Abstract: A neural network based signal processor for a magnetic storage channel, such as a magnetic disk drive for a computer, uses a multiple layer perceptron neural network to perform the symbol sequencing detection, equalization and decoding of information signals retrieved from the magnetic storage medium.

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 preprocessor for automatic speech recognition based upon auditory modeling includes a tapped delay line and a neural network in the form of a multilayer perceptron. The tapped delay line receives an analog speech signal and provides multiple time delayed samples thereof in parallel as inputs for the neural network. The single analog output of the neural network is suitable for interfacing with a signal processor for further processing of the speech information using spectral signal analysis so as to provide a speech representation with desirable characteristics of an auditory based spectral analysis model while simultaneously maintaining a standard analog signal interface.

Abstract: A system and method for designing a fixed weight analog neural network to perform analog signal processing allows the neural network to be designed with off-line training and implemented with low precision components. A global system error is iteratively computed in accordance with initialized neural functions and weights corresponding to a desired analog neural network configuration for analog signal processing. The neural weights are selectively modified during training and then expected values of weight implementation errors are added thereto. The error adjusted neural weights are used to recompute the global system error and the result thereof is compared to a desired global system error. These steps are repeated as long as the recomputed global system error is greater than the desired global system error. Following that, MOSFET parameters representing MOSFET channel widths and lengths are computed which correspond to the neural functions and weights.

Abstract: A neural network controlled phase lock loop (PLL) with an artificial neural network for comparing the phase of the feedback signal from the voltage controlled oscillator (VCO) with the phase of the PLL reference signal. The VCO feedback signal and PLL reference signal are inputted to and processed by a multiple layer perception which has been trained with a model of the VCO to generate the appropriate control voltage necessary to establish and maintain the desired phase and/or frequency of the VCO output signal.