Abstract: A method is implemented in hardware or software type neural network, the neural network is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. Each neuron applying a gating function to each of neural network inputs. The neural network utilizes a training algorithm which does not require repetitive training and which yields a global minimum to each given set of input vectors.

Abstract: A circuit and method for computing an exponential signal x.sup.g is provided. The circuit includes a logarithm converter which converts an input signal to binary word that represents the logarithm of an input signal x. A first shift register shifts the binary word in a bit-wise fashion to produce a first intermediate value; while a second shift register shifts the binary word in a bit-wise fashion to produce a second intermediate value. The shift registers may be implemented using multiplexers. The shifting operations are equivalent to multiplying the intermediate values by a factor which is a power of two. The first intermediate value is either added to or subtracted from the second intermediate value to produce a combined value. An inverse-logarithm converter converts the combined value to the exponential signal.

Abstract: A system polynomial is determined using a plurality of system I/O data, wherein the system polynomial expresses the system output in terms of the system input, and wherein the system polynomial has at least one linear term and at least one nonlinear term. A control polynomial is determined, the control polynomial having at least one cancellation term and at least one control term, the at least one cancellation term calculated to cancel the at least one nonlinear term of the system polynomial, and the at least one control term calculated to control the at least one linear term of the system polynomial. A control output signal is generated based on the control polynomial and the control input signal.

Abstract: A method for storing data in a memory device is provided. The method stores blocks of data elements in a two-dimensional address space such that the blocks overlap in the address space. The data elements may be digital words which represent exponents of a polynomial expansion.

Abstract: An artificial neuron, which may be implemented on an integrated circuit, has only one significant processing element in the form of an adder. Inputs are first fed through gating functions to produce gated inputs. These gated inputs are then added together to produce a sum which is added to a weight to produce the neuron output.

Abstract: A system polynomial is determined using a plurality of system I/O data, wherein the system polynomial expresses the system output in terms of the system input, and wherein the system polynomial has r' terms including at least one linear term and at least one nonlinear term, the r' terms found using a regression subsets technique. A control polynomial is determined, the control polynomial having at least one cancellation term and at least one control term, the at least one cancellation term calculated to cancel the at least one nonlinear term of the system polynomial, and the at least one control term calculated to control the at least one linear term of the system polynomial. A control output signal is generated based on the control polynomial and the control input signal.

Abstract: A neural network, which may be implemented either in hardware or software, is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. A hidden neuron in the neural network generates an output based on the product of a plurality of functions. The neural network utilizes a training algorithm which does not require repetitive training and which yields a global minimum to each given set of input vectors.

Abstract: A speech-recognition system for recognizing isolated words includes pre-processing circuitry for performing analog-to-digital conversion and cepstral analysis, and a plurality of neural networks which compute discriminant functions based on polynomial expansions. The system may be implemented using either hardware or software or a combination thereof. The speech wave-form of a spoken word is analyzed and converted into a sequence of data frames. The sequence of frames is partitioned into data blocks, and the data blocks are then broadcast to a plurality of neural networks. Using the data blocks, the neural networks compute polynomial expansions. The output of the neural networks is used to determine the identity of the spoken word. The neural networks utilize a matrix-inversion or alternatively a least-squares estimation training algorithm which does not require repetitive training and which yields a global minimum to each given set of training examples.

Abstract: An artificial neuron, which may be implemented either in hardware or software, has only one significant processing element in the form of a multiplier. Inputs are first fed through gating functions to produce gated inputs. These gated inputs are then multiplied together to produce a product which is multiplied by a weight to produce the neuron output.

Abstract: A bond inspection technique which determines the integrity of a plurality of package leads (13) bonded to a plurality of contact areas (12) on a semiconductor chip (11). A bonding process heats each package lead (13) bonded to each contact area (12). A camera (16) forms an infra-red intensity image at a predetermined time of the semiconductor chip (11) and ports image data to a computer (18). Infra-red intensity radiated from each bond on the semiconductor chip (11) is compared by the computer (18) with infra-red intensity data of known good and bad bonds. The comparison of each bond determines bond integrity of the semiconductor chip (11).

Abstract: Light (19) is projected at an incidence angle (29) onto a plurality of leads (12, 13). The light (19) is simultaneously reflected from each of the plurality of leads (12, 13). The light that is simultaneously reflected (24, 26) from each lead (12, 13) is detected. A cotangent function of the incidence angle (29) is utilized to detect an amount of displacement (32) of at least one of the plurality of leads.

Abstract: A non-destructive method for testing quality of at least one bond which physically and electrically couples a package lead to an integrated circuit. A thermal gradient is created across the at least one bond which causes heat transfer thru the at least one bond. Heat transfer is measured which is proportional to area at a bond interface. The measured heat transfer thru the at least one bond is compared with heat transfer data taken under substantially equal conditions of known good bonds thereby determining quality of the at least one bond. The heat transfer is indirectly measured by creating a mechanical vibration at an input area and by measuring the time it takes the mechanical vibration to reach an output area after the mechanical vibration has traveled through the at least one bond.