Abstract: The algorithm used for training a state machine has been disclosed in another application. The objective of this application is to reduce this theoretical framework to a more concrete structure. The structure disclosed involves the use of nodes to perform the function of state equations and calculating the value of particular state variables. After the nodes where classified as either Lead-Type Nodes or Non-Lead-Type Nodes, this classification was used to define a structure of nodes to build a State Machine Block. This State Machine Block model also restricts the location of system inputs and system outputs. The internal structure of a node is discussed with the components being a Function Block and a Complex Impedance Network. The Function Block is typically a multivariable power series and the Complex Impedance Network is a linear circuit of resistors and capacitors. Such a Complex Impedance Network is referred to as an Electrical Component Model.

Abstract: In a computer system functioning as an autonomous agent with a robotic controller wherein the computer system need not be programmed to perform a specific task, values in an array of error variables are obtained from comparison between an array of resources variables used to indicate resource use and state of an Autonomous Agent and an array of corresponding desired levels for the resource variables. Each individual value in the array of error variables represents an error in the actual value of a particular resource variable as compared to its desired value. Derivative variables for the resource variables and for parameters controlling behavior of the Autonomous Agent are created. The values of the array of error variables and their associated derivative variables are used to adjust values of the behavior-controlling parameters thereby modifying behavior of the Autonomous Agent.

Abstract: A method is provided for calculating the change in level which is output from a multivariable power series as a separate variable, where this change out put signal is the change in level which is output when supply input is from two different data points. This method requires defining a structure of a change-variable and modifying the arithmetic operations so this structure can be processed. A similar procedure is followed to calculate the derivative of the parameters used in the construction of the multivariable power series with this change output signal. Given an error in the change output signal and the existents of appropriate derivatives, it is possible to train the power series using the change-variable. As with any training algorithm, a matrix technique can be used to increase the training rate.

Abstract: In a computer system functioning as an autonomous agent with a robotic controller wherein the computer system need not be programmed to perform a specific task, values in an array of error variables are obtained from comparison between an array of resources variables used to indicate resource use and state of an Autonomous Agent and an array of corresponding desired levels for the resource variables. Each individual value in the array of error variables represents an error in the actual value of a particular resource variable as compared to its desired value. Derivative variables for the resource variables and for parameters controlling behavior of the Autonomous Agent are created. The values of the array of error variables and their associated derivative variables are used to adjust values of the behavior-controlling parameters thereby modifying behavior of the Autonomous Agent.

Abstract: A signal recognition system is provided which is initially capable of being programmed to learn how to recognize a signal. Using as signal inputs amplitude varying signals representative of different frequency components of a single audio channel, a frequency discriminator is used to provide data on which to train a State Machine Block in a feedback relationship with an Adjustment Block. As the trainable system is trained, the value of parameters in the State Machine Block is adjusted to modify system behavior. After the system has been fully trained, the resultant database of states created as a result of training is exported to a system with lesser capability, namely a system adapted for mere recognition of the input signals.

Abstract: A neural network is trained using a training neural network having the same topology as the original network but having a differential network output and accepting also differential network inputs. This new training method enables deeper neural networks to be successfully trained by avoiding a problem occuring in conventional training methods in which errors vanish as they are propagated in the reverse direction through deep networks. An acceleration in convergence rate is achieved by adjusting the error used in training to compensate for the linkage between multiple training data points.

Abstract: A new technique for adjusting the parameters of a feedback controller. A linear feedback controller includes proportional, integral and derivative (PID) controllers but is not limited to them. The feedback controller may utilize non-linearity in its control and still have its parameters adjusted by this algorithm. The technique involves calculating an error derivative signal, which is the derivative of the processes loops' error with a parameter, for each parameter being adjusted and utilizing these error derivative signals along with error using a modified LMS algorithm. A key aspect of the modified LMS algorithm is the development of the error derivative signals.

Abstract: A method of training a recursive filter comprises updating recursive parameters of the filter with delta values computed from sampling of an error signal and obtaining derivative terms of the output signal with respect to the parameters. Adaptation using derivatives for FIR filters is known, but this technique was not available for IIR filters because of the unavailability of the derivative values. However, when it is realized that the derivative itself is recursive, a parameter derivative function is obtained which will produce derivative terms for updating the filter parameters with acceptable filter performance.

Abstract: A bipolar VLSI process includes masking and patterning, implanting a P+ channel stop and locally oxidizing a lightly P-doped, monolithic silicon substrate to define a long, narrow collector region. An N-type collector is implanted in the collector region. The implants are diffused to form a shallow gradient P-N junction. Then, device features requiring a predetermined spacing and size are photolithographically defined along the length of the collector region. The device features and the collector region are made long enough for the features to readily transect the collector region even if the mask is misaligned. The active transistor and the collector, base and emitter contacts are self-aligned with the collector region so as to take advantage of the noncritical spacing of the preceding steps. A single polysilicon layer used to form base, collector and emitter contacts and a triple diffusion transistor.

Abstract: A method for fabricating a semiconductor diode array, utilizing an alignment tool to precisely position a plurality of diodes so that they can be bonded into a precision array. The alignment tool and a method for fabricating the tool are also disclosed.

Abstract: A method for fabricating a semiconductor diode array, utilizing an alignment tool to precisely position a plurality of diodes so that they can be bonded into a precision array. The alignment tool and a method for fabricating the tool are also disclosed.

Abstract: A method of training a recursive filter comprises updating recursive parameters of the filter with delta values computed from sampling of an error signal and obtaining derivative terms of the output signal with respect to the parameters. Adaptation using derivatives for FIR filters is known, but this technique was not available for IIR filters because of the unavailability of the derivative values. However, when it is realized that the derivative itself is recursive, a parameter derivative function is obtained which will produce derivative terms for updating the filter parameters with acceptable filter performance.