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 neural system is disclosed for processing an exogenous input process to produce a good outward output process with respect to a performance criterion, even if the range of one or both of these processes is necessarily large and/or keeps necessarily expanding during the operation of the neural system. The disclosed neural system comprises a recurrent neural network (RNN) and at least one range extender or reducer, each of which is a dynamic transformer. A range reducer transforms dynamically at least one component of the exogenous input process into inputs to at least one input neuron of said RNN. A range extender transforms dynamically outputs of at least one output neuron of said RNN into at least one component of the outward output process. There are many types of range extender and reducer, which have different degrees of effectiveness and computational costs.

Abstract: To enhance the safety and security of the operation of a railway network, a railway operation monitoring and diagnosing system is disclosed that monitors and diagnoses the entire railway network as an integrated system. The railway operation monitoring and diagnosing system comprises a railway operation predictor and a diagnosing means. The railway operation predictor generates anticipated values of selected railway operation state (ROS) variables. ROS variables may discrete or continuous. If there are continuous ROS variables selected, the railway operation predictor also determines the safety intervals of these continuous ROS variables. The diagnosing means examines the measured values of the selected ROS variables versus their anticipated values and/or safety intervals to detect and diagnose their discrepancies. A heuristics, statistics, fuzzy logic, artificial intelligence, neural network, or/and expert system is included in the diagnosing means for diagnosing the records of such discrepancies.

Abstract: A recursive neurofilter comprising a recursive neural network (NN) is disclosed for processing an information process to estimate a signal process with respect to an estimation error criterion. The information process either consists of a measurement process, or if the signal and measurement processes are time-variant, consists of the measurement process as well as a time variance process, that describes the time-variant properties of the signal and measurement processes. The recursive neurofilter is synthesized from exemplary realizations of the signal and information processes. No assumptions such as Gaussian distribution, linear dynamics, additive noise, and Markov property are required. The synthesis is performed essentially through training recursive NNs. The training criterion is constructed to reflect the mentioned estimation error criterion with the exemplary realizations.

Abstract: An adaptive neural system (ANS) disclosed herein comprises a processor and an adaptor. The processor includes mainly a neural network whose adjustable weights are divided into nonadaptively and adaptively adjustable weights. The nonadaptively adjustable weights are determined through minimizing or reducing a nonadaptive training criterion in an off-line nonadaptive training. Being constructed with a priori training data, the nonadaptive training criterion is a function of the nonadaptively adjustable weights and the diversity variables associated with typical values of the environmental parameter. During an operation of the adaptive neural system, only the adaptively adjustable weights are adjusted on-line to adapt to the unknown environmental parameter. This adaptive training is achieved by minimizing or reducing an adaptive training criterion.

Abstract: A method and apparatus is provided for processing a measurement process to estimate a signal process, even if the signal and/or measurement processes have large and/or expanding ranges. The method synthesizes training data comprising realizations of the signal and measurement processes into a primary filter for estimating the signal process and, if required, an ancillary filter for providing the primary filter's estimation error statistics. The primary and ancillary filters each comprise an artificial recurrent neural network (RNN) and at least one range extender or reducer. Their implementation results in the filtering apparatus. Many types of range extender and reducer are disclosed, which have different degrees of effectiveness and computational cost.