Abstract: A matrix-based pattern prediction system of the present disclosure is that of a multi-block matrix-based system that operates on an input signal having a time-based pattern that is semi-chaotic. The system includes a wavelet/time conversion block M?, which is representative of a wavelet transformation of a semi-chaotic input signal, and a memory block MP which stores the wavelet coefficients generated by M? in an asymmetric matrix. The system further includes a predictor block MPP, which performs a time shifted symbolic operation on the memory block MP and is, therefore representative of the time shifted wavelet (M?). A Pattern Output block transforms the output predictor block MPP into a time-based output that is predictive of the semi-chaotic time-based input at a future point in time. First and second variables (Cp and Cpp) are automatically generated and used to compensate low (Cp) and high (Cpp) frequency components of an input.

Abstract: A generic, self-tuning control system deployable in a wide variety of systems is achieved from a digital system effectuating a matrix chain. Each matrix in the chain yields an output based upon two or more inputs. The outputs of the various matrices making up the chain are used as inputs to other matrices in the chain. Measured inputs are state variables of the system under control. Some inputs that are generated by other matrices are state variables. Consequently, the set of values generated by the matrices results from the state variables describing the system at a given point in time. Initially, the matrices exhibit a quality referred to as symmetry. This quality serves to cause the system under control to transition to desired state. In addition to the controlling force exerted by virtue of the symmetry of the matrices, a learning algorithm may be introduced to the matrix chain.

Abstract: A generic, self-tuning control system deployable in a wide variety of systems is achieved from a digital system effectuating a matrix chain. Each matrix in the chain yields an output based upon two or more inputs. The outputs of the various matrices making up the chain are used as inputs to other matrices in the chain. Measured inputs are state variables of the system under control. Some inputs that are generated by other matrices are state variables. Consequently, the set of values generated by the matrices results from the state variables describing the system at a given point in time. Initially, the matrices exhibit a quality referred to as symmetry. This quality serves to cause the system under control to transition to desired state. In addition to the controlling force exerted by virtue of the symmetry of the matrices, a learning algorithm may be introduced to the matrix chain.