Abstract: A clothes load estimation method for a clothes washer, and a washing machine having a controller for controlling at least one operating condition for a clothes washer, are provided, in which a value of an inertia (I.sub.c) of the clothes load is determined in accordance with a relationship to a sensed basket acceleration (.alpha..sub.o) and a sensed motor phase angle (.phi..sub.m). The relationship is based on and derived from a dynamic modeling of a motor/inner clutch subsystem, an outer clutch, subsystem, a belt subsystem, and a washer basket/clothes load subsystem. The method and apparatus require only low cost motor phase sensors and either velocity or position sensors in controlling the washer operating condition.

Abstract: The present invention discloses a system and method for predicting the dryness of clothing articles in a clothes dryer 10. In one embodiment of this invention, the clothes dryer 10 uses a temperature sensor 52, a phase angle sensor 54, and a humidity sensor 56 to generate signal representations of the temperature of the clothing articles, the motor phase angle, and the humidity of the heated air in the duct, respectively. A controller 58 receives the signal representations and determines a feature vector. A neural network 168 uses the feature vector to predict a percentage of moisture content and a degree of dryness of the clothing articles in the clothes dryer 10. In another embodiment of this invention, the clothes dryer uses a combination of sensors to predicts a percentage of moisture content and a degree of dryness of the clothing articles.

Abstract: A gas turbine control system includes a controller that is coupled to actuator systems that govern operation of the gas turbine. The controller includes a processor for generating respective actuator control signals in correspondence with a plurality of turbine operating condition signals; the controller includes at least one neural network estimator that is trained to generate an estimated turbine operating condition signal. The neural network estimator typically has one or more hidden neuron layers that are coupled together in a feedforward structure, a recurrent neural network architecture. The estimated turbine operating condition signal generated by the neural network estimator typically, but not necessarily, represents a turbine internal cycle operating parameter for which the turbine has no corresponding operating parameter sensor.