Abstract: A neural network architecture for classifying input data is provided. The neural network architecture includes an input block, an output block, and at least one hidden block interposed between the input block and the output block. Characteristically, each neuron of an input block output neuron layer, an output block input neuron layer, an output block output neuron layer, a hidden block input neuron layer and a hidden block output neuron layer, independently applies a logistic activation function or an activation function that is the sum of a logistic activation function and a linear term or an activation function that is the sum of a logistic activation function and a quasi-linear term.

Abstract: A learning computer system may estimate unknown parameters and states of a stochastic or uncertain system having a probability structure. The system may include a data processing system that may include a hardware processor that has a configuration that: receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states.

Abstract: The invention shows how to use noise-like perturbations to improve the speed and accuracy of Markov Chain Monte Carlo (MCMC) estimates and large-scale optimization, simulated annealing optimization, and quantum annealing for large-scale optimization.

Abstract: A learning computer system may include a data processing system and a hardware processor and may estimate parameters and states of a stochastic or uncertain system. The system may receive data from a user or other source. Parameters and states of the stochastic or uncertain system are estimated using the received data, numerical perturbations, and previous parameters and states of the stochastic or uncertain system. It is determined whether the generated numerical perturbations satisfy a condition. If the numerical perturbations satisfy the condition, the numerical perturbations are injected into the estimated parameters or states, the received data, the processed data, the masked or filtered data, or the processing units.

Abstract: The invention shows how to use noise-like perturbations to improve the speed and accuracy of Markov Chain Monte Carlo (MCMC) estimates and large-scale optimization, simulated annealing optimization, and quantum annealing for large-scale optimization.

Abstract: An estimating computer system may iteratively estimate an unknown parameter of a model or state of a system. An input module may receive numerical data about the system. A noise module may generate random, chaotic, or other type of numerical perturbations of the received numerical data and/or may generate pseudo-random noise. An estimation module may iteratively estimate the unknown parameter of the model or state of the system based on the received numerical data. The estimation module may use the numerical perturbations and/or the pseudo-random noise and the input numerical data during at least one of the iterative estimates of the unknown parameter. A signaling module may signal when successive parameter estimates or information derived from successive parameter estimates differ by less than a predetermined signaling threshold or when the number of estimation iterations reaches a predetermined number.

Abstract: A learning computer system may update parameters and states of an uncertain system.

Abstract: A learning computer system may include a data processing system and a hardware processor and may estimate parameters and states of a stochastic or uncertain system.

Abstract: A learning computer system may estimate unknown parameters and states of a stochastic or uncertain system having a probability structure. The system may include a data processing system that may include a hardware processor that has a configuration that: receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states.

Abstract: Non-transitory, tangible, computer-readable storage media may contain a program of instructions that enhances the performance of a computing system running the program of instructions when segregating a set of data into subsets that each have at least one similar characteristic. The instructions may cause the computer system to perform operations comprising: receiving the set of data; applying an iterative clustering algorithm to the set of data that segregates the data into the subsets in iterative steps; during the iterative steps, injecting perturbations into the data that have an average magnitude that decreases during the iterative steps; and outputting information identifying the subsets.

Abstract: An estimating computer system may iteratively estimate an unknown parameter of a model or state of a system. An input module may receive numerical data about the system. A noise module may generate random, chaotic, or other type of numerical perturbations of the received numerical data and/or may generate pseudo-random noise. An estimation module may iteratively estimate the unknown parameter of the model or state of the system based on the received numerical data. The estimation module may use the numerical perturbations and/or the pseudo-random noise and the input numerical data during at least one of the iterative estimates of the unknown parameter. A signaling module may signal when successive parameter estimates or information derived from successive parameter estimates differ by less than a predetermined signaling threshold or when the number of estimation iterations reaches a predetermined number.

Abstract: A system and method for fuzzy spread spectrum communication. The system includes a fuzzy spreader for spreading an input signal over a range of frequencies and fuzzy despreader for extracting the spread input signal from the range of frequencies. In the illustrative implementation, the inventive system a fuzzy pseudo-random generator for use in the fuzzy spreader and the fuzzy despreader. The fuzzy pseudo-random generator uses a novel method for generating pseudo-random numbers. It does not use encryption or decryption techniques. The invention further provides a method for adaptive rule generation and a novel method for identifying the centroid of the set of output numbers. This allows the fuzzy system to learn spreading rules that favor data compression, compact multiplexing, bandwidth conservation, and other communication tasks as well as rules that favor security.