Abstract: A recurrent, neural network-based fuzzy logic system includes in a rule base layer and a membership function layer neurons which each have a recurrent architecture with an output-to-input feedback path including a time delay element and a neural weight. Further included is a recurrent, neural network-based fuzzy logic rule generator wherein a neural network receives and fuzzifies input data and provides data corresponding to fuzzy logic membership functions and recurrent fuzzy logic rules.

Abstract: A servomechanism controller for controlling the movement and positioning of a servomechanism (such as that of an actuator assembly for the read/write heads of a hard disk drive assembly) in accordance with an improved "bang-bang" seek technique includes at least one neural network. In one embodiment, a single neural network, connected to a plant with servomechanism, receives two input positioning signals and provides an output positioning signal. One input positioning signal represents a desired servomechanism position, while the other represents its present position. The output positioning signal represents a positioning time period within which the servomechanism will reach the desired position plus a deceleration time period within the positioning time period upon the termination of which the servomechanism will reach its desired position. In another embodiment, one neural network, connected to the plant, receives an input positioning signal and provides an output positioning signal and a status signal.