during the operation of the system. AVC feedforward, feedback, and feedforward-feedback systems in accordance with the present invention are described. An AVC feedforward system has no error sensor and an AVC feedback system has no reference sensor. All sensor outputs of an AVC system are processed by the controller for generating control signals to drive at least one secondary source (240). While an error sensor (480, 481) must be a vibrational sensor, a reference sensor (230, 270, 295, 305, 330) may be either a vibrational or nonvibrational sensor.

Abstract: A method and an apparatus are disclosed for processing a measurement process to estimate a signal process. The method synthesizes realizations of a signal process and a measurement process into a primary filter for estimating the signal process and, if required, an ancillary filter for providing the primary filter's estimation error statistics. Both the primary and the ancillary filters are made out of artificial recurrent neural networks (RNNs). Their implementation results in the filtering apparatus. The synthesis is performed through training RNNs. The weights/parameters and initial dynamic state of an RNN are determined by minimizing a training criterion by the variation of the same. The training criterion, which is constructed on the basis of a selected estimation error criterion, incorporates the aforementioned realizations. An alternative way to determine the initial dynamic state of an RNN is to simply set it equal to a canonical initial dynamic state.