M & M Technologies is a developer of innovative and powerful techniques for automatic control.  Two areas of our interest are neural networks and tracking controllers.  We license tracking controller patents and can refer you to our neural network license holder.  You may also have us design your own controller.

Natural Tracking Controller

    U.S. Patent No. 5,379,210
    Abstract

Neural Network Analog-to-Digital Converter

    U.S. Patent No. 5,068,662
    Abstract

Real-Time Expert System Neural Network

    U.S. Patent No. 5,179,631
    Abstract

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Natural Tracking Controller Abstract

Novel controllers have been discovered which permit control of the outputs of a system with minimal knowledge of the system's internal dynamics, and with no knowledge of the values of disturbances on the system, or of the effects of the disturbances on the system. Very rapid convergence of the actual outputs to selected desired outputs is possible. The systems controlled may have multiple inputs and multiple outputs; may be linear or nonlinear; may be expressed in input-output form, state space form, or mixed form; and may be implemented with analog circuit elements, digital circuit elements; or a hybrid of both analog and digital circuit elements. Both general and special cases of such controllers are disclosed.

Neural Network Analog-to-Digital Converter Abstract

An asynchronous, rapid, neural network analog-to-digital converter. This converter requires only two different resistance values in R2R resistor ladders, and does not require both positive and negative biases. An average of n/2 steps is required for an n-bit conversion.

Real-Time Expert System Neural Network Abstract

A novel neural network implementation for logic systems has been developed. The neural network can determine whether a particular logic system and knowledge base are self-consistent, which can be a difficult problem for more complex systems.  Through neural network hardware using parallel computation, valid solutions may be found more rapidly than could be done with previous, software-based implementations. This neural network is particularly suited for use in large, real-time problems, such as in a real-time expert system for testing the consistency of a programmable process controller, for testing the consistency of an integrated circuit design, or for testing the consistency of an "expert system." This neural network may also be used as an "inference engine," i.e., to test the validity of a particular logical expression in the context of a given logic system and knowledge base, or to search for all valid solutions, or to search for valid solutions consistent with given truth values which have been "clamped" as true or false. The neural network may be used with many different types of logic systems: those based on conventional "truth table" logic, those based on a truth maintenance system, or many other types of logic systems. The "justifications" corresponding to a particular logic system and knowledge base may be permanently hard-wired by the manufacturer, or may be supplied by the user, either reversibly or irreversibly.